an intelligent recommendation framework for student ... › id › eprint › 25781 › 1 ›...

An Intelligent Recommendation Framework

for Student Counselling Management

in Thai Private Universities

Kanokwan Kongsakun

B.B.A. (Business Computer) Prince of Songkla University, Thailand

M.S. Ind. Ed. (Computer and Information Technology),

King Mongkut’s University of Technology Thonburi, Thailand

This thesis is presented for the Degree of

Doctor of Information Technology of

Murdoch University

September 2013

ii

Declaration

I declare that this thesis is my own account of my research and contains as its main

content work which has not previously been submitted for a degree at any tertiary

education institution.

Kanokwan Kongsakun

September 27, 2013

iii

Acknowledgements

The doctoral thesis could have not been completed, if I have not received the support

from many important people and supportive organisations in my life. This is a good

opportunity to thank all of them.

In countless ways, I have received love and support from my wonderful family. I would

like to thank my parents for the endless love and caring, two older sisters for love and

assistance, and all my cousins for their encouragement to further my study in Australia.

I am grateful to the Scholarship Committee of Hatyai University, Thailand for the

provision of a Postgraduate Study Scholarship and supports for my doctoral degree at

Murdoch University, Western Australia. Especially, I am grateful to Ajarn Tharnpas

Sattayarak, Vice President for Administration, Hatyai University, who assists me with

many supports.

I would also like to express my utmost gratitude to my principal supervisor, Associate

Professor Dr. Lance Chun Che Fung who has worked very hard to guide, support,

encourage and critique my work throughout the period of my study. I am also deeply

grateful for his supervision and efforts for the invaluable insight and experiences in my

academic career. I would like to express my gratefulness to my co-supervisor, Associate

Professor Dr. Kevin Kok Wai Wong for his advice, helpful comments and excellent

guidance in my research.

iv

I would like to thank Associate Professor Dr. Tanya McGill for the advice and teaching

on the subject of research methodology. I appreciate my fellow student, Mr Jesada

Kajornrit and his wife, Usarom Pongsarak, for their help and advice. My thanks are due

to Mr John Covate who assisted me at the beginning of this study, to my colleagues in

Thailand, and thanks to all my housemates, office mates and friends for their friendship

during my time in Perth.

Finally, I am deeply grateful to my wonderful husband, Michael Steven Watkins, for

love, understanding, inspiration, encouragement and support to empower me to finish

my research for the doctoral study. Thank you very much for standing by my side

during the study.

v

Abstract

This study proposed a framework for an Intelligent Recommendation System for

private universities in Thailand. Choosing a program of study for students is

significant due to the commitment involved and the potential career opportunities.

However, many students have enrolled in course majors without receiving any

advice from appropriate authorities or university services. This could have

potential mismatch between a student’s background, personal interests and

capability, with the particular course being taken up. This may lead to low

retention and dropouts. In order to improve the academic management processes,

many universities are developing innovative information systems and services with

an aim to enhance efficiency and student relationship. One of the key initiatives is

the development of Student Relationship Management Systems (SRM) and among

their functions, is the provision of recommendation and advice for students. The

proposed system in this study examined the correlation between up to 11,000

student records and their academic performance. The system focuses on the

following outcomes: programme and activity recommendation, likely overall GPA

and results in each year, Identification of postgraduate students and potential

dropouts. Association Rules and K-Means Clustering have been used together with

three classification techniques: Artificial Neural Networks (ANN), Decision Tree

(DT) and Support Vector Machines (SVM). Ensemble and the Modular Artificial

Neural Networks based on Optimised Weight of Subspace Reconstruction

(MANN-OWSR) have also been used to combine the learning models for

improved performance. Results from the experiments will be useful for counsellors

and academic staff in suggesting appropriate recommendations for the students.

vi

List of publications related to this thesis

JOURNAL

[P1] K. Kongsakun and C. C. Fung, "Neural Network Modelling for an Intelligent Recommendation System Supporting SRM for Universities in Thailand” In WSEAS TRANSACTIONS on COMPUTERS. Issue 2, Vol. 11, February 2012, pp.34-44.

[P2] K. Kongsakun, J. Kajornrit and C. C. Fung, Neural Network Modelling for an Intelligent Recommendation System Supporting SRM for Universities in Thailand. In The Asian International Journal of Science and Technology in Production and Manufacturing Engineering (AIJSTPME), July – September, 2012, Vol. 5, No. 3

CONFERENCE PROCEEDINGS

[P3] K. Kongsakun, J. Kajornrit and C.C. Fung, Understanding Student Relationship Management and Its effects on University students. In the Postgraduate Electrical Engineering and Computing Symposium (PEECS 2009), WA, Australia. October 2009.

[P4] K. Kongsakun, and C.C. Fung, a Recommendation System for Student Relationship Management. In Proceeding of the 8th International Conference on E-Business (INCEB 2009), Bangkok, Thailand, 28th-29th October 2009.

[P5] K. Kongsakun, C. C. Fung, S. Borirug and W. Philuek, An Intelligent Recommendation System Framework for Student Relationship Management. In Proceedings of the “World Academy of Science, Engineering and Technology”, Penang, Malaysia, volume 62, February 2010.

[P6] K. Kongsakun and C. C. Fung, Developing an Intelligent Recommendation System for a Private University in Thailand. In Proceedings of the International Association for Computer Information Systems (IACIS), Las Vegas, USA, 6-9 October, 2010.

[P7] K. Kongsakun, J. Kajornrit and C. C. Fung, Neural Network Modelling for an Intelligent Recommendation System Supporting SRM for Universities in Thailand. In Proceedings of the 8th International Conference on Computing and Information Technology (IC2IT), Pattaya city, Thailand, 9-10 May, 2012.

[P8] K. Kongsakun, Tuchtawan Chanakul and C. C. Fung, Decision Tree Modelling for an Intelligent Recommendation System Supporting SRM for Universities in Thailand. In Proceeding of the International Conference on Computer and Information Technology (ICCIT’2012), Bangkok, Thailand, 16-17 June, 2012.

[P9] K. Kongsakun, Prediction of Likelihood of Overall Results from Freshmen Using a Combined Classifier in a Recommendation System. In Proceeding of the

vii

Postgraduate Electrical Engineering and Computing Symposium (PEECS 2012), Curtin University, WA, Australia, 9 November 2012.

[P10] K. Kongsakun, C.C. Fung and K.W. Wong, Drop-out Identification model using Data Mining for an Intelligent Recommendation System for Universities in Thailand. In Proceeding of the Hatyai Symposium 2013, Songkla, Thailand, 10 May 2013.

[P11] K. Kongsakun, An improved recommendation model using linear regression and clustering for a private university in Thailand. In Proceeding of the International Conference on Machine Learning and Cybernetics (ICMLC 2013),Tianjin, China, 14-17 July 2013.

viii

Contributions of this thesis

In general, recommendations in university are provided by counsellors or advisers

without any analysis of the information from past students. In this thesis, an intelligent

recommendation system for university students is proposed. The contributions in this

thesis which have been published previously in the list of publications related to this

thesis are summarised below.

Key Contributions Supportive papers

A review of various techniques in recommendation systems, data mining and intelligent techniques, and report on the proposed framework.

Conference Papers

[P3], [P4] and [P5]

Reported on the use of combined classifiers for GPA prediction in a recommendation system to improve the performance accuracy.

Conference Paper

[P9]

Reported on the use of K-Mean clustering and comparison of results obtained from other approaches. Also, reported on the proposed techniques and results based on artificial neural networks and decision tree used in this study together with comparison with other approaches.

Conference Papers

[P6], [P7], [P8]

Journal Papers

[P1] and [P2]

Reported on the proposed framework and results based on clustering, together with ANN, Decision Tree and SVM including Ensemble and MANN-OWSR for dropout identifications and comparison with other approaches.

Conference Paper

[P10] [P11]

ix

Contents

Acknowledgements ........................................................................................................ iii

Abstract ............................................................................................................................ v

List of publication related to this thesis ....................................................................... vi

Contributions of this thesis ......................................................................................... viii

List of Figures ................................................................................................................ xii

List of Tables ................................................................................................................ xiv

List of Abbreviations .................................................................................................... xvi

Chapter 1: Introduction ................................................................................................. 1

1.1 Background ............................................................................................................. 1 1.2 Objective ................................................................................................................. 4 1.3 Methodology ........................................................................................................... 4 1.4 Thesis Outline ......................................................................................................... 6

Chapter 2: Background .................................................................................................. 9

2.1 Introduction ............................................................................................................. 9 2.2 University System in Thailand ................................................................................ 9

2.2.1 University types ............................................................................................... 9 2.2.2 University admission process ......................................................................... 11 2.2.3 Student relationship management in Thai universities .................................. 13 2.2.4 Student counselling in universities ................................................................. 15 2.2.5 Background information on students ............................................................. 16 2.2.6 Justification for the proposed recommendation system ................................. 17

2.3 Intelligent Techniques for the Proposed Recommendation System ...................... 18 2.3.1 Artificial Neural Networks ............................................................................. 19 2.3.2 Decision tree ................................................................................................... 20 2.3.3 Support vector machine .................................................................................. 21 2.3.4 Association rules ............................................................................................ 22 2.3.5 K-means clustering ......................................................................................... 23 2.3.6 Confidence-weighted voting ensemble .......................................................... 23 2.3.7 Modular Artificial Neural Networks-Optimised Weight of Subspace

Reconstruction ............................................................................................... 24 2.3.8 Evaluation metrics of the intelligent recommendation system ...................... 26

2.4 Summary ............................................................................................................... 27

Chapter 3: Framework of the Proposed Recommendation System ......................... 28

3.1 Introduction ........................................................................................................... 28

x

3.2 An Overview of the Proposed Recommendation System ..................................... 28 3.3 Description of the Modules and Their Purposes ................................................... 29

3.3.1 Module 1: likely overall GPA ........................................................................ 29 3.3.2 Module 2: ranked programme recommendation ............................................ 29 3.3.3 Module 3: likely GPA for each semester ....................................................... 30 3.3.4 Module 4: ranked activities recommendation ................................................ 31 3.3.5 Module 5: programme completion identification .......................................... 31 3.3.6 Module 6: postgraduate study identification .................................................. 31

3.4 Description of Parameters Used in this Study ....................................................... 32 3.4.1 UniID .............................................................................................................. 34 3.4.2 GPAs .............................................................................................................. 34

3.4.2.1 Overall GPA ............................................................................................ 35 3.4.2.2 GPA each semester ................................................................................. 35 3.4.2.3 Previous school GPA .............................................................................. 36 3.4.2.4 Postgraduate GPA ................................................................................... 36

3.4.3 Previous major ............................................................................................... 37 3.4.4 Type of school ................................................................................................ 37 3.4.5 Number of awards .......................................................................................... 38 3.4.6 Talents and interests ....................................................................................... 39 3.4.7 Motivation channels ....................................................................................... 39 3.4.8 Admission round ............................................................................................ 40 3.4.9 Guardian occupation ...................................................................................... 41 3.4.10 Gender .......................................................................................................... 41 3.4.11 Activity type ................................................................................................. 41 3.4.12 University major ........................................................................................... 42

3.5 Methodology ......................................................................................................... 44 3.5.1 Data pre-processing ........................................................................................ 44 3.5.2 Data analysis (hybrid classification association recommendation models) ... 45 3.5.3 Validation of model based on intelligent recommendation system ............... 47

Chapter 4: Programme and Activity Recommendation ............................................ 48

4.1 Introduction ........................................................................................................... 48 4.2 Objectives .............................................................................................................. 49 4.3 Input and Output Variables Selection ................................................................... 49 4.4 Experiment Methodology and Design .................................................................. 52 4.5 Intelligent Technique Used ................................................................................... 54 4.6 Experiment Results ............................................................................................... 55

4.6.1 Example results of ranked programme and activity recommendations based on GRI algorithm ................................................................................. 56

4.6.2 Example results of ranked programme and activity recommendations based on GRI and K-means clustering .......................................................... 61

4.7 Conclusion and Discussion ................................................................................... 63 Chapter 5: Grade Point Average Prediction and Postgraduate Identification ....... 65

5.1 Introduction ........................................................................................................... 65 5.2 Objectives .............................................................................................................. 65 5.3 Input and Output Variables Selection ................................................................... 66 5.4 Intelligent Techniques ........................................................................................... 68 5.5 Experimental Methodology and Design ................................................................ 70

xi

5.6 Experiment Results ............................................................................................... 72 5.6.1 First comparison between SVM, ANN and CHAID ...................................... 72 5.6.2 Second comparison of the ANN, CHAID and ensemble models .................. 74 5.6.3 Third comparison using MANN-OWSR, SVM and ensemble in overall

GPA and GPA of each semester .................................................................... 76 5.6.4 Third comparison of MANN-OWSR, SVM and CHAID in the

postgraduate identification module ................................................................ 78 5.7 Conclusion and Discussion ................................................................................... 79

Chapter 6: Dropout Identification ............................................................................... 81

6.1 Introduction ........................................................................................................... 81 6.2 Objectives .............................................................................................................. 81 6.3 Input and Output Variables Selection ................................................................... 82 6.4 Experimental Methodology and Design ................................................................ 83 6.5 Experimental Results ............................................................................................ 85

6.5.1 First comparison of classification techniques ANN, CHAID and SVM ....... 85 6.5.2 Results from K-means clustering ................................................................... 86 6.5.3 Comparing results from three models using data from Cluster 1: second

comparison ..................................................................................................... 87 6.5.4 Comparison of results based on data from Cluster 2 ..................................... 88 6.5.5 Fourth comparison between Ensemble 1 and Ensemble 2 ............................. 90 6.5.6 Fifth comparison between MANN-OWSR and the best ensemble ................ 91

6.6 Conclusion and Discussion ................................................................................... 92

Chapter 7: Conclusion and Future Work ................................................................... 94

7.1 Introduction ........................................................................................................... 94 7.2 Summary of Findings ............................................................................................ 94

7.2.1 Programme and activity recommendations .................................................... 94 7.2.2 Grade point average prediction and postgraduate identification .................... 95 7.2.3 Dropout identification: programme completion identification and dropout

identification modules ................................................................................... 96 7.3 Discussion on Future Work ................................................................................... 97 7.4 Conclusion ............................................................................................................. 98

References ...................................................................................................................... 99

Appendix ...................................................................................................................... 111

xii

List of Figures

Figure 1.1: Process of developing the proposed recommendation system ....................... 5

Figure 1.2: Thesis outline .................................................................................................. 8

Figure 2.1: Relationship marketing model for student retention [31] ............................. 14

Figure 2.2: A multilayer feed-forwards network ............................................................ 19

Figure 2.3: Development of the MANN-OWSR model [2] ............................................ 24

Figure 2.4: The vector of a new student as X1Y1 within a boundary of β. Only the

training data within β are used for the MANN-OWSR model [2] ................. 25

Figure 2.5: Training data used in the MANN-OWSR model [2] .................................... 25

Figure 3.1: Percentage of participants’ opinion in relation to independent variables,

the likely study level and programme of study .............................................. 33

Figure 3.2: Proposed intelligent recommendation system based on the Hybrid

Classification Association framework ........................................................... 44

Figure 4.1: Number of undergraduate students by programme of study (2001–2007) ... 50

Figure 4.2: Process to compare performance of GRI for ranked programme and

activity recommendations .............................................................................. 52

Figure 4.3: Flowchart to derive recommendation for three ranked programme

majors and activities ....................................................................................... 53

Figure 4.4: Distribution of the rules in each ranking ...................................................... 59

Figure 4.5: Comparison of the accuracy between ranked programme and activity

recommendations ........................................................................................... 60

Figure 4.6: Comparison of mean absolute error between ranked programme and


Figure 4.7: Comparison of accuracies between ranked programme and activity


Figure 4.8: Comparison of mean absolute errors between ranked programme and


Figure 5.1: Number of postgraduate students in each postgraduate programme

(2001–2009) ................................................................................................... 66

Figure 5.2: Process for determining the best GPA recommendation model ................... 70

Figure 5.3: Accuracy rate of the classification techniques ............................................. 73

xiii

Figure 5.4: Comparison of MAE from the first process ................................................. 73

Figure 5.5: Comparison of the accuracy rate between ANN, CHAID and ensemble ..... 75

Figure 5.6: Comparison of MAE between ANN, CHAID and ensemble ....................... 75

Figure 5.7: Comparison of the accuracy between MANN-OWSR, SVM and

ensemble ......................................................................................................... 77

Figure 5.8: Comparison of MAE between MANN-OWSR, SVM and ensemble .......... 77

Figure 5.9: Comparison of the accuracy between MANN-OWSR, SVM and CHAID .. 78

Figure 5.10: Comparison of MAE between MANN-OWSR, SVM and CHAID ........... 79

Figure 6.1: Number of undergraduate students, including dropouts, by programme

of study (2001–2007) ..................................................................................... 82

Figure 6.2: Process for determining the student dropout identification model ............... 84

Figure 6.3: Comparison of the accuracy between classification techniques ................... 86

Figure 6.4: Number of data in each cluster from K-means clustering ............................ 87

Figure 6.5: Comparison of accuracy based on dataset from Cluster 1 ............................ 88

Figure 6.6: Comparison of accuracy based on data from the second cluster .................. 89

Figure 6.7: Comparison of accuracy of SVM and ANN ensembles ............................... 90

Figure 6.8: Comparison of accuracy of ensemble and MANN-OWSR .......................... 91

Figure 6.9: Accuracy of ensemble in comparison to the single SVM model ................. 92

xiv

List of Tables

Table 2.1: Enrolment numbers in higher education institutions in Thailand [26] .......... 11

Table 3.1: Statistical parameters for the categorised GPAs ............................................ 34

Table 3.2: Six classes of overall GPA based on statistics ............................................... 35

Table 3.3: Five classes of previous school GPA ............................................................. 36

Table 3.4: Five classes of postgraduate GPA results ...................................................... 37

Table 3.5: Classes of previous major .............................................................................. 37

Table 3.6: Classes of type of school ................................................................................ 38

Table 3.7: Class of number of awards ............................................................................. 38

Table 3.8: Class of talents and interests .......................................................................... 39

Table 3.9: Class of motivation channels ......................................................................... 40

Table 3.10: Classes of admission round .......................................................................... 40

Table 3.11: Classes of guardian occupation .................................................................... 41

Table 3.12: Classes of gender ......................................................................................... 41

Table 3.13: Classes of activity type ................................................................................ 42

Table 3.14: Classes of university major .......................................................................... 42

Table 3.15: Samples of variables in the training sample dataset for likely overall

GPA ................................................................................................................ 43

Table 4.1: Variables used in the ranked programme and activity modules .................... 51

Table 4.2: Example results of ranked programme recommendation .............................. 54

Table 4.3: Example results of ranked activity recommendation ..................................... 54

Table 4.4: Example results of rules extraction by GRI for ranked programme


Table 4.5: Example results of rules extraction by GRI for ranked activity

recommendation ............................................................................................. 57

Table 4.6: A comparison of the accuracy between the ranked programme and


Table 4.7: Comparison of mean absolute error between ranked programme and


Table 4.8: Comparison of accuracies between ranked programme and activity


xv

Table 4.9: Comparison of mean absolute errors between ranked programme and


Table 5.1: Variable names and data types in each module ............................................. 67

Table 5.2: Example results for likely overall GPA and likely GPA in each semester .... 72

Table 5.3: Example results for postgraduate identification ............................................ 72

Table 5.4: Accuracy rate from the first process .............................................................. 72

Table 5.5: Comparison of MAE from the first process ................................................... 73

Table 5.6: Comparison of the accuracy rate between ANN, CHAID and ensemble ...... 74

Table 5.7: Comparison of MAE between ANN, CHAID and ensemble ........................ 75

Table 5.8: Comparison of the accuracy between MANN-OWSR, SVM and

ensemble ......................................................................................................... 76

Table 5.9: Comparison of MAE between MANN-OWSR, SVM and ensemble ............ 77


CHAID in the postgraduate identification module ........................................ 78

Table 5.11: Comparison of MAE between MANN-OWSR, SVM and CHAID ............ 79

Table 6.1: Name and type of input and output data ........................................................ 83

Table 6.2: First comparison of classification technique accuracy .................................. 86

Table 6.3: Number of clusters and iterations by K-means clustering ............................. 86

Table 6.4: Comparison of results based on data from Cluster 1 ..................................... 87

Table 6.5: Comparison of results from the second cluster .............................................. 88

Table 6.6: Results of comparison between Ensemble 1 and 2 ........................................ 90

Table 6.7: Accuracies from the best ensemble and MANN-OWSR ............................... 91

Table 6.8: Comparison of SVM cluster ensemble and single SVM model .................... 92

xvi

List of Abbreviations

Abbreviation Definition

ANN Artificial Neural Network

AR Association Rules

BP Back Propagation

CAAR Classification based on Atomic AR

CHAID Chi-squared Automatic Interaction Detector

CRM Customer Relationship Management

CUAS Central University Admissions System

DT Decision Tree

GPA Grade Point Average

GRI Generalised Rule Induction

HCAF Hybrid Classification Association Framework

HE Higher Education

HEI Higher Education Institutes

MAE Mean Absolute Error

MANN-OWSR Modular ANNs based on Optimised Weight of Subspace

Reconstruction

MLP Multilayer Perceptron

RMSE Root of the Mean Square Error

SD Standard Deviation

SRM Student Relationship Management

SVM Support Vector Machine

1

Chapter 1: Introduction

1.1 Background

Higher education (HE) is essential to the development of a country’s long-term

economic performance and productivity [1]. As it requires substantial investments and

resources, one of the key objectives of higher education institutes (HEIs) is to focus on

improving student completion rates in respective programmes. In Thailand, records

reveal that there is room for improvement in this area. Novoa, Curado and Machado [3]

found that one cause can be attributed to the high number of student dropouts. This has

led to wasted resources and a reduced number of graduates to meet the demands of

industry and the community. There are many reasons why a student may choose to drop

out, such as finding that the programme is unsuitable. This problem usually originates at

enrolment when the student selects or is recommended an unsuitable programme of

study.

Previous studies have investigated the issues that can lead to student dropouts at

university. One of these issues is depression. This can occur when the student is unable

to cope with study, which is a common problem among tertiary students. This affects

the student’s behaviour, motivation level, concentration, feeling of self-worth and mood

and can eventually lead to the student electing to drop out [4]. From a university

perspective, causes for dropouts are related to the allocation of resources and inability to

recruit students of appropriate calibre with a high probability of completion.

Inappropriate management decisions can lead to unoccupied student placements and

loss of potential tuition fees when students dropout. The problem of student retention in

HE can also be attributed to low student satisfaction and student transfer [5]. In addition

to these causes, previous studies have found that the quality and convenience of support

services influence Thai students to change educational institutes in HE [6]. Therefore, it

is necessary to meet student needs and to match their capabilities with suitable

programmes of study in HE recruitment and enrolment processes. Understanding

student needs will enhance their learning experience, increase their chances of success

and reduce resource wastage that is due to dropouts and change of programs.

2

With a limited supply of resources and increasing competition for students in Thailand’s

HE sector, universities and institutes are focusing their efforts on increasing the rate of

student retention and completion. In addition, reputation is being used increasingly to

measure the university’s quality and performance [7]. One aspect of such measurement

is based on factors that affect student satisfaction. Gatfield [8] stated that it is vital that

HEIs concentrate on quality through accreditation processes and various aspects of

quality services from a student perspective.

Archer and Cooper [9] confirmed that the provision of counselling services is an

important factor contributing to students’ academic success. Urata and Takano [10]

stated that the essence of student counselling should include advice on career guidance,

identification of learning strategies, handling of interpersonal relationships, along with

self-understanding of the mind and body. A key aspect of student services is to provide

counselling on programme guidance because this will assist the students in their

enrolment decisions and future university experience. Although many students choose

particular programmes of study because of job opportunities, issues may arise if a

student is not interested in the career or if the programme is not suitably matched with

the student’s capabilities [11]. Therefore, to assist with student retention, HEIs need to

determine how they can attract or recruit students and how they can match students to

appropriate programmes of study to achieve a high completion rate.

In the business world, organisations and corporations rely on successful relationships

with customers and they dedicate a large amount of effort to gaining and maintaining

customers and establishing successful relationships with those customers [12].

Customer Relationship Management (CRM) is a management concept aimed at

enhancing customer satisfaction and improving the relationship between the

organisation and its customers [13]. Student Relationship Management (SRM) is a

similar concept applied in the academic world. CRM has been defined as:

a fundamental strategic orientation which is pursued by all members of a

company in order to increase customer satisfaction, customer loyalty and the

benefit for the consumer as well as for the company during the entire supplier-

customer-relationship [14].

3

In educational institutes, students could be considered a form of customer and, as such,

the objective of SRM is to increase student satisfaction and loyalty for the benefit of the

institute. SRM can be considered similar to CRM because it aims to develop and

maintain a close relationship between the institute and the students by supporting the

administrative process and monitoring the students’ academic activities and

performance. Piedade and Santos [15] explained that SRM involves the identification of

performance indicators and behavioural patterns that characterise the students and the

situations under which they are supervised. In addition, SRM is:

understood as a process based on the student acquired knowledge, whose main

purpose is to keep a close and effective students institution relationship

through the closely monitoring of their academic activities along their

academic path.

Therefore, similarly to CRM, SRM is considered an important means of enhancing

student satisfaction [12].

The HE sector in Thailand consists of 79 public and 71 private HEIs and 19 community

colleges [16]. The Thai education system is based on government policies. Gamage [11]

found that:

another challenge faced by the higher education in Thailand that is pushing the

public universities to become ‘autonomous universities’, or public

corporations with more administrative and financial autonomy.

This causes intense competition between private and government universities in

Thailand. Both sectors are competing fiercely to attract students that may eventually

affect the university’s sustainability [17]. Therefore, HEIs in Thailand need to maintain

or obtain sufficient student numbers. This means that private universities need to

compete with other universities and enhance their reputation to gain student attention.

For example, Hatyai University, a private university in the south of Thailand, faces

various challenges, including competition with other universities, change of government

policies and political unrest in the southern part of Thailand. However, some factors are

within the control or influence of the university, such as student recruitment, student

enrolment and student retention. For example, as a typical private university, Hatyai

University has strategies that aim to increase student retention and completion rates,

4

improve the university’s reputation and enhance student satisfaction through the

provision of student services, such as programme advice and career guidance. To

achieve these aims, the university needs to establish some form of SRM approach to

ensure successful relationships with its students.

Within this context, this research study aimed to investigate and develop an intelligent

system to provide academic recommendations for new students based on historical

records of students who have successfully completed their programmes. Moreover, this

project focused on techniques that enabled the recommendation system to improve

student services, which, in turn, supported SRM by assisting students to choose the

most appropriate programme for their study at university. This focus ensured that the

objective was met, which was to improve completion rates in HEIs.

1.2 Objective

The objective of this research was to develop and apply intelligent techniques and

methodologies to a recommendation system for recommending appropriate programmes

and activities to students. It also aimed to assess the likely overall grade point average

(GPA), as well as the GPA for each semester, for prospective students, new students

and current students. Other objectives including identifying students who were likely to

succeed in postgraduate study and identifying students who were likely to drop out

before graduation. The proposed techniques were implemented and evaluated based on

classification models in each of the techniques. Finally, the proposed techniques were

applied to determine the best model for achieving good results from the intelligent

recommendation system.

1.3 Methodology

The literature review found that few Thai universities used recommendation systems to

support SRM. The workflow of the development of the proposed system is illustrated in

Figure 1.1.

5

Figure 1.1: Process of developing the proposed recommendation system

This study used several processes to achieve its objectives. The first process involved

defining the research problems. This was followed by the data selection and pre-

processing data processes. The data analysis process was performed next and was

followed by the proposal of an intelligent recommendation system that makes

appropriate recommendations for students based on artificial intelligence and data-

mining techniques.

Figure 1.1 demonstrates that the data selection process was carried out after the research

problems were defined. This involved choosing the appropriate variables for the

training data and, as such, was an important step. The variables were based on survey

results provided by the university, which were based on the opinion and experience of

supervisors and counsellors who had been involved with the process. During the data

preparation process, pre-processing was used to organise the student records from the

university’s enterprise database. The data were then re-formatted in preparation for

processing by subsequent algorithms. Next, the data cleaning process was executed to

identify the parameters from the dataset, and missing data were either deleted or

completed with null values [18]. Preparation of the analytical variables was done in the

6

data transformation step or in a separate process. Validity of the data was then checked

against the legitimate range of values and data types.

The next step was data analysis, which included five techniques: Decision Tree (DT),

Artificial Neural Network (ANN), Support Vector Machine (SVM), Association Rules

(AR) and Clustering. The development process also involved a process for training,

validating and testing the model. The prediction models comprised of six modules

within the intelligent recommendation framework. The details of each module are

explained in the subsequent chapters. As there were multiple outputs from the various

modules, two aggregation models (ensemble and modular ANNs [MANNs] based on

Optimised Weight of Subspace Reconstruction [OWSR]) were employed to improve the

accuracy of the final results. In the final process, the results were compared and the

models that returned the best accuracy were chosen to determine the recommendations.

The proposed intelligent recommendation system was designed in such a way that it

forms an integral part of an online system for one or multiple private universities in

Thailand. The proposed system will be available for use by new students who may

access the online application during the enrolment process. Counsellors, staff and

university management will use the function for predicting subsequent years’ results to

provide support for students who are likely to be in need of help during their studies.

This information will enable the university to improve its resource management

processes. In particular, it could be used to improve the retention rate by providing

additional support to at risk students.

1.4 Thesis Outline

Chapter 1 provided an introduction to the research study. Chapter 2 will provide the

background of Thai university systems and will discuss the techniques to be used in the


Chapter 3 will provide the framework of the proposed recommendation system, along

with the main idea and research methodology. It will also provide an overview of the

system, modules and purposes, including the parameters to be used in the system.

7

Chapter 4 will provide the module and process for the programme and activity

recommendations. This is a multiclass classification problem that aims to recommend

an appropriate programme and activities to the students; choice will be provided. The

objective of this module is to determine the most appropriate recommendation based on

past successful cases. The chapter will also describe the selection of input and output

variables and illustrate the proposed model with the experimental methodology and

design of the model. The subsequent section will discuss the intelligent technique

justification, which will be followed by the experimental results, discussion and

contributions.

Chapter 5 describes three modules: the likely overall GPA for prospective students or

new students, the likely GPA for students in each year and postgraduate identification,

which are forecasting problems. The model is trained with past GPA results from

student records for both GPA predictions and with past postgraduate student records for

the postgraduate identification. This chapter will discuss objectives, input and output

variable selection, experimental methodology and design and the justification of the

intelligent technique used. The experimental results are compared between different

techniques and variables to obtain the best result. Finally, the chapter will provide a

discussion and contributions.

Chapter 6 will describe the module for dropout identification. This is used not only for

new students but also for existing students in each year. The objective of this chapter is

to identify a possible dropout during a student’s programme of study. This will be

followed by input and output variable selection to support the model, experimental

methodology and design for the dropout identification model, intelligent technique

justification, experimental results, discussion and contributions.

The final chapter will conclude with a summary of the findings, contributions and

suggestions for future development. The thesis outline is illustrated in Figure 1.2.

8

Figure 1.2: Thesis outline

9

Chapter 2: Background

2.1 Introduction

In Thailand, one of the performance assessments for an educational institute is the

number and percentage of successful graduates. Educational institutes establish and

implement strategies to improve student satisfaction and academic development to

enhance the number of completions,, while upholding the quality and capabilities of the

graduates. Further, institutes use technology to assist students to succeed in their study.

In this thesis, an intelligent recommendation system based on artificial intelligence and

data-mining techniques is proposed to assist university students in choosing the

appropriate programme of study and the relevant subjects. The proposed system uses a

Hybrid Classification Association framework (HCAF) to aggregate results from

different techniques to enhance the performance of the system and confidence in the

outcomes. Recommendations on the programmes in which students should enrol are

important because they have implications on student commitment and the students’

families. This chapter provides the background of the university system in Thailand and

the justification for this study. This is followed by an explanation of the techniques and

methodology adopted in the proposed recommendation system.

2.2 University System in Thailand 2.2.1 University types

Thailand is a developing country with a population approaching 69 million of which 20

per cent are below the age of 14 and 9.2 per cent are above the age of 65 [19, 20].

Education is a significant factor in enhancing the capabilities and opportunities for the

people and improving the quality and standard of living. Sangnapaboworn, Director of

the International Education Development Center at the Office of the Education Council,

stated that HE in Thailand should be the highest level in the education system and

should focus on various fields of knowledge and research. It is expected that HEIs will

develop community leaders who will lead and establish sustainable solutions to address

the nation’s issues and to expand the areas of research and technology development.

10

Therefore, educating students at the university level will have a positive effect on the

nation’s economic growth, art, culture and social welfare through the development of

appropriate programmes and projects [21].

Thailand’s oldest HEI, Chulalongkorn University, was named after King

Chulalongkorn, Rama V, the Fifth King of the Chakri Dynasty and was established in

1916 [22]. Since then, HE in Thailand has grown considerably. For example, the 1933

Thammasat University Act was passed after the 1932 revolution, which laid the

foundation for commitment to HE with the establishment of the Thammasat University

in 1934 as an open university. The aim of this was to propagate the knowledge of law

and politics to the Thai people. In 1960, Thammasat University changed the open

admission to a restrictive selection process [22]. Details of the history of HE in Thailand

have been reported in [23].

Prior to 1969, HE in Thailand was a state monopoly. Towards the end of the 1960s, the

demand for tertiary study grew steadily [24] and, in 1969, the Thai government

established two open universities to meet the increasing demand. Private colleges and

universities have also been established since the passing of the Private College Act in

1969 [24, 25], and they have played an important role in their contribution to HE in

Thailand. In a survey conducted in July 2008, the number of HEIs in Thailand was 164

and comprised of 78 public universities, 67 private universities and 19 community

colleges, which provided educational opportunities for Thai communities [25, 26].

Along with the rise in HEIs came an increase in student numbers. The 1999 National

Education Act extended free basic education from 9 to 12 years and increased the

number of students further. A report by World Bank stated that the HEI gross enrolment

rates in Thailand have risen from seven per cent in 1987 to 56 per cent in 2005 [25]. A

survey conducted by the Bureau of International Cooperation in November 2008

showed that the number of university students in Thailand was 2,032,638 with 64,115

faculty members working in 145 institutes. The total number of students in the HE

sector was estimated to be 2.2 million with 91 per cent enrolled in undergraduate

programmes [27]. The student to faculty member ratio was estimated at 31:1. Table 2.1

provides information on enrolment numbers in Thai HEIs from 1998 to 2006 and

demonstrates that student numbers have increased substantially from year to year.

11

Table 2.1: Enrolment numbers in higher education institutions in Thailand [26]

Year Total PhD Master Graduate

diploma Bachelor

Lower

than

bachelor

1998 1,033,325 1,725 73,364 1,332 947,907 8,997

1999 1,012,285 2,362 78,131 1,914 918,421 11,457

2000 1,103,888 3,190 89,563 2,456 994,240 14,493

2001 1,179,569 5,080 107,825 2,015 1,046,501 18,148

2002 1,273,096 6,213 126,123 4,087 1,122,812 13,861

2003 1,850,864 7,711 126,863 4,958 1,631,693 79,639

2004 1,804,573 7,949 136,552 9,881 1,579,508 70,683

2005 1,900,203 11,623 154,338 6,401 1,656,427 71,414

2006 2,123,024 14,765 181,292 8,191 1,850,846 67,930

Table 2.1 shows that the number of enrolments has increased by almost double from

1998 (1,033,325) to 2006 (2,123,024). In particular, the number of PhD enrolments has

grown dramatically from 1,725 in 1998 to 14,765 in 2006. These statistics indicate a

significant increase in the number of enrolments at Thai universities.

2.2.2 University admission process

The academic year in Thai HEIs is divided into two semesters. The first semester

normally runs from June to September and the second semester runs from November to

March. School breaks (2–4 weeks) occur between these two semesters in October.

During the long summer break from April to May, many universities provide an

optional short semester, which is known as the summer semester.

Currently, there are approximately 9,300 study programmes, ranging from lower

undergraduate to PhD degree programmes, in both public and private universities [28].

As in other countries, the degree system in Thailand provides bachelor, master and PhD

12

degrees and a bachelor degree is normally a four-year programme. The exceptions are

the pharmacy and architecture programmes, which are five-year programmes, and the

dental surgery, medicine and veterinary medicine PhD programmes, which are all six-

year programmes. A master’s degree requires two years of full time study, which can

incorporate two forms of study: course work and research. Similarly, PhD degrees

include both course work and research study, and the programmes typically take three to

five years to complete [26]. During the study period, students spend most of their time

at the university and many have to live away from home. This can lead to social

challenges for some students, which can often affect their studies. If a student has

chosen a programme that is not suitable for him or her, this will put additional pressure

on the student and may lead to drop out or failure.

To gain admission to university, students have to fulfil certain entrance requirements.

Specifically, they need to participate in the assessment organised by the Central

University Admissions System (CUAS), which commenced in 2006. The CUAS

replaced the national entrance examination, which had been used for over four decades.

The CUAS aims to enable each individual student to study in one of the programmes

offered by the public universities. However, students who do not receive an offer from a

public university or who do not take the CUAS assessment have to consider alternate

options, such as private universities, open universities or Rajaphat universities.

There are other reasons why students may choose to enrol in private universities. For

example, they may select programmes based on reasons such as a particular programme

is not offered at the public universities, the proximity of the private university to their

home, examples or advice received from friends and parents, preferred learning modes

are offered or a better resourced environment. These students need to meet the financial

requirements, such as higher tuition and related fees, to study at the private universities.

However, some students may obtain education loans from the government. In 2006,

there were 1,846,301 students enrolled in public universities, including open

universities, and 276,723 students enrolled in private universities, making up

approximately 15 per cent of the overall student population in the HE sector [29].

13

2.2.3 Student relationship management in Thai universities

With a focus on private universities providing better services to students, one issue that

requires attention is the problem of low student retention. This problem can be

attributed to low student satisfaction, student transfers and dropouts [5] and leads to a

reduction in enrolment numbers and revenue and an increase in the cost of replacement.

Conversely, it was found that the quality and convenience of support services are

factors that may influence students to stay or change education institutes [6, 16]. An

understanding of the available information can assist student management, student

services and market operation. In addition, it is important to develop strategies to

maintain and enhance student satisfaction to achieve the above objectives. One

approach involves the establishment of an SRM system. A definition of SRM can be

adopted from the established practices of CRM in businesses, which focuses on

customers and aims to establish effective competition and new strategies to improve an

organisation’s performance [30]. SRM is used within the education sector. Although

there have been many research studies focused on CRM, few have concentrated on

SRM. As reported by Piedade and Santos [15], the technological supports are

inadequate to sustain SRM in universities. For instance, an SRM system was proposed

to support the SRM concepts and techniques that assist a university’s business

intelligent system by providing a tool to aid tertiary students in their decision-making

processes. The SRM strategy also provided the institution with SRM practices,

including planned activities for the students and other relevant participants. However,

the study concluded that the technological support for the SRM concepts and practices

was insufficient at the time of writing [15]. In the literature concerning CRM and SRM,

a number of other proposals and examples were found.

Verhoef [32] and Bolton et al.[33] focused on customer retention, which can be

considered similar to the goals of students retention. Ackerman and Schibrowsky [31]

applied the concept of business relationships and proposed a relationship marketing

model, as shown in Figure 2.1.

14

Figure 2.1: Relationship marketing model for student retention [31]

The relationship marketing model illustrates an alternative aspect of student retention by

providing a different perspective on retention strategies. For example, the financial

bonding activities and programmes in Figure 2.1 provided a different view on retention

strategies and an economic justification on the need for implementing retention

programmes. A prominent result was the improvement of graduation rates by 65 per

cent by retaining one additional student from every ten [31]. In their study, it was

recognised that the focus of student retention could adopt the principles of relationship

marketing, and this contributed towards maintaining a stronger relationship with the

students [34–36].

In the context of educational institutes, management can consider students to have a

role similar to that of customers. The objective of SRM is to increase student

satisfaction and learning experiences. SRM may be defined similarly to CRM and aims

to develop and maintain close relationships between the institute and the students by

supporting the management processes and monitoring the students’ academic activities

and behaviours. Piedade and Santos [15] explained that SRM involves the

identification of performance indicators and behavioural patterns that characterise the

students and the different situations under which the students are supervised. Therefore,

SRM can be used as an important means to support and enhance student satisfaction.

Weaker Bonds

Stronger Bonds

Financial Bonding

Activities and Programmes

Student Retention Social Bonding


Structural Bonding


15

As understanding the needs of the students is essential for enhancing their satisfaction,

it is necessary to prepare strategies in both teaching and related services to support

SRM. Therefore, this thesis proposes an intelligent information system to assist

students in universities to support the SRM concept.

2.2.4 Student counselling in universities

One type of service that supports SRM and is provided by most universities is student

counselling. Archer and Cooper [9] stated that the provision of counselling services is

an important factor contributing to students’ academic success. Further, the

advancement of technology in educational institutions could create opportunities for

substantial improvement in management and information systems. Many designs and

techniques now allow for better results in analysis and recommendations. With this in

mind, universities in Thailand are working towards improving education quality [37]

and many institutes are focusing on how to increase student retention rates and

completion rates. In addition, a university’s performance is also increasingly being used

to measure its ranking and reputation [38]. Urata and Takano [10] stated that the

essence of student counselling should include advice on career guidance, identification

of learning strategies, handling of interpersonal relationships and self-understanding of

the mind and body. It can be said that a key aspect of student services is to provide

programme guidance, as this will assist the students in their programme selection and

future university experience. Other research focused on the provision of counselling and

careers services, which have been adopted by many universities. To enhance the

university’s mission, the prominent services provided by universities are psychological

counselling, careers and work-placement advice and financial assistance.

Conversely, many students have chosen particular programmes of study because of

perceived job opportunities, peer pressure and parental or family advice. Issues may

arise if a student is not interested in the programme or if the programme or career is not

suitably matched with the student’s capabilities [11]. In Thailand’s tertiary education

sector, teaching staff may have insufficient time to counsel students because of high

workload and inadequate support tools. Hence, it is desirable that some form of

intelligent recommendation tool was developed to assist staff and students in the

enrolment process. This forms the motivation of this research.

16

2.2.5 Background information on students

With various pathways available to gain admission to private universities, students have

the benefit of choosing from a range of programmes. However, producing graduates

who are suitable for and effective in the workplace is also an important objective for

private universities because the employability and demands of graduates reflect the

quality of the university and the programmes. In turn, this will affect the number of

future applications and demand for the programmes. The enrolment process is an

important and integral part of a student’s experience, as it assists the student in selecting

the appropriate programme of study, mapping the student’s ability with better chances

of graduation and possibly good results during the programme [39].

From the university’s perspective, this issue is also related to the allocation of resources

and the recruitment of high calibre students who have a high probability of completion

and good results. If the selection of programmes and allocation of students are not

mapped appropriately, this could lead to unfulfilled places and loss of potential tuition

fees. Research has shown that the problem of student retention in HE can be attributed

to low student satisfaction, student transfer and dropout [5]. Apart from the loss of

students and revenue, this issue also increases the cost of replacement, as students need

to be recruited from advanced years instead of from the first year. Moreover, it was

found that the quality and convenience of support services are also factors that influence

students to change educational institutes in HE [6]. Hence, a system that recommends

more appropriate programme placement, leading to higher level of success, could be

considered a high quality supporting service, thereby, increasing student retention.

Other studies focused on issues relating to student backgrounds prior to their enrolment,

which may affect the progress of the students’ studies. For example, a research group

from the Department of Education, Thailand [40], studied the backgrounds of 289,007

Grade 12 students to determine the factors that might have affected their academic

achievements. The study showed that personal information, such as gender and

interests, parental factors, such as jobs and qualifications, and information on the

schools, such as their size, type and ranking, were determining factors. Therefore, these

factors have been used as parameters for the proposed recommendation system in this

study to make the appropriate recommendations for students.

17

2.2.6 Justification for the proposed recommendation system

Prior studies have addressed issues faced by Thai students during their time at

university. For example, Sarawut [41] studied the causes of dropouts and programme

incompletion among undergraduate students from the Faculty of Engineering at King

Mongkut’s University of Technology North Bangkok. It was reported that the general

reasons for under achievement were due to teaching and learning issues. Further, the

study showed that there were three groups, which each had different reasons for not

completing their studies. The first group’s primary reason for incompletion was the

students’ attitude towards the field of study. This group felt that their field of study was

too difficult. The second and third group’s primary reasons were related to teaching and

learning. Hence, this indicated the need to match the programme requirements with the

academic capabilities of the students.

Another study at the Dhurakij Pundit University, Thailand, examined the relationship

between learning behaviour and low academic achievement (below 2.0 GPA) of first-

year students in regular four-year undergraduate degree programmes. The results

indicated that students who had low academic achievement had a moderate score in

every aspect of learning behaviour. On average, the students scored the highest in class

attendance, followed by the attempt to spend more time on study after obtaining low

examination grades. Some of the problems and difficulties that affected students’ low

academic achievement were students’ lack of understanding of the subject and the lack

of motivation and enthusiasm to learn [42].

While most Thai students considered a university degree an essential part of their

education, many of them did not know which programme and subjects to study. One

service that can help students and staff with this challenge is the student counselling

service, which provides programme advice and counselling for new students to achieve

a better match between the student’s ability and the chances of success in completing

the programme. In private universities in Thailand, this service is normally provided by

counsellors or advisors who have many years of experience in the organisation or in

HE. However, with the increasing number of students and expanding number of

choices, the workload on advisors is becoming too much. It is apparent that some form

18

of intelligent system will be useful in assisting the advisors and this forms the

motivation of this study.

In summary, it is necessary to meet student needs and to match their capability with the

programme of their choice in the recruitment and enrolment of students in private

universities. The students’ backgrounds may also have a part to play in the matching

process. Understanding student needs will implicitly enhance the student’s learning

experience and increase their chances of success, thereby, reducing resource wastage

that is due to dropouts and change of programs. Therefore, these factors are considered

in the proposed recommendation system in this study.

2.3 Intelligent Techniques for the Proposed Recommendation System

Herlocker [43] defined a recommendation system as one that predicts an interesting or

useful item for the user. Within the context of recommendation systems, intelligent

techniques used in data mining to find models and relationships between data are used

to classify and analyse information in databases [44]. There are reported studies that

focused on the improvement of recommendation systems [45-50] and other studies that

focused on management issues in the HE system [51]. Application examples of

intelligent techniques and recommendations include assessment of students’ academic

performance [52–57], recommending students for remedial classes [40], managing

classroom processes [57, 58], student satisfaction [56, 59], programme enrolment [39],

graduation or academic success [60], student dropout [61] and student retention [62]. In

this study, ANN, SVM, DT, K-means clustering and AR are employed in the

experiments. In addition, two aggregation methods, ensemble and MANN-OWSR, have

been applied to improve the performance accuracy of the prediction models. The basic

concepts of the techniques used in this thesis are described below.

19

2.3.1 Artificial Neural Networks

ANNs have been used extensively in machine learning [51] and in various applications

for data analysis. For example, an ANN was used to analyse Internet traffic data over

Internet protocol networks [63] to recognise faces [64] and to enhance the creation of

targeted strategies based on computational intelligent techniques for CRM [65]. In

addition, Kala et al. [66] reported that ANNs and machine learning have been used in a

large number of research studies dealing with huge datasets, such as handwriting

recognition. With respect to the neural network algorithm used in this study, the Feed-

Forwards Neural Network, also called Multilayer Perceptron (MLP), was used. A

multilayer feed-forwards network is shown in Figure 2.2.

Figure 2.2: A multilayer feed-forwards network

In the training of an MLP, the back propagation (BP) learning algorithm is commonly

used to perform the supervised learning process [67]. During the training phase, data are

applied as input to the neural network and the data generated by the network at the

output layer is considered the prediction output. The output is then compared with the

expected data. The differences between the prediction output and the values of actual

output are then used in the BP algorithm to update the connection weights of the

neurons to improve the prediction performance. The process repeats until certain

stopping criteria are reached, such as a predefined system error, or after a certain

number of iterations have been executed. After the training process, the network is used

for the prediction of output based on new input. Assuming that the subsequent inputs

Input layer Hidden layer 1 Hidden layer 2

Output layer

y1

y2

yn

x1

x2

x3

xn

Layer 1 Layer 2 Layer 3 Layer 4

20

are of similar characteristics with the training data, the neural network is able to perform

prediction with reasonable accuracy.

In the feed-forwards calculations used in this experiment, the input neurons are

activated with the values of the encoded input fields. In the hidden layer or output layer,

the activation of each of the nodes is calculated according to the following expression:

ai = σ (∑jWijOj) (1)

where ai is the activation of neuron i, j is the set of neurons in the preceding layer, ѡij is

the weight of the connection between neuron i and neuron j, Oj is the output of neuron j

and σ(x) is the sigmoid transfer function, which is shown as follows:

σ (x) = 1/(1+e-x) (2)

The BP learning algorithm updates the network weights and biases in the direction in

which the system performance increases most rapidly. The process stops when certain

termination criterion is reached and the network is considered trained.

There are other studies on the application of ANNs in recommendation systems. An

example was given by Superby et al. [54]. They used data-mining techniques to

determine the factors influencing the achievement of first-year university students.

Their study classified students into three groups: low-risk, medium-risk and high-risk

students. Their report presented results from the use of machine learning techniques,

such as neural networks, DTs and random forests. The findings showed that the

prediction results were not remarkable; however, the authors stated that this was

because the dataset from the three universities was not appropriate for the proposed

techniques in their study.

2.3.2 Decision tree

The DT technique resembles an inverted tree structure consisting of nodes and branches

connecting the nodes. Generally, the bottom nodes are called ‘leaves’, which are used to

specify different classes, and the top node is called ‘root’, where all the training

examples are applied. These examples are then classified into appropriate classes [68].

In this study, the Chi-squared Automatic Interaction Detector (CHAID), developed by

21

Kass [69] and Hawkins [70], was used. The CHAID algorithm is a highly efficient

technique that is capable of building classification tree models with an aim to identify

the most important predictors based on adjusted significance testing. CHAID uses a

Chi-square test to determine the split data in the DT.

Many recommendation systems have used DT algorithms. Vialadi et al. [39] proposed a

recommendation system to help student decision-making in programme enrolment by

predicting failure or success using a classifier. Their study employed production rules in

a pattern discovery module to discover the patterns and the DT (C4.5) algorithm in the

sub-modules. Their study aimed to develop a system to predict failure or success in the

chosen programme of study. The results of the study showed that the global accuracy of

the trial was 77.3 per cent.

Another focus on recommendation systems centred on its use as a marketing tool in e-

commerce. Kim [71] employed several data-mining techniques, including DT, and their

experimental results showed that the CHAID algorithm performed better than the other

models with statistical significance. Hence, CHAID is being incorporated in the

proposed system in this thesis.

2.3.3 Support vector machine

SVM is a classification technique and supervised learning method developed by Vapnik

[72]. It [73, 74] creates the input–output mapping functions, which can be either a

classification function or a regression function from a set of training data. SVM has also

been used in various prediction and recommendation works. Bo and Luo [75] proposed

a personalised recommendation algorithm that used SVM to classify the data for

collaborative recommendation in a web information recommendation algorithm. Xu et

al. [76] used SVM and other techniques to find hidden relational models; the approach

of their study realised a solution for recommendations based on the features of the

items, the features of the users and their relational information.

22

2.3.4 Association rules

An AR is used to discover and establish relationships or associations between values of

categorical variables in large datasets [68, 77]. Two main parameters are used in

building ARs: support and confidence. One form of AR is as follows:

A B [support, confidence] (3)

where an occurrence of A implies occurrence of B, with a given value of ‘support’ and

‘confidence’, which are the measurements of an association rule. ‘Support’ is connected

to the coverage of a rule and ‘confidence’ is related to the trust that is likely to be in the

prediction of the rules. The support of a set of items is the percentage of transactions

that are composed of all items, while the confidence of a rule A B is the percentage

of transactions that comprise all items in B and the value of confidence indicates the

strength of the rules. It can be calculated as follows:

Confidence (A B) = (4)

Significantly, an AR produces an if–then statement in terms of rules [78]. In this case,

the following example can be given: A = shampoo and B = conditioner. If A is

purchased, then it is likely that B is also purchased in the same transaction. The

expression on the establishment of AR can be formatted as follows:

Let I = {i1, i2,…, in} be a set of items and D be the set of transactions (5)

where each transaction (T) consists of a set of items and is associated with an identifier

TID and n is the number of items [68].

In previous research reports, ARs have been used in various applications. For example,

McNee [79] used an AR to identify users who liked particular writers. The system then

recommended all books from the users’ favourite authors. A study by Demiriz [50]

employed an AR to find the user rating for items in online e-commerce customer

databases. Therefore, ARs could be used to examine the similarities between students in

a dataset and, as such, it is incorporated in this thesis to find programme and activity

recommendations for students.

23

2.3.5 K-means clustering

Clustering techniques are popular in machine learning for the partitioning of groups of

similar data in a dataset [80]. Clustering has been applied in diverse problems. For

example, clustering was used to analyse the customer relationship in security trading

[81], to replicate microarray data for various covariance structure [82] and to classify

customers for customer segmentation [83–88]. K-means is a popular clustering

technique and a traditional partition-based method [6, 14]. In [89], Sarwar et al.

mentioned that K-means clustering is popularly used because it is fast and it is able to

produce a proper size of clusters. In their research, K-means clustering was employed to

produce a high quality recommendation for a large number of customers and products.

It was found that using K-means clustering could improve the scalability of the

recommendation system. Similarly, K-means clustering is employed in this study to

improve the performance accuracy of the recommendation system for university

students.

2.3.6 Confidence-weighted voting ensemble

Ensemble is a widely used method for improving the performance of multiple

classification systems. For example, an ensemble neural network could be constructed

by training a number of individual neural networks and then aggregating their outputs.

Kim and Kang [90] proposed an ensemble method based on boosting and bagging

methods to improve the performance of neural networks on bankruptcy prediction tasks.

Another example is Baruque and Corchado’s [91] study, which used the weighted

voting ensemble to achieve the lowest topographical error for the results of an ensemble

of self-organising maps to achieve the best visualisation of the dataset’s internal

architecture. Rico-Juan and Inesta [92] proposed the confidence voting method

ensemble to decrease the final equal error rate for offline signature verification. In this

thesis, the confidence voting method ensemble is employed to achieve the lowest

prediction error for the recommendation models.

24

2.3.7 Modular Artificial Neural Networks-Optimised Weight of Subspace

Reconstruction

In prediction, Frayman et al. [93] suggested that better results could be achieved by

aggregating the forecast results from multiple techniques instead of choosing the best

one. Using the concepts of Tobler’s first law: ‘everything is related to everything else,

but near things are more related than distant things’ [94], Kajornrit et al. [2] proposed

the use of MANNs, which comprises two aggregation methods: the Inverse Distance

Weighting Method (IDWM) and the OWSR, to estimate missing monthly rainfall data.

The architectural overview is described in Figure 2.3.

Figure 2.3: Development of the MANN-OWSR model [2]

In Figure 2.3, the MANN-OWSR method has been applied. Suppose β is a small region

around an input vector of student data; Zfinal and the vectors of a set of training data (Z1,

Z2,…, Zk) are the data points within the region β in which:

|| Zi - Zfinal || < β (6)

This technique can be illustrated by considering a new student as being represented by a

two-dimensional vector x and y, and the radius of the boundary is β. This is shown in

Figure 2.4.

Input

1st Model (ANN)

2nd Model (SVM)

Output Aggregation

Training Data

(For MANN-OWSR)

25

Figure 2.4: The vector of a new student as X1Y1 within a boundary of β. Only the

training data within β are used for the MANN-OWSR model [2]

By applying the MANN-OWSR approach in this study, the data (X1Y1) is the input of

the training model ANN, and the output of ANN is set as Z1(ANN). Similarly, the output

of SVM can be set as Z1(SVM), as shown in Figure 2.5.

Figure 2.5: Training data used in the MANN-OWSR model [2]

In general, the final value of the modular model could be the linear combination of the

estimated values of each module. In this case, the final value could be expressed as

follows:

Z(final) = W1Z(ANN) + W2Z(SVM) (7)

where Z(final) is the final estimated value, ZANN and ZSVM are estimated values from the

ANN and SVM module, respectively, and W1 and W2 are combination weights. The

summation of combination weights is equal to 1:

ANN Model

SVM Model

Input

(X1Y1)

Input

(X1Y1)

Output

Z1(ANN)

Output

Z1(SVM)

Y

X

X1Y1

26

1 = W1 + W2 (8)

By substituting Equation 8 for Equation 7, the formula can be expressed as follows:

Z(final) = W1Z(ANN) + (1-W1) Z(SVM) (9)

Therefore, the problem is how to find the optimal weight (W1) that provides the best

final estimation result. By using the MANN-OWSR method as mentioned above, the

optimal weight can be found by minimising cost function in Equation 10, as follows:

(10)

where is the mean square error, is the final estimated value from the model, is

the observed value associated with the data point i and k is the number of closest value

points. Applying Equation 9 to Equation 10:

(11)

where is the mean square error, is the observed value, is the estimated

value associated with the ANN value point, is the estimated value associated with

the SVM value point, w is the weight associated with the target point and k is the

number of close value points. MANN-OWSR is an effective aggregation technique to

improve the accuracy of the classification models. It is chosen in this study as a means

to determine the optimal output.

2.3.8 Evaluation metrics of the intelligent recommendation system

To evaluate the recommendation systems, published works have been used to measure

the recommendation system by comparing the prediction numerical recommendation

values against the recorded actual values [95]. The accuracy metric can be formed as

follows:

casetotalcorrectionofnumberaccuracy

___

= (12)

27

There are many metrics that could be used to evaluate the recommendation algorithms,

for example, the means square error and the root of the means square error (RMSE)

values. There have been many research studies conducted on recommendation systems

that used the mean absolute error (MAE) as a metric to measure the performance of the

system. Willmott and Matsuura [96] found that MAE is more advantageous than

RMSE, which is a function of three types of error sets, rather than just one. They stated

that ‘MAE is a more natural measure of average error, and (unlike RMSE) is

unambiguous’. Therefore, MAE is employed in this study to compare it with previous

studies and as a measure of the deviation of the recommendations from the actual

values. It is formulated as follows:

nPiOi

MAEn

i∑ =−

= 1||

(13)

where Oi is the observed value, Pi is the predicted value and n is the number of

predicted data. If the MAE value is low, it means the performance of the

recommendation system is more accurate than predictions with a higher value of MAE.

As mentioned, many studies have used MAE as an evaluation metric [97–104].

Consequently, MAE is used to measure the prediction error in the following chapters.

Another common evaluation metric is the correlation coefficient (r) [2], which is used

in Chapter 5. Therefore, this study uses percentage accuracy, MAE and correlation to

evaluate the intelligent recommendation system.

2.4 Summary

This chapter provided a background of this study and outlined the relevant techniques

involved. Thailand’s university system was described, together with the relevant issues

and the need to establish an SRM system. In particular, the intelligent recommendation

system was introduced as an aid for students and university counsellors. This chapter

also provided the principles of various intelligent techniques, such as ANN, DT, SVM,

AR, K-means clustering and ensemble methods. A new technique, MANN-OWSR, was

introduced and evaluation metrics for assessing the performance of the recommendation

system were discussed. The next chapter details the proposed framework for this study.

28

Chapter 3: Framework of the Proposed Recommendation

System

3.1 Introduction

This chapter presents the framework of the proposed recommendation system. Various

data-mining techniques were employed in this study and three classification techniques

(ANN, DT based on the CHAID algorithm and SVM) were included. In addition, the

AR technique was used to find the relationships between the parameters and clustering

was used to find similar student data and group them accordingly. Finally, two

aggregation techniques, ensemble based on confidence-weighted voting methods and

MANN-OWSR, were used in the data analysis process to aggregate the results to

enhance the outcomes. The framework (HCAF) developed in this study comprises six

recommendation modules, which are explained in this chapter.

3.2 An Overview of the Proposed Recommendation System

The literature has proposed several solutions to support SRM in Thai universities;

however, few systems have focused on recommendation systems using historical

records from past graduates. This thesis proposes a recommendation system that uses

artificial intelligence and data-mining techniques to assist supervisors and counsellors in

making appropriate recommendations for students.

A private university in the south of Thailand provided datasets with suggestions based

on opinions and experience from their supervisors and counsellors and these were used

to determine the variables for the training data. Student background information, such

as high school attended, related school results and student performance in terms of

GPA, were suggested as prominent factors and were used in this study. The university

also provided student datasets and records from past years for use in the experiments.

The dataset parameters are typically used by most universities in Thailand and the

process in developing the proposed recommendation system is applicable to other

29

institutes, subject to the availability of the datasets and selection of the appropriate

parameters.

3.3 Description of the Modules and Their Purposes

Within the framework, the recommendation system is designed to provide suggestions

from six modules, which are described in the following sections.

3.3.1 Module 1: likely overall GPA

This module aims to provide the likely overall GPA for prospective students and new

students. It uses the data to train the GPA recommendation model in the experiment and

the output is given as the likely overall GPA. The prediction model and its development

are detailed in Chapter 5. In this module, the prospective or new student’s data, such as

expected programme of study, previous GPA and talents and interests, are used as the

input for the module and the results provide the likely overall GPA based on the

expected programme of study. This module can be used by counsellors and supervisors

in the enrolment process and for monitoring the new student’s performance during their

first semester at the university. This module is an essential component of the intelligent


3.3.2 Module 2: ranked programme recommendation

This module focuses on the ranked programme recommendation for students. The

prediction model and its development are detailed in Chapter 4. This module will assist

the counsellor or supervisor in recommending that students enrol in an appropriate

programme, as opposed to the student choosing the programme of study that their

friends have chosen, which can lead to mismatched choices. It is believed that this

recommendation will refine the enrolment process. This module recommends three

ranked programmes for the applicants based on results from past students who have

similar profiles to the current applicant. These recommendations use similar variables to

the previous module with the difference being that this module focuses on personal data

and previous school history. In terms of parameters, prospective students are required to

30

provide input data, such as previous GPA and talents and interests. This module

provides ranked programme options for the student. This information may be used by

counsellors to make suggestions to the students and parents. If an online intelligent

recommendation system is developed, this module could be made available directly to

users.

3.3.3 Module 3: likely GPA for each semester

This module aims to provide recommendations to assist counsellors and supervisors in

guiding students to select the subjects to study and plan for the following semester. The

likely GPA for each semester from the first semester of Year 1 through to the last

semester of Year 4 can be used to monitor the performance of any particular group of

students. The prediction model and its development are explained in Chapter 5.

The input data of this process are similar to that of the likely overall GPA module, the

differences being the addition of the GPA scores from the previous semester and the

target GPA for the next semester. For example, if a student studies in the first semester

of Year 1, this module will estimate the likely GPA of the second semester of the same

year. After the first semester of Year 1, students can provide input data, including the

GPA of the first semester in Year 1 with the target GPA of the first semester of Year 2.

When a student completes the second semester of Year 1, this module will provide the

likely result of the first semester of Year 2. The input data will include the GPA of the

first and second semesters in Year 1 with a target GPA of the first semester of Year 2.

These are used as extended features in the input data of the GPA recommendation

model. Similarly, the system can be used to assess the likely GPA for each semester in a

similar fashion. This module can also be used by counsellors and supervisors to guide

their respective students; however, it should be emphasised that the aim is to assist

students who are likely to be at risk and to encourage students to perform better than the

prediction. Hence, accuracy of the module is not the key objective; rather, the results are

used more as a guide than a goal.

31

3.3.4 Module 4: ranked activities recommendation

This module aims to provide ranked activities recommendations for students. The

prediction model and its development are illustrated in Chapter 4. There are five types

of activities: academic, such as academic competition, technological, such as computer

club, acting, such as theatre club, social development, such as rural development

volunteering club, and other, such as sports. This will help counsellors and supervisors

to recommend appropriate remedial activities for the students, which may help or

improve the student’s performance. The input data of this process are similar to the

previous modules, such as previous major, GPA from secondary school, talents and

interests and university major. The output ranks three types of recommended activities

based on results from previous students’ with similar profiles who have been successful

in their university study.

3.3.5 Module 5: programme completion identification

This module aims to assist lecturers, supervisors and counsellors to identify students

who may be at risk and who may need extra support. The system will alert the user

when a particular student is identified as being at risk of withdrawal or failure prior to

graduation. This module uses the dropout identification model to identify students who

are at risk. This is treated as a binary problem in this study. The prediction model and its

development are shown in Chapter 6. The input data of this process are based on similar

parameters to those used in the previous modules. The parameters include previous

major, previous GPA from secondary school, talents and interests, university major,

number of awards and guardian occupation. The output identifies the students who are

at risk of possibly withdrawing from the programmes before completion, based on the

results from previous students who had similar profiles at the time of their university

studies.

3.3.6 Module 6: postgraduate study identification

This module uses postgraduate study identification to identify students who may be

suitable and likely to succeed in postgraduate study. The prediction model and its

development are detailed in Chapter 5. This module focuses on the final year students

32

only. The system uses historical data to identify students who are likely to be successful

in future postgraduate study. This module also uses the GPA recommendation model to

provide the likely overall GPA in postgraduate study. The input data of this process are

based on similar variables to the previous modules, such as previous major, previous

GPA from secondary school, talents and interests, university major, number of awards

and guardian occupation. Other variables such as postgraduate major and overall GPA

from undergraduate study are also used. The output identifies students who have

postgraduate success in terms of four levels of GPA: likelihood of achieving a GPA

between 3.00 and 3.25, between 3.26 and 3.50, between 3.51 and 3.75 and between 3.76

and 4.00. The minimum GPA for a student to pass a postgraduate programme in

Thailand is 3.00; therefore, it is used as the starting value.

3.4 Description of Parameters Used in this Study

The variables selection process is important for the success of the proposed

recommendation system. In this study, Hatyai University provided previous internal

survey results from 62 supervisors and counsellors. Their survey investigated the

participants’ opinions and experience relating to the independent variables, which were

considered significant in determining the forecasted results for the students. The

participants’ opinions relating to the chosen variables are shown in Figure 3.1.

Figure 3.1 shows that more than 50 per cent of participants agreed with the use of the

first four independent variables in the experiment, while more than 50 per cent of

participants agreed or were neutral with the use of the other variables. Moreover, the

participants recommended additional variables to be used, and they have been chosen

for use in this experiment. The additional variables include programme of study from

previous school, programme of study at university and number of awards from previous

schooling.

33

Figure 3.1: Percentage of participants’ opinion in relation to independent

variables, the likely study level and programme of study

McKenzie and Schweitzer [105] demonstrated that gender correlated with GPA results

during university studies. Another study by Newman-Ford and Lloyd [106] confirmed

that gender also related with academic attainment but only had minor effects. A study

by Thai Education Research [107] found that gender, interests, parental jobs, parental

qualifications, previous school size, previous school type and previous school rankings

were significant to student success at universities. Another study found that high school

GPA was related to progress at university for new students and students in the four-year

programmes [108]. A research study by the Dhurakit Bundit University found that

learning behaviour in university is related to the GPA from the student’s previous

school [42]. In the process of collecting suitable variables for this experiment, the

variables suggested by participants in the survey results have been included. However,

some variables cannot be included in the experiment because of a lack of available data.

The numerical data of the overall GPA from previous schools and the overall GPA from

university were transformed to categorised classes based on means and standard

deviations (SDs) of all data. Other variables were transformed into categorical or binary

bins. The data variables available for use in this study are explained in the following

sections.

34

3.4.1 UniID

‘UniID’ is the student identification number from university. This is not included in the

training and testing results. Although UniID can be used as a student identifier, the

information has been randomised by the university and this study did not identify any

individual students. It was used for the validation and checking of results only.

3.4.2 GPAs

In terms GPAs, four variables were categorised statistically into a number of classes, as

shown below.

Table 3.1: Statistical parameters for the categorised GPAs

Statistic

Overall GPA &

GPA each

semester

Previous school

GPA

Postgraduate

GPA

Mean 2.720 2.977 3.434

Median 2.690 3.000 3.430

Mode 2.500 3.000 3.450

Standard deviation 0.466 0.618 0.186

Kurtosis –0.370 –0.077 –0.219315083

Skewness 0.083 –0.472 0.4546

Range 3.090 3.000 1.000

Minimum 0.910 1.000 3.000

Maximum 4.000 4.000 4.000

The statistics in Table 3.1 were used for overall GPA, GPA each semester, previous

school GPA and postgraduate GPA. These GPAs were transformed into classes based

on mean, SD and other statistics in each type of GPAs. These are described in the

following sections.

35

3.4.2.1 Overall GPA

‘Overall GPA’ (or university GPA) is the student’s overall GPA once he or she

graduates from university. It normally ranges from zero to four and two decimal points

were used (e.g., 3.45).

3.4.2.2 GPA each semester

‘GPA each semester’ is the student’s GPA from each semester, which is worked out as

an average over the total study time. The GPA scores are multiplied by the number of

subjects in each unit and then divided by the total number of units in each semester. The

GPA normally ranges from zero to four. This variable does not included summer

semesters.

As the GPA each semester was part of the overall GPA for each student data, the two

variables used the same criterion to classify the data. In Table 3.1, the SD and mean of

these two variables were 0.466 and 2.720, respectively. These variables were

transformed into six classes, as shown below.

Table 3.2: Six classes of overall GPA based on statistics

Class Minimum GPAs Maximum GPAs

0.1 0.910 1.425

0.2 1.426 1.941

0.3 1.942 2.457

0.4 2.458 2.973

0.5 2.974 3.489

0.6 3.490 4.000

36

3.4.2.3 Previous school GPA

‘Previous school GPA’ is a student’s secondary school GPA. As in university, the GPA

ranges from zero to four. In Table 3.1, the SD and mean of this variable are 0.618 and

2.977, respectively. Consequently, this variable was transformed into five classes, as

shown below.

Table 3.3: Five classes of previous school GPA

Class Minimum GPAs Maximum GPAs

0.1 1.000 1.618

0.2 1.619 2.237

0.3 2.238 2.856

0.4 2.857 3.475

0.5 3.476 4.000

3.4.2.4 Postgraduate GPA

‘Postgraduate GPA’ is a postgraduate student’s GPA once he or she graduates. In

Thailand, postgraduate study has a minimum GPA of 3.00 for a student to pass. In

Chapter 5, the GPA ranges for postgraduate study are set based on statistical

characteristics. In Table 3.1, the SD and mean of this variable are 0.186 and 3.434,

respectively. The values of this variable were transformed into five classes, as shown

below.

37

Table 3.4: Five classes of postgraduate GPA results

Class Minimum Postgraduate

GPAs

Maximum Postgraduate

GPAs

0.1 3.00 3.19

0.2 3.20 3.39

0.3 3.40 3.59

0.4 3.60 3.79

0.5 3.80 4.00

3.4.3 Previous major

‘Previous major’ is a student’s major or programme of study that was completed at

secondary school. However, previous majors from many types of schools were

transformed into binary bins. For example, a student’s previous major could be

accounting and his or her university major could be business computing. In this case,

the previous major would be different from the student’s programme of study at

university. Therefore, the variable is set at zero, as detailed below.

Table 3.5: Classes of previous major

Class Type of school

0 Different programme of study

1 Same or equivalent programme of study

3.4.4 Type of school

‘Type of school’ is the type of secondary school or college where students graduated.

Table 3.6 shows how these types of schools were grouped in this study.

38

Table 3.6: Classes of type of school

Class Type of school

0.1 High school

0.2 Technical college

0.3 Commercial college

0.4 Sports, Thai dancing, religion or handcraft training

schools

0.5 Other universities (change universities or

programmes)

0.6 Vocation training schools

3.4.5 Number of awards

‘Number of awards’ is the number of awards that a student has received from secondary

school or college. In this study, the number of awards is normalised between 0.0 and

one, as shown below.

Table 3.7: Class of number of awards

Class Number of awards

0.0 No award

0.1 Received 1 award

0.2 Received 2 awards








1.0 Received 10 awards or more

39

3.4.6 Talents and interests

‘Talents and interests’ is the information reported by enrolled students. The information

used in this study is shown in Table 3.8.

Table 3.8: Class of talents and interests

Class Type of talents and interests

0.1 Sports

0.2 Music and entertainment

0.3 Presentation

0.4 Academic

0.5 Other

0.6 Involved with two to three talents and

interests

0.7 Involved with more than three talents and

interests

3.4.7 Motivation channels

‘Motivation channels’ are the media from which students learnt about the programme of

study or the university. Table 3.9 shows how the motivation channels were set in this

study.

40

Table 3.9: Class of motivation channels

Class Motivation channels

0.1 Poster

0.2 Brochure

0.3 Teacher

0.4 Friend

0.5 Family

0.6 Internet

0.7 Newspaper

0.8 Visiting university

0.9 Television

1.0 Other

3.4.8 Admission round

‘Admission round’ is the university’s admission round, which includes Round 1 to

Round 5. For example, some students enrol at university in the first round because they

know exactly which programme and at which university they want to study, whereas

other students fail the entrance examination at one university and then enrol in the final

round of another university, which would be Round 5 or higher. Private universities

open many rounds of enrolment to ensure students are able to study at university. Table

3.10 shows how the admission rounds were classified in this study.

Table 3.10: Classes of admission round

Class Admission round

0.1 First round

0.2 Second round

0.3 Third round

0.4 Fourth round

0.5 Fifth round or higher

41

3.4.9 Guardian occupation

‘Guardian occupation’ is the occupation of a student’s parents or guardian, such as

teacher or government officer. For students who do not live with their parents, their

guardian’s occupation is considered instead. Table 3.11 shows how the guardian

occupations were classified in this study.

Table 3.11: Classes of guardian occupation

Class Type of guardian occupation

0.1 Housewife

0.2 Agriculture

0.3 Business or shop owner

0.4 Politician or government officer

0.5 Freelance

0.6 Police or nurse

0.7 Other

3.4.10 Gender

The gender of the student is either female or male, as shown below.

Table 3.12: Classes of gender

Class Gender

0.1 Female

0.2 Male

3.4.11 Activity type

‘Activity type’ is the type of activity at the university. The classes are illustrated below.

42

Table 3.13: Classes of activity type

Class Activity type

0.1 Academic activities, such as academic competition

0.2 Technological activities, such as computer club

0.3 Acting activities, such as theatres club

0.4 Social development activities, such as rural

development volunteering club

0.5 Other activities

3.4.12 University major

‘University major’ (or programme of study) is the student’s major at university. Some

programmes of study have been ignored because of insufficient or imbalanced data.

Seven popular majors are detailed below. Majors not listed below are not considered in

the recommendation system developed in this study; however, they could be

incorporated, should sufficient data and demand become available.

Table 3.14: Classes of university major

Class University major

0.1 Management

0.2 Accounting

0.3 Business computing

0.4 Marketing

0.5 Human resource management

0.6 Business English

0.7 Law

In the data preparation and selection process, student data included records from the

first year through to graduation. The data in this study did not reveal any personal

information because of privacy issues, and no form of student identification was

included in this research. The student data were randomised and all private information

was removed by the university. Example data from the dataset are shown below.

43

Table 3.15: Samples of variables in the training sample dataset for likely overall

GPA

UniID

Input data: previous school data Target

Pre-

GPA

Typ

e of

scho

ol

No.

of a

war

ds

Tal

ents

& in

tere

sts

Mot

ivat

ion

chan

nel

Adm

issi

on r

ound

Gua

rdia

n

occu

patio

n

Gen

der

Uni

vers

ity G

PA

4800 2.35 C 0.2 1 Poster 1 Police F 3.75

4801 3.55 B 0.3 4 Brochure 2 Governo

r

M 3.05

5001 2.55 A 0.2 3 Friend 5 Teacher F 2.09

5002 2.75 G 0.4 5 Family 4 Nurse F 2.58

5003 3.00 F 0.2 7 Newspaper 3 Teacher M 2.77

5101 2.00 E 0.1 2 Other 1 Farmer F 2.11

Table 3.15 shows some examples of the variables and student data, which included a

randomised student ID, the GPA from previous study, the type of school, awards

received, talents and interests, motivation channel, admission round, guardian

occupation, gender and overall GPA from university. In the data preparation process,

the continuous values, which are previous GPA (input data) and overall GPA (target)

from university, were transformed based on the mean and SD of all data and categorised

into ranges of five and six, respectively. This dataset included both qualitative and

quantitative information.

44

3.5 Methodology

Figure 3.2: Proposed intelligent recommendation system based on the Hybrid

Classification Association framework

As described in Chapter 2, data-mining techniques are deemed effective in

recommendation systems. Figure 3.2 illustrates the framework of the proposed

recommendation system and the details are provided below.

3.5.1 Data pre-processing

In the data pre-processing stage, data from previous student records were collected from

the university’s enterprise database. Initially, the data were re-formatted in the data

Student Historical Data

Data Cleaning Data Transformation

2. Data Analysis (Hybrid Classification Association Framework and Intelligent Recommendation Models: HCAF)

1. Likely overall GPA

2. Ranked Programme Recommendation

3. Likely GPA Each Year

Student—Year 1 to 4 Prospective student and new student

Student—Year 4

6. Postgraduate Study Identification

4. Ranked Activities Recommendation

5. Course completion Identification

Neural Network

SVM

AR

DT (CHAID)

Clustering

Data-mining Techniques

Aggregation Techniques

Ensemble

MANN-‐OWSR

ANN

SVM

AR

CHAID

Clustering

Models

Ensemble

MANN-‐OWSR

INPUTS

OUTPUTS

1. Data Pre-‐Processing

45

transformation stage to prepare them for processing by subsequent algorithms. In the

data cleaning process, the parameters used in the data analysis were identified and

records with missing data were either eliminated or the fields filled with null values

[18]. Preparation of analytical variables was done in the data transformation step or

completed separately. The integrity of the data was checked by validating it against the

legitimate range of values and data types. Finally, the data were separated randomly into

training and testing data categories for processing by a combination of data-mining

techniques. The percentages of data used for training, validation and testing were 60 per

cent, 20 per cent and 20 per cent, respectively.

3.5.2 Data analysis (Hybrid Classification Association Recommendation models)

In this study, the data analysis process was separated into three models:

• programme and activity recommendation model—this model is based on AR to

find associations for ranked programmes. The proposed techniques were applied

against classification techniques. K-means clustering is employed in the

proposed technique to classify data before rule extraction by AR to improve the

performance of the model and then the best model is chosen to predict the

ranked programmes and activities for prospective students. The details are

explained in the next chapter

• GPA recommendation model—this model focuses on improvement of prediction

models and chooses the best accuracy for the recommendation system. The

proposed techniques were applied against the classification techniques: ANN,

CHAID and SVM. To improve the performance of the models, the ensemble

method based on confidence-weighted voting and MANN-OWSR are employed.

In the final process, the best models, which showed the best performance

accuracy and the lowest accuracy error rate, are chosen to predict the overall

GPA for prospective students, new students, students in each academic year and

postgraduate students to identify potential students for continuing with

postgraduate study. This model is explained in Chapter 5

• programme completion identification model—the proposed techniques were

applied against the classification techniques. To classify student dropouts, K-

means clustering is employed and three classification techniques (SVM, CHAID

and ANN) are then applied to each cluster. All outputs of each cluster are

46

compared and the two models with the highest accuracy are chosen. Clusters of

the same model are then combined before the next step of aggregation. In the

combination process, two aggregation techniques (ensemble method based on

confidence-weighted voting and MANN-OWSR) are employed to aggregate the

two models with highest accuracy with the combination of all clusters of the

same model. The outputs of the two aggregation models are then compared and

the most accurate model is chosen. In the final step, the chosen model is

compared again with the classification models. Then, the best model is chosen

for the dropout identification module in the intelligent recommendation system.

This model is explained in Chapter 6.

The main classification techniques used in comparison with the proposed hybrid

techniques in each model are described in Chapters 4, 5 and 6. An ANN, a DT, SVMs

and ARs were used as the classification to train the input data in the three models. The

ANN used the feed-forwards algorithm to classify the data and to establish the

approximate function. The BP algorithm used was a multilayer network that used a log-

sigmoidal (logsig) transfer function. In the training process, the BP training function in

the feed-forwards networks was used to predict the output based on the input data. The

DT used the CHAID [69] algorithm, which created child nodes with optimal splits for

segmentations of the parameters (or tree growing). The CHAID also evaluated the

values of a possible predictor field with similar values merged and all other values

maintained. Further, SVM, a learning algorithm to classify the data developed from

statistical learning theory [72], was used in the model. SVM learns structure from data

and has the ability to classify unseen data correctly. These three classification

techniques were used to compare the results of three techniques. ARs were used to

discover hidden relationships between the chosen variables, using if–then statements in

terms of rules. This technique constructed the rules to find the association between the

student data attributes. In this study, ARs were used to find the rules for the outputs of

three ranked programmes of study and activities in the recommendation models, and

clustering was also used to find relationships within the data to build a group of clusters.

This system provides recommendations on suitable activities that may improve a

student’s performance. Examples of such activities are drama, debate, volunteer work

and other social clubs, which will improve student communication, social and

47

intellectual capabilities. Moreover, the system provides identification of students who

are likely to succeed and students who are likely to fail in relation to study. Such

information will nominate students who may need extra support from supervisors,

counsellors or lecturers. Finally, the system provides identification of final year students

who are likely to enrol in postgraduate studies and who are likely to succeed. These

processes use comparison results from three classifiers in the experiment and the data

were based on historical records from the university’s database. The results from the

classification models were combined and improved by using ensemble methods and

MANN-OWSR and the best result, in comparison to the classification techniques, was

chosen, along with appropriate recommendations.

3.5.3 Validation of model based on intelligent recommendation system

In this research, all training, validation and testing data were randomised and generated

prior to each training, validation and testing session to ensure that the comparison

between the three different classification techniques did not occur by chance.

In terms of the appropriateness of parameters and performance of the model, the results

were only tested by counsellors and university officers from the organisation that

supplied the data because of privacy issues. All comments provided in their reports

were maintained as confidential.

The goal is that the new intelligent recommendation models will form an integral part of

an online system for private universities in Thailand. The developed system will be

evaluated by university management and experienced counsellors. In some modules, the

proposed system will be available for use by new students who will access the online

application during the enrolment process. The recommendations for current students

and subsequent years’ results could be used by counsellors, staff, supervisors and

university management to provide support for students who are likely to need help with

their studies. This information will enable the university to use their current resources

with greater efficiency. In particular, this could be used to improve the retention rate by

providing additional support to students who are identified as being most at risk.

48

Chapter 4: Programme and Activity Recommendation

4.1 Introduction

The framework of the proposed intelligent recommendation system was described in the

previous chapter. This chapter discusses the programme and activity recommendation

modules from the intelligent recommendation system HCAF in relation to the proposed

model of ranked programme and activity recommendation. The main objectives of the

recommendation modules are to recommend three ranked programmes and activities for

students as a multiclass-classification problem based on historical data. In the proposed

model, ARs based on generalised rule induction (GRI) algorithms are employed first.

Next, they become the classification for comparison with the proposed combination of

K-means clustering and GRI. The combined use of ARs and clustering methods was

performed to improve the accuracy of the ranked programme and activity

recommendation. The metrics used to measure the performance of each method were

the prediction performance accuracy and the MAE. In the experiment process, the

Statistical Package for the Social Sciences Clementine was used in the first step of

finding rules using ARs based on the GRI algorithm. Matrix Laboratory was then used

to match the rules with the student profiles from historical data and to predict the three

ranked targets for the programme and activity recommendations for students.

This chapter is separated into various sections. Section 4.2 presents the objectives of the

chapter and Section 4.3 presents the input and output variables selection with a

description of the dataset. The experimental design is explained in Section 4.4 and a

discussion on the instructional techniques that are employed is provided in Section 4.5.

Section 4.6 presents the experimental results, which is followed by the chapter

discussion and conclusion. The final section describes the contributions of the

techniques used in this chapter.

49

4.2 Objectives

This chapter aims to:

1. find the ranked programme and activity recommendation based on past records

from the student database. This is intended to assist supervisors and counsellors

in advising prospective students and enrolled students at university

2. investigate and develop the ranked programme and activity prediction model in

the proposed intelligent recommendation system based on the HCAF. ARs are

employed to identify the relationship between the data

3. improve the performance of the recommendation model using clustering

techniques

4. propose the integrated techniques and improve the accuracy of the

recommendation model in the proposed intelligent recommendation system

HCAF.

4.3 Input and Output Variables Selection

In this experiment, the sample data were chosen from the university’s database of

11,400 student records. After the data cleaning process, 9,001 student records were used

in this study. The distribution of the students, with respect to programmes, is illustrated

in Figure 4.1.

50

Figure 4.1: Number of undergraduate students by programme of study (2001–

2007)

In Figure 4.1, the tertiary student data were obtained from seven academic years of

records (2001–2007), excluding summer semesters. Student data included records from

first year to graduation. The data comprised of 30.62 per cent of students from business

computing, 19.02 per cent from accounting, 22.18 per cent from management, 14.75 per

cent from marketing, 5.2 per cent from human resource management, 4.84 per cent from

business English and 3.38 per cent from law. The data in this study did not indicate any

personal information because of privacy issues, and no student was identified in the

research. The university randomised the data and all private information was removed

in this experiment.

As mentioned in Chapter 3, the process of choosing variables was based on results from

a survey conducted and provided by the university. The variables used in these two

modules are shown in Table 4.1.

51

Table 4.1: Variables used in the ranked programme and activity modules

No.

Module 2:

ranked programme

recommendation

Module 4:

ranked activity recommendation

Variable name Type Variable name Type

1 University Major* Target Activity type* Target

2 Previous school GPA Input Previous school GPA Input

3 Gender** Input University major** Input

4 Talents and interests Input Talents and interests Input

Note: * and ** refer to different variables between two models.

To choose variables to support the programme and activity selection for GRI

algorithms, the study of Geiser and Santelices [101] found that previous school GPA

was the best predictor not only for new students but also for student outcomes in four

years. Another study found that gender and interests also related to the success of study

of tertiary students [107]. Therefore, the variables chosen in Module 2 (previous school

GPA, gender and talents and interests) are input variables with the target of major or

programme of study. In addition, with the purpose of choosing activities to improve the

student’s performance in their study and future career, a study by Hoover and Dunigan

[109] found that the majority of students who joined collegiate organisations also

improved their performance during their study and future career. In the framework,

‘university major’ is a significant input to discover the types of activities that should be

supported by extracting the successful cases from the student database. This ranked

activity recommendation module provides information on recommended activities to the

students after they have determined their programme of study at university and before

obtaining their GPA results in the first semester. Most students are expected to use the

ranked programme activity at the beginning of the first semester. In the same module,

the three variables (previous school GPA, university major and talents and interests) are

input with the target output from the module to be ranked activities. Details of the

methodology used in this experiment are described in the next section.

52

4.4 Experiment Methodology and Design

This section describes the methodology and the ranked programme and activity

recommendation model. Normalisation of the data was first carried out as an essential

step in pre-processing. To prepare the dataset for the GRI algorithm in the data analysis

process, quantitative data was required. For the training, validation and testing of the

model, the dataset was randomised and divided into three sets: 60 per cent, 20 per cent

and 20 per cent of data, respectively. The proposed model is illustrated in Figure 4.2.

Figure 4.2: Process to compare performance of GRI for ranked programme and

activity recommendations

The GRI algorithm was used in the first stage. To improve the prediction accuracy, the

K-means clustering technique was incorporated with the GRI algorithms, as shown in

Figure 4.2. In this study, 9,000 random records with the aforementioned parameters for

ranked programme and activities were used. Based on the recommendations from

supervisors and lecturers, the number of clusters used was two. The model execution

flowchart is provided in Figure 4.3.

Result

Comparison

Input Association Rules (GRI)

Output

Association Rules Technique

Proposed Model

Input K-means

Clustering Cluster 1

Cluster 2

Association Rules (GRI) Output

Best Result

53

Start

End

Rule Extraction

Filter Rules to 3 ranksBy Confidence

Confidence 80-‐100%?



Set Rules with confidence 80-‐100%

to Each Major/Activity

N

N

Y

Student database

Match student profiles with Target from each group of rules for each

ranking

Major/Activity 3?

Major/Activity 4?

Major/Activity 5?

Major/Activity 1?

Major/Activity 2?

Set Rules with confidence 60-‐79% to Each Major/

Activity

Y

Set Rules with confidence 40-‐59% to Each Major/

Activity

Y

Major6?

Major7?

N N

Show Results of Rank 1,2 and 3 for majors and

activities

Y

Y

Y

Y

Y

Y

Y

N

N

N

N

N

N

Show Results of Rank 1,2 and 3 for majors and

activities

Figure 4.3: Flowchart to derive recommendation for three ranked programme

majors and activities

Figure 4.3 shows that, after determining the ARs using GRI algorithms to find the

correlations between student records in the dataset, the confidence levels of the rules

from the results in the first stage are sorted according to the ranked programme majors

and activities. The extracted rules are filtered and categorised according to the

confidence levels 80–100 per cent, 60–79 per cent and 40–59 per cent as the top, second

and third ranked programme majors and activities, respectively.

After the three rule levels have been set, the next step is matching the rules with the

student profiles. Examples of the displayed results are shown in Table 4.2 and 4.3.

54

Table 4.2: Example results of ranked programme recommendation

Rank Recommended

programme

Programme name

1 Programme = 0.2 Accounting

2 Programme = 0.1 Business Computing

3 Programme = 0.5 Human Resource

Management

Table 4.3: Example results of ranked activity recommendation

Rank Recommended

activity

Activity name

1 Activity = 0.1 Academic activity, such as academic

competition related with student major

2 Activity = 0.3 Acting activity, such as theatre club

3 Activity = 0.4 Social development activity, such as rural

development volunteering club

The results in ranked format are provided to counsellors and supervisors to assist them

with their recommendations for the students.

4.5 Intelligent Technique Used

Data-mining techniques were used in various recommendation systems to determine the

relationship between data records [110]. Classification is one important technique in

data mining that can be used to classify data and discover knowledge from large

databases [111]. In this study, to solve the multiclass-classification problem, the AR

tool, proposed by Agrawal et al. [77], was an important tool in data mining that aimed

to extract a model to find the relevant relationships between the attribute set and class

labels [112]. There have been many research reports on the use of AR for classification

purposes [63, 77–80, 113–121]. Example applications are product recommendations

[79, 122–124], student performance recommendations [89], user-rating predictions [50]

and book recommendations [4].

55

A concept to construct a concise and accurate classifier using an AR was proposed by

Xu et al. [80]. They presented a novel classification algorithm classification based on

atomic ARs (CAAR). Compared with the DT algorithm, they claimed that their

proposed CAAR classification rule set achieved the highest average accuracy and was

faster than classification based on ARs.

Another study by Paireekreng et al. [63] proposed an integrated method by using

classification and association rule techniques to extract knowledge from mobile content

in a user profile. This proposed method simplified the association from outcomes of the

classification and clustering processes for the non-interactive recommendation system.

Another study by Soliman and Adly [82] also proposed an algorithm using an AR to

find the best subset of rules for all possible ARs to build an efficient classifier [125].

Therefore, many research reports have shown that ARs are an accomplished technique

for the classification [82, 83, 126, 127]. In this study, ARs based on GRI were used to

extract the rules for the multiclass-classification problem. Many research reports have

shown that the results of ARs based on GRI were of high quality [91, 92].

To improve the performance of ARs, K-means clustering was introduced by Tou and

Genzalaz in 1974. Liu and He [105] and Khattak et al. [106] suggested that clustering

can classify data and improve the accuracy of ARs. Plasse et al. [97] found that the

clustered data, which were extracted by ARs, gained more accuracy than normal data.

Therefore, in this proposed GPA recommendation model, K-means clustering was used

to enhance the performance of the model.

4.6 Experiment Results

This section compares the results from the GRI algorithms with the results from the

combination of K-means clustering and GRI algorithms. In the example results

illustrated in the tables, ‘consequent’ represents the target programme or activity,

‘antecedent’ represents the extracted rules, ‘support’ shows how often the rule appears

in the student dataset and ‘confidence’ represents the percentage of number of

transactions, including all target programmes or activities in the consequent, as well as

the antecedent, to the number of transactions that include all items in the antecedent.

56

4.6.1 Example results of ranked programme and activity recommendations based

on GRI algorithm

Example results from the GRI algorithm are shown in the following table:

Table 4.4: Example results of rules extraction by GRI for ranked programme

recommendations

Consequent Antecedent Support

(%)

Confidence

(%)

Programme

= 0.2

PGPA = 0.1 and TI = 0.4 and G =

0.1

25.02 100

Programme

= 0.5


0.1

12.02 100

Programme

= 0.3


0.2

11.04 100

Programme

= 0.3

PGPA = 0.1 and G = 0.1 15.23 80.95

Programme

= 0.1


0.1

25.08 71.43

Programme

= 0.7

PGPA = 0.2 and TI = 0.7 15.18 68.75

Programme

= 0.6

PGPA = 0.2 and TI = 0.7 25.03 66.67

Programme

= 0.3

PGPA = 0.1 and TI = 0.1 15.09 62.5

Programme

= 0.2


0.2

17.96 61.65

Programme

= 0.3

TI = 0.4 and G = 0.1 27.18 58.67

Programme

= 0.5


0.2

15.08 57.14

Programme

= 0.2

PGPA = 0.3 and TI = 0.4 18.46 53.7

57

Programme

= 0.4


0.2

8.61 52.73

Programme

= 0.3


0.1

15.52 48.94

Programme

= 0.7


0.1

17.56 46.52

Programme

= 0.4

PGPA = 0.1 and TI = 0.2 15.72 44.62

Programme

= 0.4

PGPA = 0.5 and TI = 0.1 15.16 42.86

Programme

= 0.2


0.2

29.00 40.98

Programme

= 0.3

PGPA = 0.2 and TI = 0.6 15.06 40

The results in Table 4.4 illustrate output from extraction of the programme

recommendation. The details include ‘programme’, which refers to one of the seven

programmes of study (major), ‘G’ refers to gender, ‘PGPA’ refers to one of the five

ranges of previous GPA and ‘TI’ refers to one of the seven choices of talents and

interests.

Similarly, example results from rule extraction of the activity recommendation are

shown in Table 4.5

Table 4.5: Example results of rules extraction by GRI for ranked activity

recommendation

Consequent Antecedent Support

(%)

Confidence

(%)

Activity = 0.4 TI = 0.5 29.78 100

Activity = 0.1 TI = 0.3 28.23 100

Activity = 0.3 Programme = 0.4 and TI = 0.2 24.18 100

Activity = 0.5 Programme = 0.7 and TI = 0.1 14.11 90.28

Activity = 0.4 PGPA = 0.2 and TI = 0.5 12.46 89.27

58

Activity = 0.2 Programme = 0.2 and PGPA = 0.5

and TI = 0.6

15.02 89.2


and TI = 0.2

16.07 69.4


and TI = 0.5

19.03 68.6


and TI = 0.7

20.01 55


and TI = 0.1

13.00 54.2

Activity = 0.1 PGPA = 0.5 36.63 50.7

Activity = 0.1 Programme = 0.7 and PGPA = 0.5 18.78 50.4

Activity = 0.4 Programme = 0.7 and PGPA = 0.1 13.44 42.5

Activity = 0.1 Programme = 0.3 15.31 40.03

‘Activity’ provides recommendations based on one of the five activities, ‘programme’

refers to one of the seven programmes of study (major), ‘PGPA’ refers to one of the five

ranges of previous GPA and ‘TI’ refers to talents and interests.

After the rule extraction process was executed, 201 rules were generated for the

programme recommendation and 238 rules for the activity recommendation. The rules

were then divided into three rankings according to the confidence levels 80–100 per

cent, 60–79 per cent and 40–59 per cent, respectively. The distribution of the rules in

each ranking is displayed in Figure 4.6.

59

Figure 4.4: Distribution of the rules in each ranking

This figure shows that the number of rules in each ranking is not equal. Particularly, the

number of rules for the activity recommendation in each ranking is quite different,

which may affect the accuracy of the prediction results.

To evaluate the results, 20 per cent of the student data was used to test the accuracy of

the rules. The results from the test, in terms of ranked programmes and activities, are

presented in Table 4.6 and 4.7 and in Figures 4.5 and 4.6.

Table 4.6: A comparison of the accuracy between the ranked programme and


Rule 1st ranking

(%)

2nd ranking

(%)

3rd ranking

(%)

Average

GRI programme

recommendation

67.642 70.056 70.950 69.549

GRI activity

recommendation

76.648 64.413 65.307 68.790

60

Figure 4.5: Comparison of the accuracy between ranked programme and activity

recommendations

Table 4.7: Comparison of mean absolute error between ranked programme and


Rule 1st ranking 2nd ranking 3rd ranking Average

GRI programme

recommendation

0.070 0.065 0.064 0.066

GRI activity

recommendation

0.051 0.078 0.074 0.068

Figure 4.6: Comparison of mean absolute error between ranked programme and


61

The comparison in Figure 4.5 and 4.6 shows that the ranked programme

recommendation average slightly outperformed the activity recommendation average.

The accuracy of the results from programme recommendation by GRI in each ranking is

similar, whereas the accuracy of the first-ranked activity recommendation by GRI is

significantly better than the other two. It can be observed that the number of rules for

the first-ranked activity recommendation in Figure 4.5 is also higher; this correlates

with the higher accuracy of the result and, subsequently, provides a better first-ranking

result.

4.6.2 Example results of ranked programme and activity recommendations based

on GRI and K-means clustering

Table 4.8: Comparison of accuracies between ranked programme and activity

recommendations

Rule 1st ranking

(%)

2nd ranking

(%)

3rd

ranking

(%)

Average

(%)

GRI programme

recommendation

67.642 70.056 70.950 69.549

GRI-clustered

programme

recommendation

73.631 72.682 73.464 73.259

GRI activity

recommendation

76.648 64.413 65.307 68.790

GRI-clustered

activity

recommendation

78.492 71.899 69.218 73.203

62

Figure 4.7: Comparison of accuracies between ranked programme and activity

recommendations

Table 4.9: Comparison of mean absolute errors between ranked programme and


Rule 1st ranking 2nd ranking 3rd

ranking

Average

GRI programme

recommendation

0.070 0.065 0.064 0.066

GRI-clustered

programme

recommendation

0.057 0.061 0.057 0.058

GRI activity

recommendation

0.051 0.078 0.074 0.068

GRI-clustered activity

recommendation

0.046 0.059 0.065 0.057

63

Figure 4.8: Comparison of mean absolute errors between ranked programme and


Table 4.8 and 4.9 and Figure 4.7 and 4.8 show that the proposed techniques using K-

means clustering and GRI, in terms of the GRI-clustered programme recommendation,

obtained more accuracy than using GRI alone, in terms of the GRI programme

recommendation. In addition, the results of each ranking are similar in both accuracy

and MAE. Considering the activity recommendation results, the GRI-clustered

recommendation also obtained more accuracy than GRI techniques alone. However, the

first-ranking results, in both accuracy and MAE, obtained higher performance than the

second and third ranking.

4.7 Conclusion and Discussion

With the availability of historical student records, educational institutes could make use

of such resources and data-mining techniques to support SRM. In this study, a model

for the recommendation of ranked programmes is proposed to provide three ranked

programmes, as well as three ranked activities, to the students and counsellors. The use

of clustered data could assist to improve the accuracy of the results. In both modules

(ranked programme recommendation and activity recommendation), it was found that

ARs based on GRI with the incorporation of two sets of clustered data by K-means

64

clustering outperformed the results from the ARs technique based on GRI with

uncluttered data.

Chapter 5 discusses GPA predictions for undergraduate students and for those who are

likely to be successful in postgraduate study.

65

Chapter 5: Grade Point Average Prediction and Postgraduate

Identification

5.1 Introduction

Chapter 4 illustrated the proposed ranked programme and activity recommendation

model and treated it as a multiclass-classification problem. In addition to this, several

methods for the identification of students’ academic performance and capabilities were

proposed to assist supervisors and counsellors. In this chapter, research on three

proposed modules of the intelligent recommendation system HCAF is described. These

modules are ‘likely overall GPA for prospective and new students’, ‘likely GPA for

each year from Year 1 through to Year 4’ and ‘identification of potential students to

continue with postgraduate study’. In this study, the following techniques were applied:

ANN, DT based on CHAID algorithms and SVM. The ensemble method based on the

confidence-weighted voting method and MANNs-OWSR were also used to enhance the

performance of the models. In this experiment, the prediction performance accuracy and

MAE were used to test and compare the results of each model. A statistical probability

table with a comparison of the accuracy rates was used to demonstrate the accuracies of

the prediction results.

This chapter is separated into various sections. Section 5.2 presents the objectives of the

chapter and Section 5.3 presents the input and output variables selection, including an

explanation of the datasets. A discussion of the techniques used and the experiment

design followed in Section 5.4 and 5.5, respectively. Section 5.6 presents the

experiment’s results, which is followed by a discussion and conclusion in Section 5.7.

The final section explains the contributions of the techniques of the chapter.

5.2 Objectives


1. develop and apply techniques and methodologies based on classification

techniques using past cases from the student database to predict the likely GPA

66

results of prospective, new and current students. The aim is to assist supervisors

and counsellors to advise prospective and enrolled students

2. predict the likely results from postgraduate study to identify potential students

who may continue with postgraduate study

3. improve the performance of the recommendation model by using combination

techniques, that is, the ensemble and MANN-OWSR methods

4. propose the techniques to be used in the model and choose the best model with

the highest accuracy for use in the intelligent recommendation system HCAF.


The data source used in the experiment was the same as that used in the previous

chapter. In this chapter, two sets of data were organised during the pre-processing data

stage. In the two likely GPA modules, the datasets were the same as those used in the

previous chapter. Therefore, the variables selection was explained in Section 4.3. The

postgraduate study identification module comprised of 918 student records after the data

cleaning process. Details are illustrated in Figure 5.1.

Figure 5.1: Number of postgraduate students in each postgraduate programme

(2001–2009)

Figure 5.1 shows that the dataset of 918 postgraduate student records from nine

academic years (2001–2009, excluding summer semesters) is made up of 38 per cent of

67

students from the Master of Education in Educational Administration, 36 per cent from

the Master of Business Administration, 16 per cent from the Master of Education in

Curriculum and Instruction and 10 per cent from the Graduate Diploma in Teaching

Profession. Section 3.4 provides details in terms of choosing the variables for this

experiment and pre-processing the data. The variable names and data types for this

experiment are shown in Table 5.1.

Table 5.1: Variable names and data types in each module

No.

Module 1:

likely overall GPA

for new students

Module 3:

likely GPA

for students in each

year

Module 6:

identification of

potential students to

continue with

postgraduate study

Variable

name

Type Variable name Type Variable name Type

1 Overall GPA Target GPA next

semester

Target Master degree

success

Target

2 Previous

school GPA

Input GPA (every

previous semester,

except summer)

Input University

GPA (overall

GPA)

Input

3 Previous

major

Input Previous school

GPA

Input Postgraduate

major

Input

4 Type of

school

Input Previous major Input University

major

Input

5 Number of

previous

awards

Input Type of school Input University

awards

Input

6 Talents and

interests

Input Number of

previous awards

Input Type of

university

Input

7 Motivation

channels

Input Talents and

interests

Input Previous school

GPA

Input

8 Admission

round

Input Motivation

channels

Input Type of school Input

68

9 Guardian

occupation

Input Admission

round

Input Motivation

channels

Input

10 Gender Input Guardian

occupation

Input Guardian

occupation

Input

11 University

major

Input Gender Input Activity type Input

12 University

major

Input Gender Input

The selection of appropriate input feature variables is essential for classifiers. As

explained in the previous chapter, previous school GPA, interests and gender are

associated with the ability of students to study at the tertiary level [99–101, 128].

Therefore, the main variables chosen in Module 1 and 3 were previous school GPA,

talents and interests and gender. However, other parameters may be useful in data

analysis by data mining, and additional supportive variables used in this experiment are

shown in Table 5.2. The module for identifying students who are likely to succeed in

postgraduate study used similar variables to the other modules in this chapter. As

choosing an appropriate activity could also improve student performance at university

[102], this variable was also used in Module 6. The next section discusses the intelligent

techniques used.

5.4 Intelligent Techniques

The first classification technique chosen for the GPA recommendation model was

SVM. Many research reports have shown that SVM is capable of providing successful

outcomes from classification tasks [46, 75, 129]. The second technique used in this

framework is ANN, which is a data-driven, self-adaptive method and is also a

successful and popular technique in classification [5, 15, 34, 59, 130, 131]. The third

classification technique is the DT algorithm, which has been used in various studies

[108, 132, 133], as well as in many educational data-mining studies. In this study, the

DT algorithm based on CHAID was used [134, 135]. Ramaswami and Bhaskaran [110]

reported that the results from the CHAID algorithms were satisfactory and the CHAID

algorithms could also be used to analyse both binary and categorical data. As many

69

feature variables in this study were categorical data, the DT based on CHAID algorithm

was deemed an appropriate technique for the intelligent recommendation system HCAF.

In general, combined classification models can improve the prediction performance of

the classifiers [90, 93]. In this study, two main aggregation techniques were employed.

One was the ensemble method based on confidence-weighted voting. One study showed

that ensemble is able to reduce prediction errors; however; it depends on the model

variance of the classifiers [93]. The other aggregation technique used in the study was

the MANN-OWSR, which is an efficient aggregation technique introduced and reported

by Kajornrit [2]. This technique has shown an acceptable improvement accuracy, and it

can be used to combine two classification models [2]. This suits the methodological

design in this study and these techniques are described in the following section.

70

5.5 Experimental Methodology and Design

SVM

Student Historic Data

ANN CHAID

Input

1st Comparison

Model with the Best Accuracy

Second Best Accurate Model Least Accurate Model

2nd Comparison

Ensemble

MANN-OWSR Model with Higher Accuracy

3rd Comparison

GPA Recommendation Model

Model with Highest Accuracy

Figure 5.2: Process for determining the best GPA recommendation model

Figure 5.2 illustrates the process that determines the best GPA recommendation model

for use in this chapter. While the three techniques (ANN, SVM and CHAID) have been

used extensively in the past, it is recognised that the ensemble and MANN-OWSR

methods have the potential to improve accuracy. Hence, it is necessary to determine

whether the single or combined model should be used. The process adopted in this study

is described below.

71

First, the ANN, DT based on CHAID algorithms and SVM were used. The results from

these three models were then compared in the first result comparison.

Second, the two models that returned the lowest performance accuracy were combined

using the ensemble approach based on the confidence-weighted voting method. Then,

the result from the ensemble model was compared with the results from the three

models. The two models that gave the best results were chosen for the next process.

Next, the two models with the highest accuracy from the previous comparison were

aggregated using MANN-OWSR. Then, the model (SVM, ANN or CHAID) with the

best result was compared with the results from the ensemble and MANN-OWSR

models. The one that returned the best performance accuracy and the lowest accuracy

error rate was then chosen to predict the overall GPA for prospective, new and current

students.

This technique was also applied to determine the best model for the prediction of results

from postgraduate study to identify potential students to continue with postgraduate

study. After the model was determined, it could be used by counsellors and supervisors

to provide recommendations for the students.

The outputs from the likely overall GPA and GPA for each semester module were

categorised into six GPA classes; for example, A is likely to get a GPA of 0.3, which is

between 2.254 and 2.720 (see Table 3.2). Outputs from the postgraduate identification

module were provided in five postgraduate GPA categories. For example, if B is a

senior student and the result shows that B is likely to obtain a postgraduate GPA of 0.3,

this refers to the GPA range 3.4–3.59. Examples of the results are given in Table 5.2

and 5.3.

72

Table 5.2: Example results for likely overall GPA and likely GPA in each semester

Student no. Likely GPA Remarks

A001 2.254–2.720 Performance of this student needs

to be monitored and counselling

should be provided, if needed

Table 5.3: Example results for postgraduate identification

Student no. GPA class Remarks

B009 3.4–3.59 This student is likely to be

successful in postgraduate study

with good results

5.6 Experiment Results

This section provides example results from the GPA recommendation model. Three

modules were developed: likely overall GPA (4Y), likely GPA of each semester (Y1S1

to Y4S2) and postgraduate identification (PG). To determine the best model, the

experiment was trained, validated and tested three times to ensure consistency of

results. The comparison results shown in this experiment are the average accuracy rate

and MAE from each technique used in the model and the statistical probability of

occurrence of the compared techniques. As described previously, SVM, ANN and

CHAID are used in the first process.

5.6.1 First comparison between SVM, ANN and CHAID

The comparison results are illustrated in Table 5.4 and 5.5 and in Figures 5.3 and 5.4.

Table 5.4: Accuracy rate from the first process

Technique Average accuracy rate of each module (%)

4Y Y1S1 Y1S2 Y2S1 Y2S2 Y3S1 Y3S2 Y4S1 Y4S2 PG

SVM 97.29 93.63 98.20 98.90 99.94 99.83 99.68 99.83 99.78 83.37

ANN 87.48 57.50 65.09 68.02 71.77 67.81 74.86 67.81 73.17 70.48

CHAID 85.89 54.25 41.20 64.68 69.59 65.29 70.64 65.29 69.07 72.08

73

Figure 5.3: Accuracy rate of the classification techniques

Table 5.5: Comparison of MAE from the first process

Technique MAE of each module


SVM 0.003 0.008 0.002 0.00 0.001 0.000 0.000 0.001 0.000 0.017

ANN 0.013 0.049 0.038 0.036 0.031 0.036 0.028 0.035 0.030 0.031

CHAID 0.014 0.053 0.042 0.040 0.033 0.039 0.033 0.040 0.035 0.025

Figure 5.4: Comparison of MAE from the first process

In Table 5.4 and Figure 5.3, the accuracy rate between ANN and CHAID are similar:

ANN performed slightly better in the likely overall GPA and GPA each semester

module, but CHAID performed better in the postgraduate identification module.

However, SVM performed considerably higher than ANN and CHAID in overall GPA

and GPA each semester, while performing a little higher in postgraduate identification.

74

To consider the MAE results in Table 5.5 and Figure 5.4, the comparison showed that

the trend is similar to the accuracy rate.

The comparison results of the first process demonstrated that SVM outperformed the

ANN and CHAID techniques for all modules. In likely overall GPA and GPA each

semester, the second accuracy model was ANN, followed by CHAID as the lowest

accuracy model. Conversely, in postgraduate identification, the second and third

accuracy models were CHAID and ANN, respectively.

Therefore, the SVM-based model could be used to predict students’ GPA results with

the greatest degree of accuracy in the first process. As SVM demonstrated the highest

accuracy, it was considered in the second result comparison. However, as ANN and

CHAID ranked second and third in accuracy for likely overall GPA and GPA each

semester, they were combined by ensemble in the next process, while CHAID and

ANN, which were racked second and third in accuracy for postgraduate identification,

were combined by ensemble in the next process for that module.

5.6.2 Second comparison of the ANN, CHAID and ensemble models

To improve the two lowest performing models, CHAID and ANN were combined by

ensemble. The comparison results are shown in Table 5.6 and 5.7 and in Figure 5.5 and

5.6.

Table 5.6: Comparison of the accuracy rate between ANN, CHAID and ensemble

Technique Average accuracy rate of each module (%)

CGPA Y1S1 Y1S2 Y2S1 Y2S2 Y3S1 Y3S2 Y4S1 Y4S2 PG

ANN 87.48 57.50 65.087 68.02 71.77 67.81 74.86 68.96 73.17 70.48

CHAID 85.89 54.25 61.828 64.68 69.59 65.29 70.64 64.26 69.08 72.09

Ensemble 87.74 58.34 65.628 68.89 72.68 68.49 75.71 69.88 73.94 72.04

75

Figure 5.5: Comparison of the accuracy rate between ANN, CHAID and ensemble

Table 5.7: Comparison of MAE between ANN, CHAID and ensemble

Technique

MAE of combination of weak techniques with ensemble


ANN 0.013 0.049 0.038 0.036 0.031 0.036 0.028 0.035 0.030 0.031

CHAID 0.014 0.053 0.042 0.040 0.033 0.039 0.033 0.040 0.035 0.028

Ensemble 0.012 0.049 0.038 0.035 0.029 0.036 0.027 0.034 0.029 0.028

Figure 5.6: Comparison of MAE between ANN, CHAID and ensemble

In the likely overall GPA and GPA of each semester module, the comparison results

above show that the ensemble of the ANN and CHAID models slightly outperformed

the individual ANN model, which was the second highest accurate model in the first

process. In addition, the results of the average accuracy and MAE presented a similar

trend: likely overall GPA (4Y) scored the lowest MAE and highest accuracy, whereas

likely GPA in the first semester of Year 1 scored the highest MAE and lowest accuracy.

Having considered most cases, the results of the ensemble model returned higher

76

performance accuracy than the individually trained models; however, the ensemble

method generated relatively small improvement in performance accuracy.

In the postgraduate identification module, the results of the ensemble of the CHAID and

ANN models showed slightly lower performance than the individual CHAID model.

Further, having considered the above graphs and tables, the results indicated that the

average performance of the combined ANN and CHAID models by ensemble

outperformed the individual ANN and CHAID models in the likely overall GPA and

GPA of each year module. Therefore, in the next step, ensemble was chosen to combine

with the SVM model, which was the highest accuracy model from the first process,

using MANN-OWSR. Conversely, in postgraduate identification, CHAID showed

higher performance than ensemble; therefore, CHAID was chosen to combine with

SVM, also using MANN-OWSR, in the next process.

5.6.3 Third comparison using MANN-OWSR, SVM and ensemble in overall GPA

and GPA of each semester

In these two modules, ensemble was combined with SVM in the aggregation techniques

using MANN-OWSR. The comparison results are shown in Table 5.8 and 5.9 and in

Figures 5.7 and 5.8.


ensemble

Technique Average accuracy of each module (%)

CGPA Y1S1 Y1S2 Y2S1 Y2S2 Y3S1 Y3S2 Y4S1 Y4S2

SVM 97.29 93.63 98.20 98.90 99.40 99.83 99.68 99.49 99.78

MANN-OWSR 97.37 92.95 98.09 99.29 99.30 99.80 99.72 99.43 99.82

Ensemble 87.74 58.34 65.63 68.89 72.68 68.49 75.71 69.88 73.94

77


ensemble

Table 5.9: Comparison of MAE between MANN-OWSR, SVM and ensemble

Technique MAE of each module

CGPA Y1S1 Y1S2 Y2S1 Y2S2 Y3S1 Y3S2 Y4S1 Y4S2

SVM 0.012 0.049 0.038 0.035 0.029 0.036 0.027 0.034 0.029

OWSR 0.003 0.008 0.002 0.001 0.001 0.000 0.000 0.000 0.000

Ensemble 0.003 0.008 0.002 0.001 0.001 0.000 0.000 0.001 0.000

Figure 5.8: Comparison of MAE between MANN-OWSR, SVM and ensemble

The tables and figures above show that MANN-OWSR provided better accuracy and

less prediction errors than ensemble but returned slightly better accuracy and less

prediction error than SVM. The MAE comparison results also showed similar trends

78

between SVM and OWSR. Considering the accuracy results together with MAE, the

average performance of MANN-OWSR outperformed the individual SVM and

ensemble models in these two modules. In the next section, the third comparison results

of the postgraduate identification module are demonstrated.

5.6.4 Third comparison of MANN-OWSR, SVM and CHAID in the postgraduate

identification module

In this module, the CHAID and SVM models were combined using MANN-OWSR.

The comparison results are shown in Table 5.10 and 5.11 and in Figure 5.9 and 5.10.


CHAID in the postgraduate identification module

Technique Average accuracy of each module (%)

SVM 83.37

OWSR 83.09

CHAID 72.09


CHAID

79

Table 5.11: Comparison of MAE between MANN-OWSR, SVM and CHAID

Technique MAE

SVM 0.028

OWSR 0.017

CHAID 0.017

Figure 5.10: Comparison of MAE between MANN-OWSR, SVM and CHAID

In the postgraduate identification module, the results showed that MANN-OWSR and

SVM returned similar accuracy and both models returned higher accuracy than CHAID.

The MAE comparison results showed similar trends to the accuracy results: MANN-

OWSR and SVM returned the same results at 0.017 and CHAID had more errors than

the first two models at 0.011. Even though the results of SVM and MANN-OWSR were

similar, the average performance of SVM outperformed MANN-OWSR and CHAID.

The results of the SVM model can be used to predict the best results of the postgraduate

identification module model with the best degree of accuracy.


This chapter proposed a process to develop the GPA recommendation model, which

forms the three modules in the intelligent recommendation system HCAF. The first two

modules focused on predicting the likely overall GPA for prospective and new students

and the likely GPA for students in each academic year. The postgraduate identification

80

module focused on final year students who were likely to be successful in postgraduate

study, and the result from this module could be used to support the scholarship

committee and university administrator to estimate the number of potential students to

carry on with postgraduate study.

This chapter also showed that the SVM model outperformed the ANN and CHAID

models in the first process. The finding also indicated that the best recommendation

model for the likely overall GPA and GPA in each semester module was the MANN-

OWSR model. Conversely, the best model for the postgraduate identification module

was the SVM model.

This chapter demonstrated the use of intelligent techniques to determine the best model

for predicting students’ GPA and identifying their potential to continue with

postgraduate study. However, it is noted that datasets from other universities may

exhibit different characteristics and the best model to be used may not be the same as in

this study. The proposed model and process in Figure 5.4 provided an innovative

approach to determining the best model for the prediction.

The next chapter discusses the identification of dropouts so that appropriate remedial

actions can be initiated by the university to improve the retention rate.

81

Chapter 6: Dropout Identification

6.1 Introduction

In the previous chapter, the SVM results and aggregation technique, MANN-OWSR,

were found to provide the best prediction accuracy from the dataset used in this study.

This chapter focuses on the identification of students who are likely to drop out, and this

forms one of the modules in the intelligent recommendation system HCAF (see Chapter

3). Most of the techniques employed in this proposed model were used in previous

models and include K-means clustering, ANN, DT based on the CHAID algorithm,

SVM and two aggregation techniques: ensemble and MANN-OWSR. However, unlike

previous applications that were multiclass problems, this chapter focuses on the issue,

which is, by nature, a binary classification problem.

The process of the proposed techniques is explained in this chapter, which is separated

into various sections. The next section presents the objectives of the chapter. Section 6.3

discusses the input and output variables selection, including an explanation of the

dataset. The experiment design and results are explained in Sections 6.4 and 6.5,

respectively. The final section provides a conclusion relating to the work in this chapter.

6.2 Objectives


1. identify possible student dropouts during the programme of study based on past

cases from the student database. The proposed dropout identification process

applied the techniques and methodologies in the intelligent recommendation

system to identify students who are likely to drop out before graduation

2. determine the most appropriate techniques to be used from the results in this

chapter. Clustering techniques, ensemble and MANN-OWSR were used to

analyse the data and to improve the performance accuracy. The proposed

techniques and the best model with the highest accuracy were chosen to use in

the intelligent recommendation system.

82


In this experiment, the variables were chosen from the university’s database of 11,400

student records, which was composed of the 9,001 student records used in Chapter 4

and 5 and the 2,399 student dropout records. The distribution of the 11,400 student

records is illustrated in Figure 6.1 below.

Figure 6.1: Number of undergraduate students, including dropouts, by

programme of study (2001–2007)

In Figure 6.1, the dataset was obtained from undergraduate records in six academic

years (2001–2007, excluding summer semesters). The student data were composed of

30.62 per cent of students from business computing, 19.02 per cent from accounting,

22.18 per cent from management, 14.75 per cent from marketing, 5.2 per cent from

human resource management, 4.84 per cent from business English and 3.38 per cent

from law.

As explained in Chapter 3, the variables used in the process were suggested by the

university lecturers and counsellors. However, some variables could not be included in

the experiment because of insufficient data. Reference was made to previous studies

relating to factors that influenced student dropout rates in tertiary education. Yu et al.

[62] declared that demographics, such as gender, are related to dropout rates. They also

83

found that high school academic performance could be another significant input

variable among dropout students [62]. Braxton et al. [136] stated that parental

encouragement is an important factor for student retention and Sittichai [137] found that

parental guidance and career were related to student dropout rates. In this experiment,

numerical data, such as overall GPA from previous schools and overall GPA at

university, were transformed into categorical classes (see Chapter 3). The data variables

used in these three modules are illustrated in Table 6.1.

Table 6.1: Name and type of input and output data

No.

Module 5:

programme completion identification

Variable name Type

1 Dropout identification Target

2 Previous school GPA Input

3 Previous major Input

4 Number of previous awards Input

5 Talents and interests Input

6 Motivation channels Input

7 Admission round Input

8 Guardian occupation Input

9 Gender Input

10 University major Input

11 Overall GPA (or GPA before dropout) Input

6.4 Experimental Methodology and Design

The techniques used in this study were described in Chapters 4 and 5. The processes for

determining the best student dropout identification model are composed of three

classification techniques, a clustering technique and two aggregation techniques (see

Figure 6.2).

84

Input

2nd Comparison

K-Means

Cluster 1

SVM ANN CHAID

Cluster 2

SVM ANN CHAID

Model of Cluster with Second Best

Accuracy

Models of Cluster 1 with the

best Accuracy

3rd Comparison

Model of Cluster 1 with Least Accuracy

Model of Cluster 2 with Second Best

Accuracy

Models of Cluster 2 with the

best Accuracy

Model of Cluster 2 with Least Accuracy

Ensemble(2)

Ensemble(1)

MANN-OWSR

Student Dropout Identification Model

4th Comparison

RankingRanking

SVM ANN CHAID

1st Comparision

Classification Model with the Best

Accuracy

Second Best Accurate Model

Least Accurate Model

Ranking

5th ComparisonModel with Higher Accuracy

Final Comparison Model with Higher Accuracy

Model with Highest Accuracy

Figure 6.2: Process for determining the student dropout identification model

Figure 6.2 shows the process for determining the best student dropout identification

model from a dataset of student records. This process concentrates on comparing the

performance of different models or a combination of models to determine the best result

for the intelligent recommendation system.

In the first stage, three classification techniques (SVM, CHAID and ANN) were trained,

validated and tested three times. Next, the results of these three techniques were

85

compared and ranked in the first comparison and the model with the best accuracy was

compared in the final comparison.

To improve the performance of the model, the K-means clustering technique was used

to divide the dataset into two groups of related data. Each cluster was applied to the

three basic techniques (SVM, ANN and CHAID) again in a process similar to the first

stage. The results from the three basic models in each cluster were then compared in the

second and third comparisons (see Figure 6.2). The models that gave the highest and

second highest accuracy in each cluster were combined using the ensemble technique as

Ensemble 1 and Ensemble 2 outputs. These two results were then compared in the

fourth comparison. In addition, MANN-OWSR was used to aggregate the best result

from each cluster. This was then compared with the best ensemble result from the fourth

comparison. The fifth comparison compared the cluster models that gave the highest

accuracy in the fourth comparison. After testing the data, the results of the MANN-

OWSR accuracy were compared using ensemble, which gave the highest accuracy from

the fourth comparison. The results were shown in the fifth comparison.

In the final comparison, the result from the model with the highest accuracy in the first

comparison was then compared with the output from the fifth comparison. The model

that gave the best result was then chosen to determine student dropouts in the intelligent


6.5 Experimental Results

As in the process in Chapter 5, the data were used to train, validate and test each

technique three times to ensure consistency. Therefore, the results shown in this section

are the average results.

6.5.1 First comparison of classification techniques ANN, CHAID and SVM

The comparison results are shown in Table 6.2. Figure 6.3 presents the accuracy of the

recommendation system using the three classification techniques.

86

Table 6.2: First comparison of classification technique accuracy

Technique Accuracy

1st round (%) 2nd round (%) 3rd round (%) Average

ANN 92.60 93.17 93.35 93.04

CHAID 89.63 89.68 89.92 89.74

SVM 93.74 93.60 93.70 93.68

Figure 6.3: Comparison of the accuracy between classification techniques

The comparison results show that the SVM model outperformed CHAID but only

outperformed ANN by 0.64 per cent. Therefore, the SVM-based model could be

considered able to predict student dropout identification with the best accuracy in the

first process. In the second process, K-means clustering was employed to separate data

into two groups, which is described below.

6.5.2 Results from K-means clustering

This experiment used two clusters because the survey results showed that dropouts

could be categorised into two main groups: low level GPA and personal reasons.

Table 6.3: Number of clusters and iterations by K-means clustering

Input data Cluster Iterations

11,400 2 13

87

Figure 6.4: Number of data in each cluster from K-means clustering

The data in Cluster 1 and 2 consisted of 4,608 and 6,792 records, respectively. These

two clustered datasets were used to train and validate the ANN, CHAID and SVM

models in the next stage.

6.5.3 Comparing results from three models using data from Cluster 1: second

comparison

The results from the second comparison are shown in Table 6.4 and Figure 6.5 below.

Table 6.4: Comparison of results based on data from Cluster 1

Technique

Accuracy

1st round 2nd round (%) 3rd round

(%)

Average

ANN 96.28 96.17 96.06 96.17

CHAID 96.17 94.63 95.40 95.40

SVM 96.50 95.84 96.28 96.20

88

Figure 6.5: Comparison of accuracy based on dataset from Cluster 1

In Table 6.4 and Figure 6.5, the results of the first cluster indicated that each of the three

models returned higher performance accuracy than the classification techniques did. In

particular, CHAID was the most accurate with a 5.66 per cent improvement. In this test

data, the accuracy rankings were SVM, ANN and CHAID, respectively. The next

section discusses the data and results from the second cluster.

6.5.4 Comparison of results based on data from Cluster 2

The second cluster results are demonstrated in Table 6.5 and Figure 6.6.

Table 6.5: Comparison of results from the second cluster

Technique Accuracy


ANN 93.51 93.76 92.17 93.15

CHAID 88.14 88.52 88.52 88.39

SVM 93.74 94.11 94.11 93.98

91.0092.0093.0094.0095.0096.0097.0098.0099.00

100.00

1st Accuracy (%) 2nd Accuracy (%) 3rd Accuracy (%) Average

ANN

CHAID

SVM

89

Figure 6.6: Comparison of accuracy based on data from the second cluster

According to the above table and figure, the results based on data from the second

cluster illustrated that SVM gave the highest accuracy in comparison to the other two

techniques. However, SVM gained only a marginally difference against ANN, with a

0.83 per cent improvement. In this process, accuracy rankings were SVM, ANN and

CHAID, respectively.

Having considered the comparisons of the first and second clusters, it is found that the

second cluster performed lower than the first cluster. The results of the three techniques

in the second cluster are similar to the results in the first comparison, and the results

from the first cluster were better. The next process will use the comparison results based

on data from the two clusters.

In this process, the model that gave the second best accuracy was combined as

Ensemble 2. The ANN model gave the second best accuracy from both clusters. Models

with the best accuracy were also combined as Ensemble 1, which is described in the

next process. SVM gave the best results based on data from both clusters. The CHAID

model gave the lowest accuracy and, therefore, is not used in the subsequent ensemble

models.

90

6.5.5 Fourth comparison between Ensemble 1 and Ensemble 2

Results of the comparison are given in Table 6.6 and Figure 6.7.

Table 6.6: Results of comparison between Ensemble 1 and 2

Technique Accuracy


Ensemble 2 83.097 90.328 80.302 84.58

Ensemble 1 96.495 96.495 96.673 96.55

Figure 6.7: Comparison of accuracy of SVM and ANN ensembles

Ensemble 1, which was a combination of the SVM techniques, provided a higher

accuracy of 96.55 per cent. This means that it outperformed Ensemble 2, which was a

combination of the ANN techniques and provided an accuracy of 84.58 per cent.

As illustrated in Figure 6.2, the techniques that gave the highest accuracy in the second

and third comparisons were chosen to form the MANN-OWSR model. This means that

the techniques for MANN-OWSR were effectively the same as the ones used for

Ensemble 1. The results from the fourth comparison between the two ensemble outputs

were then compared with the results from the MANN-OWSR model. This comparison

is described in the next section.

91

6.5.6 Fifth comparison between MANN-OWSR and the best ensemble

Table 6.7 and Figure 6.8 demonstrate the comparison results from the fifth comparison

between the results from the best ensemble and MANN-OWSR.

Table 6.7: Accuracies from the best ensemble and MANN-OWSR

Technique

Accuracy

1st round

(%)

2nd round

(%)

3rd round (%) Average

MANN-OWSR 95.92 95.87 95.92 95.90

Best ensemble 96.50 96.50 96.67 96.55

Figure 6.8: Comparison of accuracy of ensemble and MANN-OWSR

From the classification results, the average ensemble accuracy was slightly higher than

MANN-OWSR with 0.65 per cent. The SVM cluster ensemble showed slightly better

results than MANN-OWSR consistently during the three tests. As the improvement is

quite small, it could be considered that both approaches could be used for this dataset.

A comparison between the highest performing classification techniques and the

proposed techniques is illustrated next.

92

Table 6.8: Comparison of SVM cluster ensemble and single SVM model

Technique Accuracy


SVM model 93.74 93.60 93.70 93.68

Ensemble of

SVMs

96.50 96.50 96.67 96.55

Figure 6.9: Accuracy of ensemble in comparison to the single SVM model

The above table and figure show that the accuracy of the SVM cluster ensemble was

compared with the results from the single SVM model in the first process. The results

indicated that the ensemble returned a better classification accuracy of 96.55 per cent,

while the single SVM model from the classification techniques returned an average

accuracy of 93.68 per cent. Therefore, for the dataset used in this study, the ensemble of

the combined SVM models outperformed the single SVM model for the purpose of

classification. Therefore, this model is chosen as the module for dropout identification

in the proposed intelligent recommendation system.


In terms of providing technology to support SRM, the proposed dropout identification

model could be used in the intelligent recommendation system. In this study, the

proposed model was tested with historical data to assist counsellors and supervisors at

93

university. This model will be useful for identifying students who are likely to drop out

before graduation, allowing remedial action to be initiated.

This chapter demonstrated how to establish the integrated model to determine the most

appropriate techniques to use. The experiments were compared in detail with single

classification techniques and ensemble approaches for the intelligent recommendation

system. The objective was to choose the best technique, which would provide better

services to enable counsellors and supervisors to assist students. The next chapter

provides a discussion on future work and a conclusion for this thesis.

94

Chapter 7: Conclusion and Future Work

7.1 Introduction

This thesis proposed an intelligent recommendation system with the aim to support

SRM and to address issues related to the provision of programme advice and

counselling for university students in Thailand. The research focused on the

development and implementation of the processes within the proposed framework and

demonstrated how the modules could be used, based on a set of over 9,000 sample

student records provided by a typical university in Thailand. The following sections

summarise the findings from the previous chapters.

7.2 Summary of Findings

As the goals of SRM are to recruit and retain students, improve student services, reduce

costs and improve productivity [138], outcomes from this research could be considered

a useful reference for university management in retaining students, improving student

services, increasing student and staff productivity and supporting SRM. The following

sections provide a discussion on lessons learnt from the experiments.

7.2.1 Programme and activity recommendations

In Chapter 4, the AR technique was first proposed and employed to find three ranked

outputs from the programme recommendation and activity recommendation modules in

the proposed intelligent recommendation system HCAF. From this study, it was found

that the rules extraction performed by the ARs based on the GRI algorithm gave an

accuracy of approximately 69.55 per cent and 68.79 per cent for the ranked programme

and activity recommendations, respectively.

To improve the performance, it was decided that the dataset should be refined by using

the clustering technique to group student records that had similar features. Therefore,

the K-means clustering approach was used to divide the dataset into two sets of 5,804

95

and 3,197 records. The experiment was repeated with the GRI being applied to each

dataset. It was observed that the accuracy increased to 73.26 per cent and 73.20 per cent

using the clustered data for the ranked programme and activity recommendation,

respectively.

It is observed that the GRI approach can be used to improve results with clustered data.

While it may be argued that the accuracy obtained from the proposed technique is not

necessarily in the 80–90 per cent region, this could be due to historical data being

subjected to subjective advice and student interests changing during their programme of

study. Therefore, it is difficult to achieve an exceptionally high accuracy. Secondly, the

number of clusters was limited to two after discussion with the university counsellors. It

is possible to increase the number of clusters; however, this could lead to a reduced

number in some of the clusters, rendering the results uncertain.

Therefore, Chapter 4 demonstrated that the use of GRI and clustering is a feasible

technique for providing ranked programme and activity recommendations for students

and to assist counsellors and supervisors.

7.2.2 Grade point average prediction and postgraduate identification

The issues of GPA prediction and postgraduate student identification were different in

nature when compared to the previous module. The data were continuous values,

whereas the previous problem dealt with multiclass classification using the AR

technique. In Chapter 5, three modules (prediction of likely overall GPA for new

students, prediction of likely overall GPA for students in each year and postgraduate

student identification) formed the other essential modules in the framework. These

modules are based on three classification models: ANN, DT based on CHAID

algorithms and SVM.

Based on the dataset and experiments in this study, MANN-OWSR gave the best result

for the likely overall GPA and GPA in each semester modules with an accuracy of

approximately 98 per cent in both cases. Conversely, SVM gave the best accuracy for

the postgraduate identification module with an accuracy of 83.3 per cent. The results

showed that the MANN-OWSR approach worked well with this dataset. Given another

96

dataset from another university, the best approach may be different. However, the

proposed framework will allow different approaches to be experimented on to

recommend the best approach for the particular university. For postgraduate

identification, while the accuracy did not appear to be as high as GPA prediction, this

could be due to students not being able to continue with postgraduate study because of

financial or other reasons. In addition, some students worked for a while before

returning to postgraduate study; hence, their academic records have been discontinued.

Nevertheless, over 80 per cent accuracy is a good indication that appropriate advice

could be given to encourage students who are contemplating further study.

7.2.3 Dropout identification: programme completion identification and dropout

identification modules

Student dropout is an important issue for universities for various reasons. For example,

it will lead to loss of revenue for the university and have potential employment

implications for the staff. In Chapter 6, this study attempted to identify students who

were likely to drop out by using 11,400 student records from seven years. The chapter

proposed and developed an integrated model for the intelligent recommendation system.

The nature of this problem is essentially a binary classification problem in proposing an

identification of potential dropouts.

The technique used in this module is effectively a combination of the modules used in

the previous chapters. The module incorporated the clustering approach, as used in

Chapter 4, and the three classification techniques, plus the two aggregation techniques

from Chapter 5.

The intention of this work was to propose and develop a framework to determine an

accurate approach for identifying dropouts. Based on the experiments, the SVM

ensemble based on clustered data gave the best result of 96.6 per cent, which was an

improvement on the single techniques. For the dataset based on student records from

other universities, the proposed framework could be applied to determine student

dropouts. In addition, similar to Chapter 4, it is possible to increase the number of

clusters should more data records be made available.

97

The proposed methodology and processes in the three chapters show that the model has

returned consistent results. Therefore, it is suggested that the proposed recommendation

system will provide an effective and efficient service for university management,

programme counsellors, academic staff and students and support SRM strategies of the

university.

7.3 Discussion on Future Work

This study could be considered for future research in various directions. It is agreed that

academic records and student backgrounds are only some of the factors that determine a

student’s success in the programme of study. Many other reasons could affect a

student’s motivation, progress and ability to succeed. These factors could be internal or

external. Hence, to improve the current proposed recommendation system, it should be

able to incorporate more indicators, such as career options and feedback from successful

graduates. The next generation recommendation system should also provide more

options in various types of delivery modes and educational pathways to assist students

and management. The features used in this study were based on supervisor and

counsellor opinions that could be subjective and limited. A wider survey could be

conducted to determine better use of the information from the student records. Other

factors that were missed in this study include results from psychological assessment of

new and enrolled students. However, this will require expertise from other appropriate

disciplines and expansion of the student records.

This research study explored the use of several computational intelligent techniques,

which were deemed the best choice at the time of the study. With the development of

other techniques, such as fuzzy sets, rough sets and a range of other optimisation

techniques, different techniques could be incorporated in the next recommendation

system.

Finally, the performance of the recommendation system should be monitored by

comparing performance of the results from the students in subsequent years against

recommendations by the counsellors. This will be necessary to validate the usefulness

98

of the system and to improve the performance through ongoing development of the

system.

7.4 Conclusion

This thesis provided a study of the proposal and development of a framework to

determine the appropriate techniques for the recommendations of ranked programme

preferences and activities, the likely overall GPA, likely GPA each semester,

identification of postgraduate students and dropouts based on historical student records.

Up to 11,000 student records were used in the experiments to demonstrate the proposed

processes and techniques.

The results indicated that the proposed framework determined the best approach for

providing high performance results. The recommendation could be used by supervisors,

counsellors and academic staff to advise students on the choice of programme and

activity from various student clubs. By knowing the likely future GPA scores,

supervisors, counsellors and lecturers can monitor students who are likely to need

remedial assistance or who may dropout before graduation. Students who have the

potential for postgraduate study could also be encouraged and management could invest

appropriate resources to assist such students.

It is recognised that student cohorts at other universities will differ. However, the

modules and associated techniques in the proposed intelligent recommendation system

are universal and could be adopted for other datasets or features. Therefore, it is

concluded that the proposal will be a useful tool to assist university management, staff

and students alike and will help to improve student performance at Thai universities.

99

References

[1] P. Grey and J. Byun, 'Customer relationship management', Center for Research

on Information Technology Organizations, University of Califonia, Irvine, CA,

2001. Available: http://www.escholarship.org/uc/item/76n7d23r#page-52

[2] J. Kajornrit, K.W. Wong and C.C. Fung, 'Estimation of missing precipitation

records using modular artificial neural networks', in 19th International

Conference on Neural Information Processing (ICONIP2012), pp. 52–59.

[3] A. Novoa et al., 'Estudo sobre abandono', Reitoria da Universidade de Lisboa,

2006.

[4] B. Andrews and J.M. Wilding, 'The relation of depression and anxiety to life-

stress and achievement in students', British Journal of Psychology, vol. 95, pp.

509–521, 2004.

[5] A.L. Caison, 'Determinates of systemic retention: Implications for improving

retention practice in higher education', Journal of College Student Retention:

Research, Theory & Practice, vol. 6, no. 4, pp. 425–441, 2004–2005.

[6] P.A. Helland, H.J. Stallings and J.M. Braxton, 'The fulfillment of expectations

for college and student departure decisions', Journal of College Student

Retention, vol. 3, pp. 381–396, 2001–2002.

[7] K. Jusoff, S.A. Abu Samah and P. Mohd Isa, 'Promoting university community's

creative citizenry', International Journal of Human and Social Sciences, vol. 4,

no. 1, pp. 25–30, 2009.

[8] T. Gatfield, 'A scale of marketing for higher education', Journal of Marketing for

Higher Education, vol. 10, pp. 27–41, 2000.

[9] J.J. Archer and S. Cooper, 'Counselling and mental health services on campus',

in A Handbook of Contemporary Practices and Challenges, San Francisco, CA:

Jossy-Bass, 1998.

[10] U. Uruta and A. Takano, 'Between psychology and college of education',

Journal of Educational Psychology, vol. 51, pp. 205–217, 2003.

[11] D.T. Gamage, J. Suwanabroma, T. Ueyama, S. Hada and E. Sekikawa, 'The

impact of quality assurance measures on student services at the Japanese and

100

Thai private universities', Quality Assurance in Education, vol. 16, pp. 181–198,

2008.

[12] K. Harej and R.V. Horvat, 'Customer relationship management momentum for

business improvement', Information Technology Interfaces (ITI), pp. 107–111,

2004.

[13] C. Jie and W. Mingzan, 'The data excavation model in CRM based on fuzzy

rule', in 2nd IEEE conference (ICIEA2007), pp. 742–745.

[14] A. Hibert, K. Schönbrunn and S. Schmode, 'Student relationship managment in

Germany-foundation and opportunities', Management Revue, vol. 18, pp. 204–

219, 2007.

[15] M.B. Piedade and M.Y. Santos, 'Student relationship management: Concept,

practice and technological support', IEEE Xplore, pp. 2–5, 2008.

[16] Office of the Higher Education Commission. (2008). Higher Education

Institutions recognized by Office of the Higher Education Commission [Online].

Available: http://inter.mua.go.th/main2/index.php

[17] D.T. Gamage, J. Suwanabroma, T. Ueyama, S. Hada and E. Sekikawa, 'The

impact of quality assurance measures on student services at the Japanese and

Thai private universities', Quality Assurance in Education, vol. 16, pp. 181–198,

2008.

[18] K.W. Wong, C.C. Fung and T.D. Gedeon, 'Data mining using neural fuzzy for

student relationship management', in International Conference of Soft

Computing and Intelligent Systems, Tsukuba, Japan, 2002.

[19] The World Bank. (2012, Apr.). Thailand [Online]. Available:

http://www.worldbank.org/en/country/thailand

[20] Theodora. (2012, Apr.). Thailand People 2012 [Online]. Available:

http://www.theodora.com/wfbcurrent/thailand/thailand_people.html

[21] W. Sangnapaboworn, 'Higher education reform in Thailand: Towards quality

improvement and university autonomy', in The Shizuoka Forum on "Approaches

to Higher Education, Intellectual Creativity, Cultivation of Human Resources

Seen in Asian Countries", 2003, p. 18.

[22] P.M. Komolmas, 'New trends in higher education towards the 21st century in

Thailand', ABAC Journal, vol. 19, 1999.

[23] Office of the Higher Education Commission. History of Higher Education in

Thailand [Online]. Available: http://inter.mua.go.th/main2/page_detail.php?id=3

101

[24] P.M. Komolmas, 'Privatizing globalizing higher education in Thailand', AU

Journal, vol. 18, 1998.

[25] The World Bank. (2011, Apr.). Thailand: Country Summary of Higher

Education [Online]. Available:

http://siteresources.worldbank.org/EDUCATION/Resources/278200-‐

1121703274255/1439264-‐1193249163062/Thailand_CountrySummary.pdf

[26] C.W. Runckel. (2011, Mar.). Education in Thailand [Online]. Available:

http://www.business-‐in-‐asia.com/thailand/education_in_thailand.html

[27] Bureau of International Cooperation. (2008, May). Towards a Learning Society

in Thailand [Online]. Available:

http://www.bic.moe.go.th/fileadmin/BIC_Document/book/intro-‐ed08.pdf

[28] UNESCO. (1995–2011, Apr.). Education-Thailand [Online]. Available:

http://www.unesco.org/new/en/education/resources/unesco-‐portal-‐to-‐recognized-‐

higher-‐education-‐institutions/dynamic-‐single-‐view/news/thailand/

[29] Office of the Higher Education Commission. (2006, May). Total of Students

2006 Classified by Higher Education Institutes, Level of Education and Gender

[Online]. Available: http://www.mua.go.th/infodata/49/all2549.htm

[30] Y. Gao and C. Zhang, 'Research on customer relationship managment

application system of manufacturing enterprises', in Wireless Communications,

Networking and Mobile Computing 4th International Conference (Wicom'08),

Dalian, pp. 1–4.

[31] R. Ackerman and J. Schibrowsky, 'A business marketing strategy applied to

student retention: A higher education initiative', Journal of College Student

Retention, vol. 9, pp. 330–336, 2007–2008.

[32] P.C. Verhoef, 'Understanding the effect of customer relationship management

efforts on customer retention and customer share development', Journal of

Marketing, vol. 67, pp. 30–45, 2003.

[33] R.N. Bolton, P.K. Kannan and M.D. Bramlett, 'Implications of loyalty program

membership and service experiences for customer retention and value', Journal

of the Academy of Marketing Science, vol. 17, pp. 45–65, 2000.

[34] J. Hemsley-Brown and I. Oplatka, 'Universities in a competitive global

marketplace. A systematic review of the literature on higher education

marketing', Interantional Journal of Public Sector Management, vol. 69, pp.

316–338, 2006.

102

[35] K. Neville, C. Heavin and E. Walsh, 'A case in customizing e-learning', Journal

of Information Technology, vol. 20, pp. 117–129, 2005.

[36] E.D. Seeman and M. O'Hara, 'Customer relationship management in higher

education: Using information systems to improve the student-school

relationship', Campus-Wide Information Systems, vol. 23, pp. 24–34, 2006.

[37] Office of the Higher Education Commission. (2008, May). Thai Higher

Education: Policy & Issue [Online]. Available:

http://inter.mua.go.th/main2/files/file/Plicy&Issue/OHEC%20Policy&IssueThai%20Hig

her%20Education%20PolicyIssue.pdf

[38] K. Jusoff, S.A. Abu Samah and P. Mohd Isa, 'Promoting university community's

creative citizenry', International Journal of Human and Social Sciences, vol. 4,

no. 1, pp. 25–30, 2009.

[39] C. Vialardi, J. Bravo, L. Shafti and A. Ortigosa. (2009, Jul.). Recommendation

in higher education using data mining techniques. Presented at 2nd International

Conference on Educational Data Mining [Online]. Available:

http://www.educationaldatamining.org/EDM2009/uploads/proceedings/vialardi.pdf

[40] Office of the Education Council. A study of the background of grade twelve

affect students different academic achievements [Online]. Available:

http://thaiedresearch.org

[41] S. Subyam, 'Causes of dropout and program incompletion among undergraduate

students from the faculty of engineering, King Mongkut's University of

Technology North Bangkok', in The 8th National Conference on Engineering

Education, Le Meridien Chiang Mai, Muang, Chiang Mai, Thailand, 2009.

[42] N. Jantarasapt. The Relationship between the Study Behavior and Low Academic

Achievement of Students of Dhurakij Pundit University, Thailand [Online].

Available: http://www.dpu.ac.th/researchcenter/archive.php?act=view&id=73

[43] J.L. Herlocker, 'Understanding and improving automated collaborative filtering

systems', PhD thesis, Faculty of the Graduate School, The University of

Minnesota, 2000.

[44] U. Fayyad, G. Piatetsky-Shapiro and P. Smyth, 'From data mining to knowledge

discovery in databases', AI Magazine, vol. 17, no. 3, pp. 37–54, 1996.

[45] F.-M. Shyu and H.-Y. Liao, 'An application of SVM: Blog templates

recommendation system', in 2010 Sixth International Conference on Natural

Computation (ICNC 2010).

103

[46] B. Wu, L. Qi and X. Feng, 'Personalized recommendation algorithm based on

SVM', in International Conference on Communications, Circuits and Systems

(ICCCAS2007), pp. 951–953.

[47] T.-H. Kim, Y.-S. Ryu, S.-I. Park and S.-B. Yang, 'An improved recommendation

algorithm in collaborative filtering', Lecture Notes in Computer Science, vol.

2455, pp. 254–261, 2002.

[48] B. Awerbuch, B. Patt-Shamir, D. Peleg and M. Tuttle, 'Improved

recommendation systems', in Proceedings of the Sixteenth Annual ACM-SIAM

Symposium in Discrete Algorithms (SODA'05), 2005, pp. 1174–1183.

[49] S.E. Middleton, N.R. Shadbolt and D.C. De Roure, 'Ontological user profiling in

recommender systems', ACM Transactions on Information Systems, vol. 22, pp.

54–88, 2004.

[50] A. Demiriz, 'Enhancing product recommender systems on sparse binary data',

Data Mining and Knowledge Discovery, vol. 9, pp. 147–170, 2004.

[51] J. Luan, 'Data mining and its application in higher education', New Directions

for Institutional Research, vol. 2002, pp. 17–36, 2002.

[52] S.B. Kotsiantis and P.E. Pintelas, 'Predicting student's marks in Hellenic Open

University', in The Fifth IEEE International Conference on Advanced Learning

Technologies, 2005.

[53] A. Salazar, J. Gosalbez, I. Bosch, R. Miralles and L. Vergara, 'A case study of

knowledge discovery on academic achievement', in Information Technology:

Research and Education (ITRE2004).

[54] J.F. Superby et al., 'Determination of factors influencing the achievement of the

first-year university students using data mining methods', in the 8th

International Conference on Intelligent Tutoring Systems (ITS2006), Jhongli,

Taiwan.

[55] J.P. Vandamme, N. Meskens and J.F. Superby, 'Predicting academic

performance by data mining methods', Education Economics, vol. 15, pp. 405–

419, 2007.

[56] E.H. Thomas and N. Galambos, 'What satisfies students? Mining student-

opinion data with regression and decision tree analysis', Research in Higher

Education, vol. 45, pp. 251–269, 2004.

104

[57] Y. Ma, B. Liu, C.K. Wong, P.S. Yu and S.M. Lee, 'Targeting the right students

using data mining', in Proceedings of the Sixth ACM SIGKDD International

Conference on Knowledge Discovery and Data Mining, Boston, MA, 2000.

[58] G.D. Chen, C.C. Liu, K.L. Ou and B.J. Liu, 'Discovering decision knowledge

from Web log portfolio for managing classroom processes by applying decision

tree and data cube technology', Journal of Educational Computing Research,

vol. 23, pp. 305–332, 2000.

[59] K.H. Im, T.H. Kim, S. Bae and S.C. Park, 'Conceptual modeling with neural

network for giftedness identification and education', In Advance in Natural

Computation, vol. 3611, pp. 530–538, 2005.

[60] N. Schmitt, J. Keeney, F.L. Oswald, T.J. Pleskac, A.Q. Billington, R. Sinha and

M. Zorzie, 'Prediction of 4-year college student performance using cognitive and

noncognitive predictors and the impact on demographic status of admitted

students', Journal of Applied Psychology, vol. 94, pp. 1479–1497, 2009.

[61] G.W. Dekker et al., 'Predicting students drop out: A case study', in 2nd

International Conference on Educational Data Mining, Cordoba, Spain, 2009,

pp. 41–50.

[62] C.H. Yu et al., 'A data mining approach for identifying predictors of student

retention from sophomore to junior year', Journal of Data Science, vol. 8, pp.

307–325, 2010.

[63] S. Chabaa et al., 'Identification and prediction of Internet traffic using artificial

neural networks', Journal of Intelligent Learning Systems and Applications, vol.

2010, pp. 147–155, 2010.

[64] M. Nakano et al., 'True smile recognition system using neural networks', in 9th

International Conference on Neural Information Processing (ICONIP'02),

Singapore, 2002, pp. 650–654.

[65] K.W. Wong et al., 'Intelligent data mining and personalisation for customer

relationship management', in 8th International Conference on Control,

Automation, Robotics and Vision, Kunming, China, 2004, pp. 1796–1801.

[66] R. Kala et al., 'Fuzzy neuro systems for machine learning for large data sets', in

2009 IEEE International Advance Computing Conference (IACC2009), Patiala,

India.

[67] K.-L. Du and M.N.S. Swamy, Neural Networks in a Softcomputing Framework,

vol. 1, Germany: Springer, 2006.

105

[68] K.J. Cios et al., Data Mining: A Knowledge Discovery Approach, Germany:

Springer Science and Business Media, 2007.

[69] G.V. Kass, 'An exploratory technique for investigation large quantities of

categorical data', Applied Statistics, vol. 29, pp. 119–127, 1980.

[70] D.M. Hawkins and G.V. Kass, 'Automatic interaction detection', in Topics in

Applied Multivariate Analysis, D.M. Hawkins, Ed. Cambridge: Cambridge

University Press, 1982, pp. 269–302.

[71] K.-J. Kim, 'Customer need type classifcaiton model using data mining

techniques for recommender systems', World Academy of Science, Engineering

and Technology, vol. 80, pp. 279–284, 2011.

[72] V.N. Vapnik, The Nature of Statistical Learning Theory, Germany: Springer,

1999.

[73] V. Kecman, Studies in Fuzziness and Soft Computing, Germany: Springer, 2005.

[74] S.R. Gunn, 'Support vector machines for classification and regression',

University of Southampton, UK, 1998.

[75] Y. Bo and Q. Luo, 'Personalized Web information recommendation algorithm

based on support vector machine', in 2007 International Conference on

Intelligent Pervasive Computing, 2007.

[76] Z. Xu et al., 'Infinite hidden relational models', in The Twenty-Second

Conference on Uncertainty in Artificial Intelligence (UAI2006), Cambridge,

MA, 2012.

[77] R. Agrawal et al., 'Mining association rules between sets of items in large

databases', in ACM SIGMOD International Conference on Management of Data,

Washington, DC, 1993.

[78] A. Ceglar and J.F. Roddick, 'Association mining', ACM Computing Surveys, vol.

38, pp. 1–42, 2006.

[79] S.M. McNee et al., 'Being accurate is not enough: How accuracy metrics have

hurt recommendation systems', in CHI '06 Extended Abstracts on Human

Factors in Computing Systems, 2006, pp. 1097–1101.

[80] Y. Zhu et al., 'Initializing K-means clustering using affinity propagation', in

Ninth International Conference on Hybrid Intelligent Systems, 2009.

[81] Z. Wang et al., 'Clustering analysis of customer relationship in securities trade',

in Proceeding of Third International Conference on Machine Learning and

Cybernetics, Shanghai, 2004, pp. 1760–1762.

106

[82] S.K. Ng et al., 'Clustering replicated microarray data via mixtures of random

effects models for various covariance structures', in Conferences in Research

and Practice in Information Technology (CRPIT), 2006.

[83] M. Stone et al., 'Managing the change from marketing to customer relationship

management', Long Range Planning, vol. 29, pp. 675–683, 2006.

[84] S. Y. Kim et al., 'Customer segmentation and strategy development based on

customer lifetime value: A case study', Expert Systems with Applications, vol.

31, pp. 101–107, 2006.

[85] C.C.H. Chan, 'Intelligent value-based customer segmentation method for

campaign management: A case study of automobile retailer', Expert Systems

with Applications, vol. 34, pp. 2754–2762, 2008.

[86] M. Namvar et al., 'A two phase clustering method for intelligent customer

segmentation', in International Conference on Intelligent Systems, Modelling

and Simulation, 2010.

[87] X. Lai, 'Segmentation study on enterprise customers based on data mining

technology', in First International Workshop on Database Technology and

Applications, 2009.

[88] W. Wang and S. Fan, 'Application of data mining technique in customer

segmentation of shipping enterprises', in Database Technology and Applications

(DBTA), 2010.

[89] B.M. Sarwar et al., 'Recommender systems for large-scale e-commerce: Scalable

neighborhood formation using clustering', in Fifth International Conference on

Computer and Information Technology (ICCIT2002).

[90] M.-J. Kim and D.-K. Kang, 'Ensemble with neural networks for bankruptcy

prediction', Expert Systems with Applications, vol. 37, pp. 3373–3379, 2010.

[91] B. Baruque and E. Corchado, 'A weighted voting summarisation of SOM

ensembles', Data Mining Knowledge Discovery, vol. 21, pp. 398–426, 2010.

[92] J.R. Rico-Juan and J.M. Inesta, 'Confidence voting method ensemble applied to

off-line signature verification', Pattern Analysis Application, vol. 15, pp. 113–

120, 2012.

[93] Y. Frayman et al., 'Solving regression problems using competitive ensemble

models', in Proceedings of the 15th Australian Joint Conference on Artificial

Intelligence: Advances in Artificial Intelligence, 2002, pp. 511–522.

107

[94] H.J. Miller, 'Tobler's first law and spatial analysis', Annuals of the Association of

American Geographers, vol. 94, pp. 284–289, 2004.

[95] F.H.D. Olmo and E. Gaudioso, 'Evaluation of recommendation systems: A new

approach', Expert Systems with Applications, vol. 35, 2008.

[96] C.J. Willmott and K. Matsuura, 'Advantages of the mean absolute error (MAE)

over the root mean square error (RMSE) in assessing average model

performance', Climate Research, vol. 30, pp. 79–82, 2005.

[97] J. Kajornrit et al., 'Rainfall prediction in the northeast region of Thailand using

cooperative neuro-fuzzy technique', in 8th International Conference on

Computing and Information Technology, 2012, pp. 24–29.

[98] N.F. Guler et al., 'Recurrent neural networks employing Lyapunov exponents for

EEG signals classification', Expert Systems with Applications, vol. 29, pp. 506–

514, 2005.

[99] W. Wang et al., 'Determination of the spread parameter in the Gaussian Kernel

for classification and regression', Neurocomputing, vol. 55, pp. 643–663, 2003.

[100] V.N. Staroverov et al., 'Comparative assessment of a new nonempirical density

functional: Molecules and hydrogen-bonded complexes', Journal of Chemical

Physics, vol. 119, pp. 12,129–12,137, 2003.

[101] B. Sarwar et al., 'Item-based collaborative filtering recommendation algorithms',

in Proceedings of the 10th International Conference on World Wide Web, 2001,

pp. 285–295.

[102] S. Ahmad et al., 'RVP-Net: Online prediction of real valued accessible surface

area of proteins from single sequences', Bioinformatics Applications Note, vol.

19, pp. 1849–1851, 2003.

[103] J. Kajornrit, K.W. Wong and C.C. Fung, 'Estimation of Missing Precipitation

Records using Modular Artificial Neural Networks', in 19th International

Conference on Neural Information Processing (ICONIP2012), Doha, Qatar.

[104] K. Kongsakun and C.C. Fung, 'Neural network modeling for an intelligent

recommendation system supporting SRM for universities in Thailand', WSEAS

Transactions on Computers, vol. 11, 2012.

[105] K. McKenzie and R. Schweitzer, 'Who succeeds at university? Factors

predicting academic performance in first year Australian university students',

Higher Education Research and Development, vol. 20, pp. 21–33, 2001.

108

[106] L. Newman-Ford et al., 'An investigation in the effects of gender, prior academic

achievement, place of residence, age and attendance on first-year undergraduate

attainment', Journal of Applied Research in Higher Education, vol. 1, pp. 14–28,

2009.

[107] Office of the Education Council. A Study of the Background of Grade Twelve

Affect Students Different Academic Achievements [Online]. Available:

http://thaiedresearch.org

[108] S. Geiser and M.V. Santelices. 'Validity of high-school grades in predicting

student success beyond the freshman year: High-school record vs. standardized

test as indicators of four-year college outcomes', Center for Studies in Higher

Education, University of California, Berkeley, CA, 2007. Available:

http://cshe.berkeley.edu/publications/docs/ROPS.GEISER._SAT_6.12.07.pdf

[109] T.S. Hoover and A.H. Dunigan, 'Leadership characteristics and professional

development needs of collegiate student organizations', NACTA Journal, vol. 48,

2004.

[110] D.R. Liu and Y.Y. Shin, 'Integrating AHP and data mining for product

recommendation based on customer lifetime value', Information and

Management, vol. 42, pp. 387–400, 2005.

[111] X.Y. Xu et al., 'Construct concise and accurate classifier by atomic association

rules', in Third International Conference on Machine Learning and Cybernetics,

Shanghai, 2004.

[112] W. Paireekreng et al., 'A model for mobile content filtering on non-interactive

recommendation systems', in 2011 IEEE International Conference on Systems,

Man, and Cybernetics (IEEESMC2011), Alaska.

[113] A. Anand and D. Khots, 'A data mining framework for identifying claim

overpayments for the health insurance industry', in Third INFORMS Workshop

on Data Mining and Health Informatics (DM-HI2008).

[114] V. Aggelis and D. Christodoulakis, 'E-trans association rules for e-banking

transactions', in IV International Conference on Decision Support for

Telecommunications and Information, Warsaw, Poland, 2004.

[115] P.L. Hsu et al., 'The hybrid of association rule algorithms and genetic algorithms

for tree induction: An example of predicting the student course performance',

Expert Systems with Applications, vol. 25, pp. 51–62, 2003.

109

[116] A.M. Khattak et al., 'Analyzing association rule mining and clustering on sales

day data with XLMiner and Weka', International Journal of Database Theory

and Application, vol. 3, pp. 13–22, 2010.

[117] X.W. Liu and P.L. He, 'The research of improved association rules mining

Apriori algorithm', in International Conference on Machine Learning and

Cybernetics, 2004, pp. 1577–1579.

[118] C. Özseyhan et al., 'An association rule-based recommendation engine for an

online dating site', Communications of the IBIMA, vol. 2012, p. 15, 2012.

[119] M. Plasse, N. Niang, G. Saporta, A. Villeminot and L. Leblond, 'Combined use

of association rules mining and clustering methods to find relevant links between

binary rare attributes in a large dataset', Computational Statistics & Data

Analysis, vol. 52, pp. 596–613, 2007.

[120] N. Raheja and R. Kumar, 'Optimization of association rule learning in

distributed database using clustering technique', International Journal of

Scientific and Research Publication, vol. 2, p. 7, 2012.

[121] O.S. Soliman and A. Adly, 'Bio-inspired algorithm for classification association

rules', in 8th International Conference on INFOrmatics and Systems

(INFOS2012), Cairo University Conference Center, Giza, Egypt.

[122] J.K. Kim et al., 'A personalized recommendation prodedure for Internet

shopping support', Electronic Commerce Research and Applications, vol. 1, pp.

301–313, 2002.

[123] F.H. Wang and H.M. Shao, 'Effective personalized recommendation based on

time-framed navigation clustering and association mining', Expert Systems with

Applications, vol. 27, pp. 367–377, 2004.

[124] I.N. Kouris et al., 'Using information retrieval techniques for supporting data

mining', Data and Knowledge Engineering, vol. 52, pp. 353–383, 2005.

[125] O.S. Soliman and A. Adly, 'Using quantum-inspired immune system', in The

Annual Conference, ISSR, Cairo University, Egypt, 2011.

[126] A. Veloso and W. Meira, 'Self-training associative classification', in Demand-

Driven Associative Classification, London, UK: Springer, 2011.

[127] X. Zhu et al., 'A weighted voting-based associative classification algorithm', The

Computer Journal, vol. 53, pp. 786–801, 2010.

[128] N. Jantarasapt. 'The study of the relationship between learning behavior and low

academic achievement of Dhurakij Pundit University students' [Online].

110

[129] J.A. Xu and K. Araki. 'A SVM-based personal recommendation system for TV

programs', in 12th International Multi-Media Modelling Conference

Proceedings, 2006.

[130] J.J. Guo and P.B. Luh, 'Improving market clearing price prediction by using a

committee machine of neural networks', IEEE Transactions on Power Systems,

vol. 19, pp. 1867–1876, 2004.

[131] P. Kraipeerapun et al., 'Lithofacies classification from well log data using neural

networks and interval neutrosphic sets for multiclass classification problems',

WSEAS Transactions on Computers, vol. 6, pp. 463–470, 2007.

[132] Y.H. Cho et al., 'A personalized recommender system based on Web usage

mining and decision tree induction', Expert Systems with Applications, vol. 23,

pp. 329–342, 2002.

[133] Q.A. AI-Radaideh et al., 'Mining student data using decision trees', in

International Arab Conference on Information Technology (ACIT2006),

Yarmouk University, Jordan.

[134] Z.J. Koracic, 'Early prediction of student success: Mining student enrolment

data', in Informing Science & IT Education Conference (InSITE) 2010, pp. 647–

664.

[135] M. Ramaswami and R. Bhaskaran, 'A CHAID based performance prediction

model in educational data mining', International Journal of Computer Science

Issues, vol. 7, 2010.

[136] J.M. Braxton et al, Understanding and Reducing College Student Departure,

Jossey-Bass, 2004.

[137] R. Sittichai et al., 'Discontinuation among university students Songklanakarin',

Journal of Social Science and Humanities, vol. 14, p. 10, 2008.

[138] B. Chapman, 'The Australian university student financing system: The rationale

for, and experience with, income-contingent loans', Crawford School of

Government and Economics, Canberra, Australia, 2011.

111

Appendix

Example results of module 1: likely overall GPA

ANN, CHAID and SVM models

No 1st

Overall GPA

ANN CHAID SVM 2nd

Overall GPA

ANN CHAID SVM 3rd

Overall GPA

ANN CHAID SVM

1 0.4 0.3 0.4 0.4 0.3 0.4 0.4 0.3 0.4 0.4 0.4 0.4

2 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

3 0.5 0.4 0.4 0.5 0.4 0.4 0.4 0.4 0.3 0.4 0.4 0.3

4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

5 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

6 0.2 0.2 0.2 0.2 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.2

7 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

8 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

9 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

10 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

11 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

12 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.2 0.4 0.4 0.2

13 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

14 0.2 0.3 0.3 0.3 0.2 0.3 0.3 0.2 0.3 0.3 0.3 0.3

15 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

16 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

17 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

18 0.3 0.4 0.4 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

19 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

20 0.2 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

21 0.2 0.2 0.4 0.2 0.4 0.2 0.4 0.4 0.2 0.2 0.4 0.2

22 0.4 0.3 0.3 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

23 0.4 0.4 0.3 0.4 0.3 0.4 0.3 0.3 0.4 0.4 0.3 0.4

112

24 0.4 0.4 0.3 0.4 0.3 0.4 0.3 0.3 0.3 0.3 0.4 0.3

25 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.5 0.3 0.5

26 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

27 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

28 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

29 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

30 0.2 0.3 0.2 0.2 0.2 0.3 0.2 0.2 0.3 0.3 0.2 0.3

31 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

32 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

33 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

34 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.4 0.3 0.3 0.3 0.3

35 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

36 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

37 0.3 0.4 0.4 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

38 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

39 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

40 0.6 0.5 0.5 0.6 0.5 0.5 0.5 0.5 0.6 0.6 0.5 0.6

41 0.4 0.3 0.3 0.3 0.3 0.5 0.3 0.3 0.2 0.2 0.3 0.2

42 0.5 0.5 0.4 0.5 0.4 0.5 0.4 0.4 0.5 0.5 0.4 0.5

43 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.2 0.2 0.3 0.2

44 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

45 0.6 0.5 0.4 0.6 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

46 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

47 0.2 0.2 0.3 0.2 0.2 0.3 0.3 0.2 0.3 0.3 0.3 0.3

48 0.5 0.1 0.4 0.5 0.4 0.5 0.4 0.4 0.5 0.5 0.4 0.5

49 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.2 0.2 0.3 0.2

50 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

51 0.2 0.3 0.3 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

52 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

53 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.6 0.5 0.6

113

54 0.2 0.3 0.3 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

55 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.4

56 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

57 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

58 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

59 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

60 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

61 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

62 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

63 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

64 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

65 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

66 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

67 0.6 0.5 0.6 0.6 0.4 0.6 0.6 0.4 0.6 0.6 0.6 0.6

68 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

69 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

70 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

71 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

72 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

73 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

74 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

75 0.2 0.3 0.3 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

76 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

77 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

78 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

79 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

80 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

114

ANN, CHAID and ensemble models

No

1st Overall

GPA

Ens ANN CHD

2nd Overall

GPA

Ens ANN CHD

3rd Overall

GPA

Ens ANN CHD

1 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

2 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

3 0.5 0.3 0.4 0.4 0.5 0.4 0.4 0.4 0.5 0.4 0.4 0.4

4 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

7 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

8 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

9 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

10 0.1 0.2 0.2 0.2 0.1 0.2 0.2 0.2 0.1 0.2 0.2 0.2

11 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

12 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

13 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

14 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

15 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

16 0.2 0.2 0.2 0.4 0.2 0.2 0.2 0.4 0.2 0.2 0.2 0.4

17 0.3 0.3 0.3 0.2 0.3 0.2 0.3 0.2 0.3 0.2 0.3 0.2

18 0.2 0.2 0.3 0.2 0.2 0.2 0.3 0.2 0.2 0.2 0.3 0.2

19 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

20 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

21 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3

22 0.2 0.3 0.3 0.3 0.2 0.3 0.3 0.3 0.2 0.3 0.3 0.3

23 0.4 0.3 0.4 0.3 0.4 0.3 0.4 0.3 0.4 0.3 0.4 0.3

24 0.4 0.3 0.4 0.3 0.4 0.3 0.4 0.3 0.4 0.3 0.4 0.3

25 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

115

26 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

27 0.3 0.4 0.4 0.4 0.3 0.4 0.4 0.4 0.3 0.4 0.4 0.4

28 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

29 0.1 0.2 0.2 0.2 0.1 0.2 0.2 0.2 0.1 0.2 0.2 0.2

30 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

31 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3

32 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

33 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

34 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3

35 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

36 0.4 0.5 0.5 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.5 0.4

37 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3

38 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

39 0.3 0.4 0.4 0.4 0.3 0.4 0.4 0.4 0.3 0.4 0.4 0.4

40 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

41 0.5 0.5 0.5 0.4 0.5 0.4 0.5 0.4 0.5 0.4 0.5 0.4

42 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

43 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

44 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

45 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

46 0.5 0.4 0.5 0.4 0.5 0.4 0.5 0.4 0.5 0.4 0.5 0.4

47 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

48 0.3 0.4 0.4 0.4 0.3 0.4 0.4 0.4 0.3 0.4 0.4 0.4

49 0.5 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.5 0.4 0.4 0.4

50 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

* Please note that “Ens” is ensemble and “CHD” is CHAID algorithm

116

Results of the MANN-OWSR, SVM and ensemble in overall GPA and GPA each semester

No. 1st

Overall GPA

SVM OWSR Ensemble 2nd

Overall GPA

SVM OWSR Ensemble 3rd

Overall GPA

SVM OWSR Ensemble

1 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

2 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

3 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.5 0.5 0.5 0.4

4 0.2 0.2 0.2 0.2 0.5 0.5 0.5 0.5 0.2 0.2 0.2 0.2

5 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

6 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5

7 0.4 0.4 0.4 0.4 0.2 0.2 0.2 0.2 0.4 0.4 0.4 0.4

8 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

9 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

10 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.1 0.1 0.1 0.2

11 0.4 0.4 0.4 0.4 0.6 0.6 0.6 0.5 0.4 0.4 0.4 0.4

12 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

13 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

14 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

15 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

16 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2

17 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.3

18 0.5 0.5 0.5 0.5 0.2 0.2 0.2 0.3 0.2 0.2 0.2 0.2

19 0.2 0.2 0.2 0.2 0.5 0.5 0.5 0.5 0.2 0.2 0.2 0.2

20 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

21 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.3

22 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.3

23 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.4 0.4 0.4 0.3

24 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.4 0.4 0.4 0.4

25 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

117

26 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2

27 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4

28 0.4 0.4 0.4 0.4 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.3

29 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.1 0.1 0.1 0.2

30 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3

31 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.3

32 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

33 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2

34 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.4 0.4 0.4 0.3

35 0.6 0.6 0.6 0.3 0.2 0.2 0.2 0.3 0.2 0.2 0.2 0.2

36 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

37 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.4 0.4 0.4 0.3

38 0.2 0.2 0.2 0.2 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

39 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.4

40 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

41 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.5 0.5 0.5 0.4

42 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3

43 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.4 0.4 0.4 0.4

44 0.4 0.4 0.4 0.3 0.5 0.5 0.5 0.5 0.4 0.3 0.4 0.4

45 0.5 0.5 0.5 0.5 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

46 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5

47 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

48 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.4 0.4

49 0.5 0.5 0.5 0.5 0.2 0.2 0.2 0.2 0.5 0.5 0.5 0.4

50 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.5 0.5 0.5 0.5

118

Example results of module 2: ranked programme recommendation

Association Rules (GRI) models

No 1st

Ranking GRI

2nd

Ranking GRI

3rd

Ranking GRI

1 0.4 0.4 0.1 0.1 0.5 0.5

2 0.3 0.2 0.6 0.6 0.1 0.3

3 0.6 0.3 0.4 0.4 0.5 0.5

4 0.4 0.2 0.1 0.1 0.5 0.5

5 0.4 0.2 0.1 0.5 0.1 0.1

6 0.3 0.2 0.6 0.2 0.5 0.5

7 0.3 0.4 0.2 0.5 0.5 0.5

8 0.2 0.2 0.2 0.2 0.5 0.5

9 0.5 0.4 0.5 0.5 0.5 0.5

10 0.2 0.2 0.5 0.5 0.5 0.5

11 0.4 0.2 0.5 0.5 0.4 0.4

12 0.4 0.5 0.5 0.4 0.5 0.5

13 0.2 0.5 0.5 0.5 0.4 0.4

14 0.2 0.2 0.5 0.5 0.4 0.4

15 0.2 0.2 0.4 0.4 0.3 0.3

16 0.4 0.5 0.5 0.5 0.5 0.5

17 0.4 0.5 0.5 0.5 0.5 0.5

18 0.4 0.1 0.3 0.3 0.5 0.5

19 0.2 0.2 0.1 0.4 0.3 0.3

20 0.3 0.3 0.4 0.4 0.5 0.5

21 0.3 0.3 0.1 0.2 0.5 0.5

22 0.4 0.4 0.5 0.5 0.5 0.5

23 0.2 0.1 0.1 0.5 0.5 0.5

24 0.4 0.4 0.1 0.5 0.3 0.7

119

25 0.2 0.2 0.4 0.4 0.7 0.7

26 0.3 0.3 0.5 0.5 0.5 0.5

27 0.4 0.4 0.6 0.6 0.5 0.5

28 0.5 0.5 0.5 0.5 0.5 0.5

29 0.4 0.4 0.5 0.5 0.5 0.5

30 0.5 0.5 0.2 0.2 0.5 0.5

31 0.3 0.3 0.2 0.2 0.5 0.5

32 0.4 0.4 0.4 0.4 0.5 0.5

33 0.4 0.4 0.5 0.5 0.5 0.5

34 0.3 0.3 0.7 0.5 0.5 0.5

35 0.4 0.4 0.5 0.5 0.5 0.5

36 0.5 0.1 0.7 0.5 0.5 0.5

37 0.5 0.5 0.5 0.5 0.5 0.5

38 0.2 0.2 0.5 0.5 0.3 0.5

39 0.3 0.3 0.3 0.3 0.5 0.5

40 0.5 0.5 0.3 0.3 0.5 0.5

41 0.3 0.3 0.3 0.3 0.3 0.3

42 0.5 0.5 0.6 0.6 0.6 0.6

43 0.3 0.3 0.6 0.5 0.5 0.5

44 0.2 0.2 0.5 0.5 0.5 0.5

45 0.4 0.1 0.6 0.5 0.5 0.5

46 0.5 0.5 0.5 0.5 0.5 0.5

47 0.4 0.4 0.6 0.6 0.6 0.6

48 0.3 0.4 0.4 0.4 0.5 0.5

49 0.3 0.3 0.6 0.6 0.6 0.6

50 0.3 0.3 0.6 0.5 0.7 0.7

51 0.2 0.2 0.5 0.5 0.7 0.7

52 0.4 0.4 0.2 0.2 0.5 0.5

53 0.4 0.4 0.4 0.4 0.5 0.5

54 0.4 0.4 0.6 0.6 0.7 0.6

120

55 0.5 0.1 0.7 0.5 0.5 0.5

56 0.4 0.4 0.5 0.4 0.7 0.5

57 0.4 0.4 0.5 0.1 0.7 0.5

58 0.3 0.3 0.7 0.5 0.5 0.5

59 0.3 0.3 0.7 0.2 0.5 0.5

60 0.3 0.3 0.5 0.5 0.1 0.1

61 0.4 0.4 0.5 0.2 0.1 0.3

62 0.3 0.3 0.5 0.5 0.1 0.1

63 0.4 0.4 0.5 0.5 0.1 0.5

64 0.4 0.4 0.5 0.5 0.1 0.5

65 0.4 0.4 0.5 0.4 0.5 0.5

66 0.3 0.1 0.6 0.5 0.5 0.1

67 0.4 0.4 0.5 0.5 0.5 0.1

68 0.4 0.4 0.2 0.2 0.5 0.5

69 0.5 0.5 0.5 0.5 0.5 0.2

70 0.5 0.5 0.5 0.5 0.3 0.3

Example results of module 3: likely GPA for each semester

Semester 1 of year 1


No. 1st

GPA Y1S1

ANN CHD SVM 2nd

GPA Y1S1

ANN CHD SVM 3rd

GPA Y1S1

ANN CHD SVM

1 0.3 0.2 0.2 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

2 0.1 0.3 0.2 0.1 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

3 0.3 0.3 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

4 0.3 0.3 0.2 0.3 0.4 0.3 0.3 0.4 0.5 0.4 0.5 0.5

5 0.3 0.4 0.4 0.3 0.4 0.3 0.3 0.4 0.5 0.4 0.5 0.5

121

6 0.2 0.2 0.3 0.2 0.5 0.4 0.4 0.5 0.4 0.3 0.4 0.4

7 0.3 0.4 0.3 0.3 0.3 0.4 0.4 0.3 0.2 0.3 0.3 0.2

8 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.2 0.3 0.3 0.2

9 0.3 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

10 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

11 0.4 0.4 0.3 0.4 0.2 0.3 0.2 0.2 0.4 0.4 0.4 0.4

12 0.5 0.4 0.3 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

13 0.3 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3

14 0.5 0.5 0.4 0.5 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.4

15 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

16 0.3 0.4 0.3 0.3 0.4 0.3 0.3 0.4 0.5 0.4 0.4 0.5

17 0.2 0.3 0.2 0.2 0.4 0.3 0.3 0.4 0.3 0.4 0.4 0.3

18 0.2 0.3 0.3 0.2 0.3 0.3 0.3 0.3 0.5 0.4 0.4 0.5

19 0.4 0.5 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

20 0.2 0.3 0.3 0.2 0.5 0.5 0.4 0.5 0.4 0.4 0.4 0.4

21 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.2 0.3 0.2 0.2

22 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3

23 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.2 0.3 0.3 0.2

24 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.4

25 0.4 0.5 0.5 0.4 0.4 0.5 0.4 0.4 0.3 0.3 0.3 0.3

26 0.4 0.3 0.3 0.4 0.3 0.4 0.4 0.3 0.3 0.3 0.3 0.3

27 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.4 0.3 0.3 0.2 0.3

28 0.5 0.5 0.5 0.5 0.4 0.5 0.4 0.4 0.3 0.2 0.2 0.3

29 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3

30 0.4 0.4 0.4 0.4 0.2 0.3 0.2 0.2 0.3 0.3 0.3 0.3

31 0.3 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.2 0.3

32 0.4 0.3 0.3 0.4 0.4 0.3 0.3 0.4 0.3 0.3 0.3 0.3

33 0.2 0.3 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.4

34 0.4 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.4 0.4 0.4

35 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.4 0.3 0.5

122

36 0.4 0.3 0.3 0.4 0.3 0.3 0.2 0.3 0.4 0.3 0.3 0.4

37 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.5

38 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.2 0.3 0.3 0.2

39 0.4 0.5 0.3 0.4 0.2 0.2 0.2 0.2 0.4 0.4 0.5 0.4

40 0.5 0.5 0.4 0.5 0.3 0.2 0.2 0.3 0.2 0.3 0.3 0.2

41 0.3 0.3 0.3 0.3 0.5 0.3 0.3 0.5 0.2 0.3 0.2 0.2

42 0.4 0.5 0.5 0.4 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.4

43 0.3 0.4 0.4 0.3 0.4 0.3 0.4 0.4 0.2 0.3 0.3 0.2

44 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.5 0.4 0.4 0.5

45 0.2 0.3 0.2 0.2 0.5 0.5 0.4 0.5 0.3 0.3 0.3 0.3

46 0.4 0.4 0.3 0.4 0.5 0.5 0.3 0.5 0.4 0.3 0.3 0.4

47 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

48 0.3 0.3 0.3 0.3 0.4 0.5 0.4 0.4 0.3 0.4 0.4 0.3

49 0.4 0.3 0.3 0.4 0.4 0.3 0.3 0.4 0.4 0.3 0.3 0.4

50 0.2 0.2 0.3 0.2 0.2 0.2 0.3 0.2 0.5 0.4 0.4 0.5


No.

1st

GPA

Y1S1

ANN CHD Ens 2nd

GPA Y1S1

ANN CHD Ens

3rd

GPA

Y1S1

ANN CHD Ens

1 0.3 0.2 0.2 0.2 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

2 0.1 0.3 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

3 0.3 0.3 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

4 0.3 0.3 0.2 0.3 0.4 0.3 0.3 0.3 0.5 0.4 0.5 0.5

5 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.5 0.4 0.5 0.5

6 0.2 0.2 0.3 0.2 0.5 0.4 0.4 0.4 0.4 0.3 0.4 0.4

7 0.3 0.4 0.3 0.3 0.3 0.4 0.4 0.4 0.2 0.3 0.3 0.3

8 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.2 0.3 0.3 0.3

123

9 0.3 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

10 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

11 0.4 0.4 0.3 0.3 0.2 0.3 0.2 0.2 0.4 0.4 0.4 0.4

12 0.5 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

13 0.3 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3

14 0.5 0.5 0.4 0.5 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3

15 0.4 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

16 0.3 0.4 0.3 0.3 0.4 0.3 0.3 0.3 0.5 0.4 0.4 0.4

17 0.2 0.3 0.2 0.2 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4

18 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.4 0.4 0.4

19 0.4 0.5 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

20 0.2 0.3 0.3 0.3 0.5 0.5 0.4 0.5 0.4 0.4 0.4 0.4

21 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.2 0.3 0.2 0.2

22 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3

23 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.2 0.3 0.3 0.3

24 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.4 0.4 0.4 0.4

25 0.4 0.5 0.5 0.5 0.4 0.5 0.4 0.4 0.3 0.3 0.3 0.3

26 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.3 0.3 0.3 0.3

27 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.2 0.2

28 0.5 0.5 0.5 0.5 0.4 0.5 0.4 0.4 0.3 0.2 0.2 0.2

29 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3

30 0.4 0.4 0.4 0.4 0.2 0.3 0.2 0.2 0.3 0.3 0.3 0.3

31 0.3 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.2 0.2

32 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3

33 0.2 0.3 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.3

34 0.4 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.4 0.4 0.4

35 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.4 0.3 0.3

36 0.4 0.3 0.3 0.3 0.3 0.3 0.2 0.3 0.4 0.3 0.3 0.3

37 0.4 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4

38 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.2 0.3 0.3 0.3

124

39 0.4 0.5 0.3 0.3 0.2 0.2 0.2 0.2 0.4 0.4 0.5 0.5

40 0.5 0.5 0.4 0.5 0.3 0.2 0.2 0.2 0.2 0.3 0.3 0.3

41 0.3 0.3 0.3 0.3 0.5 0.3 0.3 0.3 0.2 0.3 0.2 0.2

42 0.4 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.3

43 0.3 0.4 0.4 0.4 0.4 0.3 0.4 0.4 0.2 0.3 0.3 0.3

44 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.5 0.4 0.4 0.4

45 0.2 0.3 0.2 0.2 0.5 0.5 0.4 0.5 0.3 0.3 0.3 0.3

46 0.4 0.4 0.3 0.4 0.5 0.5 0.3 0.5 0.4 0.3 0.3 0.3

47 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

48 0.3 0.3 0.3 0.3 0.4 0.5 0.4 0.4 0.3 0.4 0.4 0.4

49 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3

50 0.2 0.2 0.3 0.2 0.2 0.2 0.3 0.2 0.5 0.4 0.4 0.4

SVM, MANN-OWSR and ensemble models

No.

1st

GPA

Y1S1

SVM OWSR Ens 2nd

GPA Y1S1

SVM OWSR Ens

3rd

GPA

Y1S1

SVM OWSR Ens

1 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.3 0.2 0.2 0.2 0.3

2 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

4 0.5 0.5 0.5 0.5 0.4 0.3 0.3 0.3 0.5 0.4 0.5 0.5

5 0.5 0.5 0.5 0.5 0.4 0.3 0.3 0.3 0.5 0.4 0.5 0.5

6 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.4 0.3 0.4 0.4

7 0.2 0.2 0.2 0.3 0.3 0.4 0.4 0.4 0.2 0.3 0.3 0.3

8 0.2 0.2 0.2 0.3 0.4 0.4 0.4 0.4 0.2 0.3 0.3 0.3

9 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

10 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

11 0.4 0.4 0.4 0.4 0.2 0.3 0.2 0.2 0.4 0.4 0.4 0.4

125

12 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

13 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3

14 0.4 0.4 0.4 0.3 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3

15 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

16 0.5 0.5 0.5 0.4 0.4 0.3 0.3 0.3 0.5 0.4 0.4 0.4

17 0.3 0.3 0.3 0.4 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4

18 0.5 0.5 0.5 0.4 0.3 0.3 0.3 0.3 0.5 0.4 0.4 0.4

19 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

20 0.4 0.4 0.4 0.4 0.5 0.5 0.4 0.5 0.4 0.4 0.4 0.4

21 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.2 0.3 0.2 0.2

22 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3

23 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.3 0.2 0.3 0.3 0.3

24 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.4 0.4 0.4 0.4

25 0.3 0.3 0.3 0.3 0.4 0.5 0.4 0.4 0.3 0.3 0.3 0.3

26 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.3 0.3 0.3 0.3

27 0.3 0.3 0.3 0.2 0.4 0.3 0.3 0.3 0.3 0.3 0.2 0.2

28 0.3 0.3 0.3 0.2 0.4 0.5 0.4 0.4 0.3 0.2 0.2 0.2

29 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3

30 0.3 0.3 0.3 0.3 0.2 0.3 0.2 0.2 0.3 0.3 0.3 0.3

31 0.3 0.3 0.3 0.2 0.4 0.4 0.4 0.4 0.3 0.3 0.2 0.2

32 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3

33 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.3

34 0.4 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.4 0.4 0.4

35 0.5 0.5 0.5 0.3 0.4 0.4 0.4 0.4 0.5 0.4 0.3 0.3

36 0.4 0.4 0.4 0.3 0.3 0.3 0.2 0.3 0.4 0.3 0.3 0.3

37 0.5 0.5 0.5 0.4 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4

38 0.2 0.2 0.2 0.3 0.5 0.5 0.5 0.5 0.2 0.3 0.3 0.3

39 0.4 0.4 0.4 0.5 0.2 0.2 0.2 0.2 0.4 0.4 0.5 0.5

40 0.2 0.2 0.2 0.3 0.3 0.2 0.2 0.2 0.2 0.3 0.3 0.3

41 0.2 0.2 0.2 0.2 0.5 0.3 0.3 0.3 0.2 0.3 0.2 0.2

126

42 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.3

43 0.2 0.2 0.2 0.3 0.4 0.3 0.4 0.4 0.2 0.3 0.3 0.3

44 0.5 0.5 0.5 0.4 0.3 0.3 0.3 0.3 0.5 0.4 0.4 0.4

45 0.3 0.3 0.3 0.3 0.5 0.5 0.4 0.5 0.3 0.3 0.3 0.3

46 0.4 0.4 0.4 0.3 0.5 0.5 0.3 0.5 0.4 0.3 0.3 0.3

47 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

48 0.3 0.3 0.3 0.4 0.4 0.5 0.4 0.4 0.3 0.4 0.4 0.4

49 0.4 0.4 0.4 0.3 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3

50 0.5 0.5 0.5 0.4 0.2 0.2 0.3 0.2 0.5 0.4 0.4 0.4



No. 1st

GPA Y1S2

ANN CHD SVM 2nd

GPA Y1S2

ANN CHD SVM 3rd

GPA Y1S2

ANN CHD SVM

1 0.3 0.2 0.2 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

2 0.1 0.3 0.2 0.1 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

3 0.3 0.3 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

4 0.3 0.3 0.2 0.3 0.4 0.3 0.3 0.4 0.5 0.4 0.5 0.5

5 0.3 0.4 0.4 0.3 0.4 0.3 0.3 0.4 0.5 0.4 0.5 0.5

6 0.2 0.2 0.3 0.2 0.5 0.4 0.4 0.5 0.4 0.3 0.4 0.4

7 0.3 0.4 0.3 0.3 0.3 0.4 0.4 0.3 0.2 0.3 0.3 0.2

8 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.2 0.3 0.3 0.2

9 0.3 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

10 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

11 0.4 0.4 0.3 0.4 0.2 0.3 0.2 0.2 0.4 0.4 0.4 0.4

12 0.5 0.4 0.3 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

13 0.3 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3

14 0.5 0.5 0.4 0.5 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.4

127

15 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

16 0.3 0.4 0.3 0.3 0.4 0.3 0.3 0.4 0.5 0.4 0.4 0.5

17 0.2 0.3 0.2 0.2 0.4 0.3 0.3 0.4 0.3 0.4 0.4 0.3

18 0.2 0.3 0.3 0.2 0.3 0.3 0.3 0.3 0.5 0.4 0.4 0.5

19 0.4 0.5 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

20 0.2 0.3 0.3 0.2 0.5 0.5 0.4 0.5 0.4 0.4 0.4 0.4

21 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.2 0.3 0.2 0.2

22 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3

23 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.2 0.3 0.3 0.2

24 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.4

25 0.4 0.5 0.5 0.4 0.4 0.5 0.4 0.4 0.3 0.3 0.3 0.3

26 0.4 0.3 0.3 0.4 0.3 0.4 0.4 0.3 0.3 0.3 0.3 0.3

27 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.4 0.3 0.3 0.2 0.3

28 0.5 0.5 0.5 0.5 0.4 0.5 0.4 0.4 0.3 0.2 0.2 0.3

29 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3

30 0.4 0.4 0.4 0.4 0.2 0.3 0.2 0.2 0.3 0.3 0.3 0.3

31 0.3 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.2 0.3

32 0.4 0.3 0.3 0.4 0.4 0.3 0.3 0.4 0.3 0.3 0.3 0.3

33 0.2 0.3 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.4

34 0.4 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.4 0.4 0.4

35 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.4 0.3 0.5

36 0.4 0.3 0.3 0.4 0.3 0.3 0.2 0.3 0.4 0.3 0.3 0.4

37 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.5

38 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.2 0.3 0.3 0.2

39 0.4 0.5 0.3 0.4 0.2 0.2 0.2 0.2 0.4 0.4 0.5 0.4

40 0.5 0.5 0.4 0.5 0.3 0.2 0.2 0.3 0.2 0.3 0.3 0.2

41 0.3 0.3 0.3 0.3 0.5 0.3 0.3 0.5 0.2 0.3 0.2 0.2

42 0.4 0.5 0.5 0.4 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.4

43 0.3 0.4 0.4 0.3 0.4 0.3 0.4 0.4 0.2 0.3 0.3 0.2

44 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.5 0.4 0.4 0.5

128

45 0.2 0.3 0.2 0.2 0.5 0.5 0.4 0.5 0.3 0.3 0.3 0.3

46 0.4 0.4 0.3 0.4 0.5 0.5 0.3 0.5 0.4 0.3 0.3 0.4

47 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

48 0.3 0.3 0.3 0.3 0.4 0.5 0.4 0.4 0.3 0.4 0.4 0.3

49 0.4 0.3 0.3 0.4 0.4 0.3 0.3 0.4 0.4 0.3 0.3 0.4

50 0.2 0.2 0.3 0.2 0.2 0.2 0.3 0.2 0.5 0.4 0.4 0.5

51 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.2 0.3 0.3 0.2

52 0.5 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.4

53 0.5 0.4 0.4 0.5 0.2 0.2 0.3 0.2 0.2 0.3 0.3 0.2

54 0.5 0.4 0.4 0.5 0.5 0.4 0.4 0.4 0.3 0.3 0.4 0.3

55 0.5 0.3 0.3 0.5 0.5 0.4 0.4 0.5 0.4 0.3 0.2 0.4

56 0.3 0.3 0.2 0.2 0.3 0.4 0.4 0.3 0.4 0.4 0.4 0.4

57 0.4 0.3 0.2 0.4 0.3 0.4 0.4 0.3 0.4 0.4 0.4 0.4

58 0.4 0.4 0.4 0.4 0.2 0.3 0.3 0.2 0.2 0.3 0.3 0.2

59 0.2 0.3 0.3 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

60 0.2 0.3 0.3 0.2 0.2 0.3 0.3 0.2 0.4 0.3 0.4 0.4

61 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.3 0.3 0.4 0.4 0.3

62 0.5 0.3 0.3 0.3 0.5 0.4 0.4 0.5 0.3 0.4 0.4 0.3

63 0.3 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

64 0.5 0.4 0.4 0.5 0.3 0.4 0.4 0.3 0.5 0.4 0.4 0.4

65 0.3 0.3 0.3 0.3 0.5 0.5 0.4 0.5 0.2 0.3 0.3 0.2

66 0.2 0.3 0.3 0.2 0.3 0.4 0.4 0.3 0.3 0.4 0.2 0.3

67 0.2 0.3 0.4 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

68 0.4 0.4 0.4 0.4 0.3 0.4 0.3 0.3 0.2 0.3 0.3 0.3

69 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.2 0.4 0.4 0.2

70 0.3 0.3 0.3 0.3 0.2 0.3 0.3 0.2 0.2 0.4 0.4 0.2

129


No.

1st GPA

Y1S2

ANN CHD Ens 2nd

GPA Y1S2

ANN CHD Ens

3rd GPA

Y1S2

ANN CHD Ens

1 0.3 0.2 0.2 0.2 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

2 0.1 0.3 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

3 0.3 0.3 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

4 0.3 0.3 0.2 0.3 0.4 0.3 0.3 0.3 0.5 0.4 0.5 0.5

5 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.5 0.4 0.5 0.5

6 0.2 0.2 0.3 0.2 0.5 0.4 0.4 0.4 0.4 0.3 0.4 0.4

7 0.3 0.4 0.3 0.3 0.3 0.4 0.4 0.4 0.2 0.3 0.3 0.3

8 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.2 0.3 0.3 0.3

9 0.3 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

10 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

11 0.4 0.4 0.3 0.3 0.2 0.3 0.2 0.2 0.4 0.4 0.4 0.4

12 0.5 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

13 0.3 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3

14 0.5 0.5 0.4 0.5 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3

15 0.4 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

16 0.3 0.4 0.3 0.3 0.4 0.3 0.3 0.3 0.5 0.4 0.4 0.4

17 0.2 0.3 0.2 0.2 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4

18 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.4 0.4 0.4

19 0.4 0.5 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

20 0.2 0.3 0.3 0.3 0.5 0.5 0.4 0.5 0.4 0.4 0.4 0.4

21 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.2 0.3 0.2 0.2

22 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3

23 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.2 0.3 0.3 0.3

24 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.4 0.4 0.4 0.4

25 0.4 0.5 0.5 0.5 0.4 0.5 0.4 0.4 0.3 0.3 0.3 0.3

130

26 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.3 0.3 0.3 0.3

27 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.2 0.2

28 0.5 0.5 0.5 0.5 0.4 0.5 0.4 0.4 0.3 0.2 0.2 0.2

29 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3

30 0.4 0.4 0.4 0.4 0.2 0.3 0.2 0.2 0.3 0.3 0.3 0.3

31 0.3 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.2 0.2

32 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3

33 0.2 0.3 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.3

34 0.4 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.4 0.4 0.4

35 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.4 0.3 0.3

36 0.4 0.3 0.3 0.3 0.3 0.3 0.2 0.3 0.4 0.3 0.3 0.3

37 0.4 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4

38 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.2 0.3 0.3 0.3

39 0.4 0.5 0.3 0.3 0.2 0.2 0.2 0.2 0.4 0.4 0.5 0.5

40 0.5 0.5 0.4 0.5 0.3 0.2 0.2 0.2 0.2 0.3 0.3 0.3

41 0.3 0.3 0.3 0.3 0.5 0.3 0.3 0.3 0.2 0.3 0.2 0.2

42 0.4 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.3

43 0.3 0.4 0.4 0.4 0.4 0.3 0.4 0.4 0.2 0.3 0.3 0.3

44 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.5 0.4 0.4 0.4

45 0.2 0.3 0.2 0.2 0.5 0.5 0.4 0.5 0.3 0.3 0.3 0.3

46 0.4 0.4 0.3 0.4 0.5 0.5 0.3 0.5 0.4 0.3 0.3 0.3

47 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

48 0.3 0.3 0.3 0.3 0.4 0.5 0.4 0.4 0.3 0.4 0.4 0.4

49 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3

50 0.2 0.2 0.3 0.2 0.2 0.2 0.3 0.2 0.5 0.4 0.4 0.4

131


No.

1st GPA

Y1S2

SVM OWSR Ens 2nd

GPA Y1S2

SVM OWSR Ens

3rd GPA

Y1S2

SVM OWSR Ens

1 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.3 0.2 0.2 0.2 0.3

2 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

4 0.5 0.5 0.5 0.5 0.4 0.3 0.3 0.3 0.5 0.4 0.5 0.5

5 0.5 0.5 0.5 0.5 0.4 0.3 0.3 0.3 0.5 0.4 0.5 0.5

6 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.4 0.3 0.4 0.4

7 0.2 0.2 0.2 0.3 0.3 0.4 0.4 0.4 0.2 0.3 0.3 0.3

8 0.2 0.2 0.2 0.3 0.4 0.4 0.4 0.4 0.2 0.3 0.3 0.3

9 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

10 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

11 0.4 0.4 0.4 0.4 0.2 0.3 0.2 0.2 0.4 0.4 0.4 0.4

12 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

13 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3

14 0.4 0.4 0.4 0.3 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3

15 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

16 0.5 0.5 0.5 0.4 0.4 0.3 0.3 0.3 0.5 0.4 0.4 0.4

17 0.3 0.3 0.3 0.4 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4

18 0.5 0.5 0.5 0.4 0.3 0.3 0.3 0.3 0.5 0.4 0.4 0.4

19 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

20 0.4 0.4 0.4 0.4 0.5 0.5 0.4 0.5 0.4 0.4 0.4 0.4

21 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.2 0.3 0.2 0.2

22 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3

23 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.3 0.2 0.3 0.3 0.3

24 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.4 0.4 0.4 0.4

25 0.3 0.3 0.3 0.3 0.4 0.5 0.4 0.4 0.3 0.3 0.3 0.3

132

26 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.3 0.3 0.3 0.3

27 0.3 0.3 0.3 0.2 0.4 0.3 0.3 0.3 0.3 0.3 0.2 0.2

28 0.3 0.3 0.3 0.2 0.4 0.5 0.4 0.4 0.3 0.2 0.2 0.2

29 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3

30 0.3 0.3 0.3 0.3 0.2 0.3 0.2 0.2 0.3 0.3 0.3 0.3

31 0.3 0.3 0.3 0.2 0.4 0.4 0.4 0.4 0.3 0.3 0.2 0.2

32 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3

33 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.3

34 0.4 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.4 0.4 0.4

35 0.5 0.5 0.5 0.3 0.4 0.4 0.4 0.4 0.5 0.4 0.3 0.3

36 0.4 0.4 0.4 0.3 0.3 0.3 0.2 0.3 0.4 0.3 0.3 0.3

37 0.5 0.5 0.5 0.4 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4

38 0.2 0.2 0.2 0.3 0.5 0.5 0.5 0.5 0.2 0.3 0.3 0.3

39 0.4 0.4 0.4 0.5 0.2 0.2 0.2 0.2 0.4 0.4 0.5 0.5

40 0.2 0.2 0.2 0.3 0.3 0.2 0.2 0.2 0.2 0.3 0.3 0.3

41 0.2 0.2 0.2 0.2 0.5 0.3 0.3 0.3 0.2 0.3 0.2 0.2

42 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.3

43 0.2 0.2 0.2 0.3 0.4 0.3 0.4 0.4 0.2 0.3 0.3 0.3

44 0.5 0.5 0.5 0.4 0.3 0.3 0.3 0.3 0.5 0.4 0.4 0.4

45 0.3 0.3 0.3 0.3 0.5 0.5 0.4 0.5 0.3 0.3 0.3 0.3

46 0.4 0.4 0.4 0.3 0.5 0.5 0.3 0.5 0.4 0.3 0.3 0.3

47 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

48 0.3 0.3 0.3 0.4 0.4 0.5 0.4 0.4 0.3 0.4 0.4 0.4

49 0.4 0.4 0.4 0.3 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3

50 0.5 0.5 0.5 0.4 0.2 0.2 0.3 0.2 0.5 0.4 0.4 0.4

51 0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.3 0.2 0.3 0.3 0.3

52 0.4 0.4 0.4 0.3 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3

53 0.2 0.2 0.2 0.3 0.2 0.2 0.3 0.2 0.2 0.3 0.3 0.3

54 0.3 0.3 0.3 0.4 0.5 0.4 0.4 0.4 0.3 0.3 0.4 0.4

55 0.4 0.4 0.4 0.2 0.5 0.4 0.4 0.4 0.4 0.3 0.2 0.2

133

56 0.4 0.4 0.4 0.4 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4

57 0.4 0.4 0.4 0.4 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4

58 0.2 0.2 0.3 0.3 0.2 0.3 0.3 0.3 0.2 0.3 0.3 0.3

59 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

60 0.4 0.4 0.4 0.4 0.2 0.3 0.3 0.3 0.4 0.3 0.4 0.4

61 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.3 0.4 0.4 0.4

62 0.3 0.3 0.3 0.4 0.5 0.4 0.4 0.4 0.3 0.4 0.4 0.4

63 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

64 0.5 0.4 0.4 0.4 0.3 0.4 0.4 0.4 0.5 0.4 0.4 0.4

65 0.2 0.2 0.3 0.3 0.5 0.5 0.4 0.5 0.2 0.3 0.3 0.3

66 0.3 0.3 0.3 0.2 0.3 0.4 0.4 0.4 0.3 0.4 0.2 0.2

67 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.2 0.3 0.3 0.2

68 0.2 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.2 0.3 0.3 0.3

69 0.2 0.2 0.2 0.4 0.3 0.3 0.3 0.3 0.2 0.4 0.4 0.4

70 0.2 0.2 0.2 0.4 0.2 0.3 0.3 0.3 0.2 0.4 0.4 0.4



No. 1st

GPA Y2S1

ANN CHD SVM 2nd

GPA Y2S1

ANN CHD SVM 3rd

GPA Y2S1

ANN CHD SVM

1 0.3 0.2 0.2 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

2 0.1 0.3 0.2 0.1 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

3 0.3 0.3 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

4 0.3 0.3 0.2 0.3 0.4 0.3 0.3 0.4 0.5 0.4 0.5 0.5

5 0.3 0.4 0.4 0.3 0.4 0.3 0.3 0.4 0.5 0.4 0.5 0.5

6 0.2 0.2 0.3 0.2 0.5 0.4 0.4 0.5 0.4 0.3 0.4 0.4

7 0.3 0.4 0.3 0.3 0.3 0.4 0.4 0.3 0.2 0.3 0.3 0.2

8 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.2 0.3 0.3 0.2

134

9 0.3 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

10 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

11 0.4 0.4 0.3 0.4 0.2 0.3 0.2 0.2 0.4 0.4 0.4 0.4

12 0.5 0.4 0.3 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

13 0.3 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3

14 0.5 0.5 0.4 0.5 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.4

15 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

16 0.3 0.4 0.3 0.3 0.4 0.3 0.3 0.4 0.5 0.4 0.4 0.5

17 0.2 0.3 0.2 0.2 0.4 0.3 0.3 0.4 0.3 0.4 0.4 0.3

18 0.2 0.3 0.3 0.2 0.3 0.3 0.3 0.3 0.5 0.4 0.4 0.5

19 0.4 0.5 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

20 0.2 0.3 0.3 0.2 0.5 0.5 0.4 0.5 0.4 0.4 0.4 0.4

21 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.2 0.3 0.2 0.2

22 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3

23 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.2 0.3 0.3 0.2

24 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.4

25 0.4 0.5 0.5 0.4 0.4 0.5 0.4 0.4 0.3 0.3 0.3 0.3

26 0.4 0.3 0.3 0.4 0.3 0.4 0.4 0.3 0.3 0.3 0.3 0.3

27 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.4 0.3 0.3 0.2 0.3

28 0.5 0.5 0.5 0.5 0.4 0.5 0.4 0.4 0.3 0.2 0.2 0.3

29 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3

30 0.4 0.4 0.4 0.4 0.2 0.3 0.2 0.2 0.3 0.3 0.3 0.3

31 0.3 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.2 0.3

32 0.4 0.3 0.3 0.4 0.4 0.3 0.3 0.4 0.3 0.3 0.3 0.3

33 0.2 0.3 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.4

34 0.4 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.4 0.4 0.4

35 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.4 0.3 0.5

36 0.4 0.3 0.3 0.4 0.3 0.3 0.2 0.3 0.4 0.3 0.3 0.4

37 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.5

38 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.2 0.3 0.3 0.2

135

39 0.4 0.5 0.3 0.4 0.2 0.2 0.2 0.2 0.4 0.4 0.5 0.4

40 0.5 0.5 0.4 0.5 0.3 0.2 0.2 0.3 0.2 0.3 0.3 0.2

41 0.3 0.3 0.3 0.3 0.5 0.3 0.3 0.5 0.2 0.3 0.2 0.2

42 0.4 0.5 0.5 0.4 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.4

43 0.3 0.4 0.4 0.3 0.4 0.3 0.4 0.4 0.2 0.3 0.3 0.2

44 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.5 0.4 0.4 0.5

45 0.2 0.3 0.2 0.2 0.5 0.5 0.4 0.5 0.3 0.3 0.3 0.3

46 0.4 0.4 0.3 0.4 0.5 0.5 0.3 0.5 0.4 0.3 0.3 0.4

47 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

48 0.3 0.3 0.3 0.3 0.4 0.5 0.4 0.4 0.3 0.4 0.4 0.3

49 0.4 0.3 0.3 0.4 0.4 0.3 0.3 0.4 0.4 0.3 0.3 0.4

50 0.2 0.2 0.3 0.2 0.2 0.2 0.3 0.2 0.5 0.4 0.4 0.5

51 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.2 0.3 0.3 0.2

52 0.5 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.4

53 0.5 0.4 0.4 0.5 0.2 0.2 0.3 0.2 0.2 0.3 0.3 0.2

54 0.5 0.4 0.4 0.5 0.5 0.4 0.4 0.4 0.3 0.3 0.4 0.3

55 0.5 0.3 0.3 0.5 0.5 0.4 0.4 0.5 0.4 0.3 0.2 0.4

56 0.3 0.3 0.2 0.2 0.3 0.4 0.4 0.3 0.4 0.4 0.4 0.4

57 0.4 0.3 0.2 0.4 0.3 0.4 0.4 0.3 0.4 0.4 0.4 0.4

58 0.4 0.4 0.4 0.4 0.2 0.3 0.3 0.2 0.2 0.3 0.3 0.2

59 0.2 0.3 0.3 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

60 0.2 0.3 0.3 0.2 0.2 0.3 0.3 0.2 0.4 0.3 0.4 0.4

61 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.3 0.3 0.4 0.4 0.3

62 0.5 0.3 0.3 0.3 0.5 0.4 0.4 0.5 0.3 0.4 0.4 0.3

63 0.3 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

64 0.5 0.4 0.4 0.5 0.3 0.4 0.4 0.3 0.5 0.4 0.4 0.4

65 0.3 0.3 0.3 0.3 0.5 0.5 0.4 0.5 0.2 0.3 0.3 0.2

66 0.2 0.3 0.3 0.2 0.3 0.4 0.4 0.3 0.3 0.4 0.2 0.3

67 0.2 0.3 0.4 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

68 0.4 0.4 0.4 0.4 0.3 0.4 0.3 0.3 0.2 0.3 0.3 0.3

136

69 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.2 0.4 0.4 0.2

70 0.3 0.3 0.3 0.3 0.2 0.3 0.3 0.2 0.2 0.4 0.4 0.2


No.

1st GPA Y2S1

ANN CHD Ens 2nd

GPA Y2S1

ANN CHD Ens 3rd

GPA Y2S1

ANN CHD Ens

1 0.3 0.2 0.2 0.2 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

2 0.1 0.3 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

3 0.3 0.3 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

4 0.3 0.3 0.2 0.3 0.4 0.3 0.3 0.3 0.5 0.4 0.5 0.5

5 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.5 0.4 0.5 0.5

6 0.2 0.2 0.3 0.2 0.5 0.4 0.4 0.4 0.4 0.3 0.4 0.4

7 0.3 0.4 0.3 0.3 0.3 0.4 0.4 0.4 0.2 0.3 0.3 0.3

8 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.2 0.3 0.3 0.3

9 0.3 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

10 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

11 0.4 0.4 0.3 0.3 0.2 0.3 0.2 0.2 0.4 0.4 0.4 0.4

12 0.5 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

13 0.3 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3

14 0.5 0.5 0.4 0.5 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3

15 0.4 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

16 0.3 0.4 0.3 0.3 0.4 0.3 0.3 0.3 0.5 0.4 0.4 0.4

17 0.2 0.3 0.2 0.2 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4

18 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.4 0.4 0.4

19 0.4 0.5 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

20 0.2 0.3 0.3 0.3 0.5 0.5 0.4 0.5 0.4 0.4 0.4 0.4

21 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.2 0.3 0.2 0.2

22 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3

23 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.2 0.3 0.3 0.3

137

24 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.4 0.4 0.4 0.4

25 0.4 0.5 0.5 0.5 0.4 0.5 0.4 0.4 0.3 0.3 0.3 0.3

26 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.3 0.3 0.3 0.3

27 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.2 0.2

28 0.5 0.5 0.5 0.5 0.4 0.5 0.4 0.4 0.3 0.2 0.2 0.2

29 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3

30 0.4 0.4 0.4 0.4 0.2 0.3 0.2 0.2 0.3 0.3 0.3 0.3

31 0.3 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.2 0.2

32 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3

33 0.2 0.3 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.3

34 0.4 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.4 0.4 0.4

35 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.4 0.3 0.3

36 0.4 0.3 0.3 0.3 0.3 0.3 0.2 0.3 0.4 0.3 0.3 0.3

37 0.4 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4

38 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.2 0.3 0.3 0.3

39 0.4 0.5 0.3 0.3 0.2 0.2 0.2 0.2 0.4 0.4 0.5 0.5

40 0.5 0.5 0.4 0.5 0.3 0.2 0.2 0.2 0.2 0.3 0.3 0.3

41 0.3 0.3 0.3 0.3 0.5 0.3 0.3 0.3 0.2 0.3 0.2 0.2

42 0.4 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.3

43 0.3 0.4 0.4 0.4 0.4 0.3 0.4 0.4 0.2 0.3 0.3 0.3

44 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.5 0.4 0.4 0.4

45 0.2 0.3 0.2 0.2 0.5 0.5 0.4 0.5 0.3 0.3 0.3 0.3

46 0.4 0.4 0.3 0.4 0.5 0.5 0.3 0.5 0.4 0.3 0.3 0.3

47 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

48 0.3 0.3 0.3 0.3 0.4 0.5 0.4 0.4 0.3 0.4 0.4 0.4

49 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3

50 0.2 0.2 0.3 0.2 0.2 0.2 0.3 0.2 0.5 0.4 0.4 0.4

51 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.2 0.3 0.3 0.3

52 0.5 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3

53 0.5 0.4 0.4 0.4 0.2 0.2 0.3 0.2 0.2 0.3 0.3 0.3

138

54 0.5 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.3 0.3 0.4 0.4

55 0.5 0.3 0.3 0.3 0.5 0.4 0.4 0.4 0.4 0.3 0.2 0.2

56 0.3 0.3 0.2 0.2 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4

57 0.4 0.3 0.2 0.2 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4

58 0.4 0.4 0.4 0.4 0.2 0.3 0.3 0.3 0.2 0.3 0.3 0.3

59 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

60 0.2 0.3 0.3 0.3 0.2 0.3 0.3 0.3 0.4 0.3 0.4 0.4

61 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.4 0.4 0.4

62 0.5 0.3 0.3 0.3 0.5 0.4 0.4 0.4 0.3 0.4 0.4 0.4

63 0.3 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

64 0.5 0.4 0.4 0.4 0.3 0.4 0.4 0.4 0.5 0.4 0.4 0.4

65 0.3 0.3 0.3 0.3 0.5 0.5 0.4 0.5 0.2 0.3 0.3 0.3

66 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.3 0.4 0.2 0.2

67 0.2 0.3 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

68 0.4 0.4 0.4 0.4 0.3 0.4 0.3 0.3 0.2 0.3 0.3 0.3

69 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.2 0.4 0.4 0.4

70 0.3 0.3 0.3 0.3 0.2 0.3 0.3 0.3 0.2 0.4 0.4 0.4


No.

1st GPAY2S1

SVM OWSR Ens 2nd

GPA Y2S1

SVM OWSR Ens 3rd

GPAY2S1

SVM OWSR Ens

1 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.3 0.2 0.2 0.2 0.3

2 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

4 0.5 0.5 0.5 0.5 0.4 0.3 0.3 0.3 0.5 0.4 0.5 0.5

5 0.5 0.5 0.5 0.5 0.4 0.3 0.3 0.3 0.5 0.4 0.5 0.5

6 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.4 0.3 0.4 0.4

7 0.2 0.2 0.2 0.3 0.3 0.4 0.4 0.4 0.2 0.3 0.3 0.3

8 0.2 0.2 0.2 0.3 0.4 0.4 0.4 0.4 0.2 0.3 0.3 0.3

139

9 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

10 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

11 0.4 0.4 0.4 0.4 0.2 0.3 0.2 0.2 0.4 0.4 0.4 0.4

12 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

13 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3

14 0.4 0.4 0.4 0.3 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3

15 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

16 0.5 0.5 0.5 0.4 0.4 0.3 0.3 0.3 0.5 0.4 0.4 0.4

17 0.3 0.3 0.3 0.4 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4

18 0.5 0.5 0.5 0.4 0.3 0.3 0.3 0.3 0.5 0.4 0.4 0.4

19 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

20 0.4 0.4 0.4 0.4 0.5 0.5 0.4 0.5 0.4 0.4 0.4 0.4

21 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.2 0.3 0.2 0.2

22 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3

23 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.3 0.2 0.3 0.3 0.3

24 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.4 0.4 0.4 0.4

25 0.3 0.3 0.3 0.3 0.4 0.5 0.4 0.4 0.3 0.3 0.3 0.3

26 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.3 0.3 0.3 0.3

27 0.3 0.3 0.3 0.2 0.4 0.3 0.3 0.3 0.3 0.3 0.2 0.2

28 0.3 0.3 0.3 0.2 0.4 0.5 0.4 0.4 0.3 0.2 0.2 0.2

29 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3

30 0.3 0.3 0.3 0.3 0.2 0.3 0.2 0.2 0.3 0.3 0.3 0.3

31 0.3 0.3 0.3 0.2 0.4 0.4 0.4 0.4 0.3 0.3 0.2 0.2

32 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3

33 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.3

34 0.4 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.4 0.4 0.4

35 0.5 0.5 0.5 0.3 0.4 0.4 0.4 0.4 0.5 0.4 0.3 0.3

36 0.4 0.4 0.4 0.3 0.3 0.3 0.2 0.3 0.4 0.3 0.3 0.3

37 0.5 0.5 0.5 0.4 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4

38 0.2 0.2 0.2 0.3 0.5 0.5 0.5 0.5 0.2 0.3 0.3 0.3

140

39 0.4 0.4 0.4 0.5 0.2 0.2 0.2 0.2 0.4 0.4 0.5 0.5

40 0.2 0.2 0.2 0.3 0.3 0.2 0.2 0.2 0.2 0.3 0.3 0.3

41 0.2 0.2 0.2 0.2 0.5 0.3 0.3 0.3 0.2 0.3 0.2 0.2

42 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.3

43 0.2 0.2 0.2 0.3 0.4 0.3 0.4 0.4 0.2 0.3 0.3 0.3

44 0.5 0.5 0.5 0.4 0.3 0.3 0.3 0.3 0.5 0.4 0.4 0.4

45 0.3 0.3 0.3 0.3 0.5 0.5 0.4 0.5 0.3 0.3 0.3 0.3

46 0.4 0.4 0.4 0.3 0.5 0.5 0.3 0.5 0.4 0.3 0.3 0.3

47 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

48 0.3 0.3 0.3 0.4 0.4 0.5 0.4 0.4 0.3 0.4 0.4 0.4

49 0.4 0.4 0.4 0.3 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3

50 0.5 0.5 0.5 0.4 0.2 0.2 0.3 0.2 0.5 0.4 0.4 0.4

51 0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.3 0.2 0.3 0.3 0.3

52 0.4 0.4 0.4 0.3 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3

53 0.2 0.2 0.2 0.3 0.2 0.2 0.3 0.2 0.2 0.3 0.3 0.3

54 0.3 0.3 0.3 0.4 0.5 0.4 0.4 0.4 0.3 0.3 0.4 0.4

55 0.4 0.4 0.4 0.2 0.5 0.4 0.4 0.4 0.4 0.3 0.2 0.2

56 0.4 0.4 0.4 0.4 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4

57 0.4 0.4 0.4 0.4 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4

58 0.2 0.2 0.3 0.3 0.2 0.3 0.3 0.3 0.2 0.3 0.3 0.3

59 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

60 0.4 0.4 0.4 0.4 0.2 0.3 0.3 0.3 0.4 0.3 0.4 0.4

61 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.3 0.4 0.4 0.4

62 0.3 0.3 0.3 0.4 0.5 0.4 0.4 0.4 0.3 0.4 0.4 0.4

63 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

64 0.5 0.4 0.4 0.4 0.3 0.4 0.4 0.4 0.5 0.4 0.4 0.4

65 0.2 0.2 0.3 0.3 0.5 0.5 0.4 0.5 0.2 0.3 0.3 0.3

66 0.3 0.3 0.3 0.2 0.3 0.4 0.4 0.4 0.3 0.4 0.2 0.2

67 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.2 0.3 0.3 0.2

68 0.2 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.2 0.3 0.3 0.3

141

69 0.2 0.2 0.2 0.4 0.3 0.3 0.3 0.3 0.2 0.4 0.4 0.4

70 0.2 0.2 0.2 0.4 0.2 0.3 0.3 0.3 0.2 0.4 0.4 0.4



No. 1st

GPA Y2S2

ANN CHD SVM 2nd

GPA Y2S2

ANN CHD SVM 3rd

GPA Y2S2

ANN CHD SVM

1 0.3 0.2 0.2 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

2 0.1 0.3 0.2 0.1 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

3 0.3 0.3 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

4 0.3 0.3 0.2 0.3 0.4 0.3 0.3 0.4 0.5 0.4 0.5 0.5

5 0.3 0.4 0.4 0.3 0.4 0.3 0.3 0.4 0.5 0.4 0.5 0.5

6 0.2 0.2 0.3 0.2 0.5 0.4 0.4 0.5 0.4 0.3 0.4 0.4

7 0.3 0.4 0.3 0.3 0.3 0.4 0.4 0.3 0.2 0.3 0.3 0.2

8 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.2 0.3 0.3 0.2

9 0.3 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

10 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

11 0.4 0.4 0.3 0.4 0.2 0.3 0.2 0.2 0.4 0.4 0.4 0.4

12 0.5 0.4 0.3 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

13 0.3 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3

14 0.5 0.5 0.4 0.5 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.4

15 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

16 0.3 0.4 0.3 0.3 0.4 0.3 0.3 0.4 0.5 0.4 0.4 0.5

17 0.2 0.3 0.2 0.2 0.4 0.3 0.3 0.4 0.3 0.4 0.4 0.3

18 0.2 0.3 0.3 0.2 0.3 0.3 0.3 0.3 0.5 0.4 0.4 0.5

19 0.4 0.5 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

20 0.2 0.3 0.3 0.2 0.5 0.5 0.4 0.5 0.4 0.4 0.4 0.4

21 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.2 0.3 0.2 0.2

142

22 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3

23 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.2 0.3 0.3 0.2

24 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.4

25 0.4 0.5 0.5 0.4 0.4 0.5 0.4 0.4 0.3 0.3 0.3 0.3

26 0.4 0.3 0.3 0.4 0.3 0.4 0.4 0.3 0.3 0.3 0.3 0.3

27 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.4 0.3 0.3 0.2 0.3

28 0.5 0.5 0.5 0.5 0.4 0.5 0.4 0.4 0.3 0.2 0.2 0.3

29 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3

30 0.4 0.4 0.4 0.4 0.2 0.3 0.2 0.2 0.3 0.3 0.3 0.3

31 0.3 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.2 0.3

32 0.4 0.3 0.3 0.4 0.4 0.3 0.3 0.4 0.3 0.3 0.3 0.3

33 0.2 0.3 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.4

34 0.4 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.4 0.4 0.4

35 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.4 0.3 0.5

36 0.4 0.3 0.3 0.4 0.3 0.3 0.2 0.3 0.4 0.3 0.3 0.4

37 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.5

38 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.2 0.3 0.3 0.2

39 0.4 0.5 0.3 0.4 0.2 0.2 0.2 0.2 0.4 0.4 0.5 0.4

40 0.5 0.5 0.4 0.5 0.3 0.2 0.2 0.3 0.2 0.3 0.3 0.2

41 0.3 0.3 0.3 0.3 0.5 0.3 0.3 0.5 0.2 0.3 0.2 0.2

42 0.4 0.5 0.5 0.4 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.4

43 0.3 0.4 0.4 0.3 0.4 0.3 0.4 0.4 0.2 0.3 0.3 0.2

44 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.5 0.4 0.4 0.5

45 0.2 0.3 0.2 0.2 0.5 0.5 0.4 0.5 0.3 0.3 0.3 0.3

46 0.4 0.4 0.3 0.4 0.5 0.5 0.3 0.5 0.4 0.3 0.3 0.4

47 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

48 0.3 0.3 0.3 0.3 0.4 0.5 0.4 0.4 0.3 0.4 0.4 0.3

49 0.4 0.3 0.3 0.4 0.4 0.3 0.3 0.4 0.4 0.3 0.3 0.4

50 0.2 0.2 0.3 0.2 0.2 0.2 0.3 0.2 0.5 0.4 0.4 0.5

143


No.

1st GPA Y2S2

ANN CHD Ens 2nd

GPA Y2S2

ANN CHD Ens

3rd

GPA

Y2S2

ANN CHD Ens

1 0.3 0.2 0.2 0.2 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

2 0.1 0.3 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

3 0.3 0.3 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

4 0.3 0.3 0.2 0.3 0.4 0.3 0.3 0.3 0.5 0.4 0.5 0.5

5 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.5 0.4 0.5 0.5

6 0.2 0.2 0.3 0.2 0.5 0.4 0.4 0.4 0.4 0.3 0.4 0.4

7 0.3 0.4 0.3 0.3 0.3 0.4 0.4 0.4 0.2 0.3 0.3 0.3

8 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.2 0.3 0.3 0.3

9 0.3 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

10 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

11 0.4 0.4 0.3 0.3 0.2 0.3 0.2 0.2 0.4 0.4 0.4 0.4

12 0.5 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

13 0.3 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3

14 0.5 0.5 0.4 0.5 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3

15 0.4 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

16 0.3 0.4 0.3 0.3 0.4 0.3 0.3 0.3 0.5 0.4 0.4 0.4

17 0.2 0.3 0.2 0.2 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4

18 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.4 0.4 0.4

19 0.4 0.5 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

20 0.2 0.3 0.3 0.3 0.5 0.5 0.4 0.5 0.4 0.4 0.4 0.4

21 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.2 0.3 0.2 0.2

22 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3

23 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.2 0.3 0.3 0.3

24 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.4 0.4 0.4 0.4

25 0.4 0.5 0.5 0.5 0.4 0.5 0.4 0.4 0.3 0.3 0.3 0.3

144

26 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.3 0.3 0.3 0.3

27 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.2 0.2

28 0.5 0.5 0.5 0.5 0.4 0.5 0.4 0.4 0.3 0.2 0.2 0.2

29 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3

30 0.4 0.4 0.4 0.4 0.2 0.3 0.2 0.2 0.3 0.3 0.3 0.3

31 0.3 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.2 0.2

32 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3

33 0.2 0.3 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.3

34 0.4 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.4 0.4 0.4

35 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.4 0.3 0.3

36 0.4 0.3 0.3 0.3 0.3 0.3 0.2 0.3 0.4 0.3 0.3 0.3

37 0.4 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4

38 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.2 0.3 0.3 0.3

39 0.4 0.5 0.3 0.3 0.2 0.2 0.2 0.2 0.4 0.4 0.5 0.5

40 0.5 0.5 0.4 0.5 0.3 0.2 0.2 0.2 0.2 0.3 0.3 0.3

41 0.3 0.3 0.3 0.3 0.5 0.3 0.3 0.3 0.2 0.3 0.2 0.2

42 0.4 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.3

43 0.3 0.4 0.4 0.4 0.4 0.3 0.4 0.4 0.2 0.3 0.3 0.3

44 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.5 0.4 0.4 0.4

45 0.2 0.3 0.2 0.2 0.5 0.5 0.4 0.5 0.3 0.3 0.3 0.3

46 0.4 0.4 0.3 0.4 0.5 0.5 0.3 0.5 0.4 0.3 0.3 0.3

47 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

48 0.3 0.3 0.3 0.3 0.4 0.5 0.4 0.4 0.3 0.4 0.4 0.4

49 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3

50 0.2 0.2 0.3 0.2 0.2 0.2 0.3 0.2 0.5 0.4 0.4 0.4


1st SVM OWSR Ensemble 2nd GPA

SVM OWSR Ensemble 3rd SVM OWSR Ensemble

145

No. GPA

Y2S2

Y2S2 GPA

Y2S2

1 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.3 0.2 0.2 0.2 0.3

2 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

4 0.5 0.5 0.5 0.5 0.4 0.3 0.3 0.3 0.5 0.4 0.5 0.5

5 0.5 0.5 0.5 0.5 0.4 0.3 0.3 0.3 0.5 0.4 0.5 0.5

6 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.4 0.3 0.4 0.4

7 0.2 0.2 0.2 0.3 0.3 0.4 0.4 0.4 0.2 0.3 0.3 0.3

8 0.2 0.2 0.2 0.3 0.4 0.4 0.4 0.4 0.2 0.3 0.3 0.3

9 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

10 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

11 0.4 0.4 0.4 0.4 0.2 0.3 0.2 0.2 0.4 0.4 0.4 0.4

12 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

13 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3

14 0.4 0.4 0.4 0.3 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3

15 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3

16 0.5 0.5 0.5 0.4 0.4 0.3 0.3 0.3 0.5 0.4 0.4 0.4

17 0.3 0.3 0.3 0.4 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4

18 0.5 0.5 0.5 0.4 0.3 0.3 0.3 0.3 0.5 0.4 0.4 0.4

19 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

20 0.4 0.4 0.4 0.4 0.5 0.5 0.4 0.5 0.4 0.4 0.4 0.4

21 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.2 0.3 0.2 0.2

22 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3

23 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.3 0.2 0.3 0.3 0.3

24 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.4 0.4 0.4 0.4

25 0.3 0.3 0.3 0.3 0.4 0.5 0.4 0.4 0.3 0.3 0.3 0.3

26 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.3 0.3 0.3 0.3

27 0.3 0.3 0.3 0.2 0.4 0.3 0.3 0.3 0.3 0.3 0.2 0.2

28 0.3 0.3 0.3 0.2 0.4 0.5 0.4 0.4 0.3 0.2 0.2 0.2

146

29 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.3

30 0.3 0.3 0.3 0.3 0.2 0.3 0.2 0.2 0.3 0.3 0.3 0.3

31 0.3 0.3 0.3 0.2 0.4 0.4 0.4 0.4 0.3 0.3 0.2 0.2

32 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3

33 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.3

34 0.4 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.4 0.4 0.4

35 0.5 0.5 0.5 0.3 0.4 0.4 0.4 0.4 0.5 0.4 0.3 0.3

36 0.4 0.4 0.4 0.3 0.3 0.3 0.2 0.3 0.4 0.3 0.3 0.3

37 0.5 0.5 0.5 0.4 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.4

38 0.2 0.2 0.2 0.3 0.5 0.5 0.5 0.5 0.2 0.3 0.3 0.3

39 0.4 0.4 0.4 0.5 0.2 0.2 0.2 0.2 0.4 0.4 0.5 0.5

40 0.2 0.2 0.2 0.3 0.3 0.2 0.2 0.2 0.2 0.3 0.3 0.3

41 0.2 0.2 0.2 0.2 0.5 0.3 0.3 0.3 0.2 0.3 0.2 0.2

42 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.3

43 0.2 0.2 0.2 0.3 0.4 0.3 0.4 0.4 0.2 0.3 0.3 0.3

44 0.5 0.5 0.5 0.4 0.3 0.3 0.3 0.3 0.5 0.4 0.4 0.4

45 0.3 0.3 0.3 0.3 0.5 0.5 0.4 0.5 0.3 0.3 0.3 0.3

46 0.4 0.4 0.4 0.3 0.5 0.5 0.3 0.5 0.4 0.3 0.3 0.3

47 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

48 0.3 0.3 0.3 0.4 0.4 0.5 0.4 0.4 0.3 0.4 0.4 0.4

49 0.4 0.4 0.4 0.3 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3

Example results of module 4: ranked activities recommendation

No. 1st

Ranking GRI

2nd

Ranking GRI

3rd

Ranking GRI

1 0.4 0.4 0.5 0.5 0.5 0.5

2 0.1 0.2 0.5 0.4 0.5 0.5

3 0.3 0.3 0.4 0.4 0.5 0.5

147

4 0.3 0.3 0.5 0.1 0.5 0.5

5 0.3 0.3 0.4 0.5 0.5 0.5

6 0.1 0.2 0.4 0.2 0.5 0.5

7 0.1 0.1 0.5 0.5 0.5 0.5

8 0.3 0.3 0.4 0.2 0.3 0.3

9 0.3 0.3 0.5 0.5 0.3 0.3

10 0.2 0.2 0.2 0.5 0.5 0.5

11 0.1 0.2 0.2 0.5 0.5 0.5

12 0.3 0.3 0.2 0.4 0.3 0.3

13 0.3 0.3 0.4 0.5 0.3 0.5

14 0.3 0.3 0.5 0.5 0.5 0.5

15 0.2 0.3 0.4 0.4 0.3 0.5

16 0.4 0.4 0.5 0.5 0.3 0.3

17 0.3 0.3 0.5 0.5 0.3 0.4

18 0.4 0.4 0.4 0.4 0.5 0.5

19 0.2 0.2 0.5 0.5 0.5 0.5

20 0.3 0.3 0.4 0.4 0.5 0.5

21 0.2 0.2 0.4 0.4 0.5 0.5

22 0.3 0.3 0.4 0.4 0.5 0.5

23 0.3 0.1 0.2 0.2 0.3 0.5

24 0.5 0.5 0.3 0.5 0.5 0.5

25 0.2 0.2 0.5 0.5 0.5 0.5

26 0.3 0.3 0.4 0.5 0.5 0.5

27 0.3 0.3 0.3 0.5 0.5 0.5

28 0.1 0.1 0.3 0.5 0.5 0.5

29 0.5 0.5 0.5 0.5 0.5 0.5

30 0.5 0.1 0.3 0.3 0.5 0.5

31 0.5 0.5 0.3 0.3 0.3 0.3

32 0.4 0.4 0.4 0.4 0.5 0.5

33 0.4 0.4 0.5 0.3 0.1 0.1

148

34 0.5 0.5 0.2 0.2 0.5 0.5

35 0.5 0.5 0.3 0.5 0.4 0.4

36 0.4 0.4 0.5 0.5 0.5 0.5

37 0.5 0.5 0.5 0.5 0.5 0.2

38 0.3 0.3 0.5 0.5 0.5 0.5

39 0.2 0.2 0.4 0.4 0.5 0.5

40 0.2 0.3 0.5 0.1 0.5 0.5

41 0.4 0.4 0.5 0.5 0.2 0.3

42 0.3 0.3 0.5 0.2 0.2 0.4

43 0.4 0.4 0.5 0.5 0.2 0.3

44 0.3 0.3 0.5 0.2 0.5 0.5

45 0.2 0.2 0.5 0.5 0.5 0.5

46 0.3 0.3 0.5 0.5 0.3 0.3

47 0.3 0.1 0.5 0.5 0.5 0.5

48 0.3 0.3 0.5 0.4 0.3 0.5

49 0.3 0.3 0.3 0.5 0.5 0.5

50 0.3 0.3 0.2 0.5 0.5 0.5

Example results of module 5: programme completion identification


No. 1st Test

ANN CHD SVM 2nd Test

ANN CHD SVM 3rd Test

ANN CHD SVM

1 0 0 0 0 0 0 0 0 0 0 1 0

2 0 0 0 0 0 0 0 0 0 0 0 0

3 0 0 0 0 0 1 1 1 0 1 1 1

4 0 1 1 1 0 0 1 0 0 0 1 0

5 0 0 0 0 0 0 0 0 0 1 1 1

6 0 0 0 0 0 1 1 1 0 0 0 0

149

7 0 0 0 0 0 0 0 0 0 0 0 0

8 0 0 0 0 0 0 0 0 0 0 0 0

9 0 0 0 0 0 0 0 0 0 1 1 1

10 0 1 1 1 0 0 0 0 0 1 1 1

11 0 0 0 0 0 1 1 1 0 0 0 0

12 0 0 0 0 0 0 0 0 0 0 0 0

13 0 1 1 1 0 0 1 0 0 1 1 1

14 0 1 1 1 0 1 0 1 0 1 1 1

15 0 0 0 0 0 0 0 0 0 1 1 1

16 0 0 1 0 0 0 0 0 0 0 0 0

17 0 0 0 0 0 1 1 1 0 0 0 0

18 0 0 0 0 0 0 0 0 0 0 0 0

19 0 1 1 1 0 0 0 0 0 1 1 1

20 0 0 0 0 0 0 0 0 0 0 0 0

21 0 1 1 1 0 0 0 0 0 0 0 0

22 0 0 0 0 0 1 1 1 0 1 1 1

23 0 1 1 1 0 0 0 0 0 0 0 0

24 0 0 0 0 0 1 1 1 0 0 0 0

25 0 0 0 0 0 0 0 0 0 0 0 0

26 0 0 0 0 0 0 0 0 0 0 0 0

27 0 0 0 0 0 0 0 0 0 0 0 0

28 0 1 1 1 0 0 0 0 0 0 0 0

29 0 0 0 0 0 0 0 0 0 0 0 0

30 0 0 0 0 0 0 0 0 0 0 0 0

31 0 0 0 0 0 0 0 0 0 0 0 0

32 0 0 0 0 0 0 0 0 0 0 0 0

33 0 0 0 0 0 0 0 0 0 0 0 0

34 0 0 0 0 0 0 0 0 0 0 0 0

35 0 0 0 0 0 0 0 0 0 0 0 0

36 0 1 1 1 0 1 1 1 0 0 0 0

150

37 0 0 0 0 0 0 0 0 0 0 1 0

38 0 0 0 0 0 0 0 0 0 0 0 0

39 0 1 1 1 0 0 0 0 0 0 0 0

40 0 0 1 0 0 1 1 1 0 0 0 0

41 0 0 0 0 0 0 0 0 0 0 0 0

42 0 0 0 0 0 1 1 0 0 0 0 0

43 0 0 0 0 0 0 0 0 0 0 0 0

44 0 1 1 1 0 0 0 0 0 0 0 0

45 0 0 0 0 0 0 0 0 0 0 0 0

46 0 0 0 0 0 0 0 0 0 0 0 0

47 0 0 0 0 0 0 0 0 0 0 0 0

48 0 0 0 0 0 1 1 1 0 0 0 0

49 0 0 0 0 0 0 0 0 0 0 0 0

50 0 1 1 1 0 0 0 0 0 0 0 0

Cluster 1 of the ANN, CHAD and SVM models

No. 1st

Test ANN CHD SVM

2nd

Test ANN CHD SVM

3rd

Test ANN CHD SVM

1 0 0 0 0 0 0 0 0 0 0 0 0

2 0 0 0 0 1 1 1 1 0 0 0 0

3 0 0 0 0 0 0 0 0 0 0 0 0

4 0 0 0 0 0 1 1 1 0 0 0 0

5 0 1 1 0 0 1 0 1 0 1 1 0

6 0 0 0 0 0 0 0 0 0 0 0 0

7 0 0 0 0 0 0 0 0 0 0 0 0

8 0 0 0 0 0 0 0 0 0 0 0 0

9 0 1 1 0 0 0 0 0 0 1 1 0

10 0 0 0 0 0 0 0 0 0 0 0 0

151

11 0 1 1 1 0 0 1 0 0 1 1 1

12 0 0 0 0 0 0 0 0 0 0 0 0

13 0 0 0 0 0 0 0 0 0 0 0 0

14 0 0 0 0 0 0 0 0 0 0 0 0

15 0 0 0 0 0 0 0 0 0 0 0 0

16 0 0 0 0 0 0 0 0 0 0 0 0

17 0 0 0 0 0 0 0 0 0 0 0 0

18 0 0 0 0 0 0 0 0 0 0 0 0

19 0 0 0 0 0 0 0 0 0 0 0 0

20 0 0 0 0 0 0 0 0 0 0 0 0

21 0 0 0 0 0 0 0 0 0 0 0 0

22 0 0 0 0 0 0 0 0 0 0 0 0

23 0 0 0 0 0 0 0 0 0 0 0 0

24 0 0 0 0 0 0 0 0 0 0 0 0

25 0 0 0 0 0 0 0 0 0 0 0 0

26 0 0 0 0 0 0 0 0 0 0 0 0

27 0 0 0 0 0 0 0 0 0 0 0 0

28 0 0 0 0 0 0 0 0 0 0 0 0

29 0 1 1 1 0 0 0 0 0 1 1 1

30 1 1 1 1 0 0 0 0 1 1 1 1

Cluster 2 of the ANN, CHAD and SVM models

No. 1st Test

ANN CHD SVM 2nd Test

ANN CHD SVM 3rd Test

ANN CHD SVM

1 1 1 0 1 0 0 0 0 0 0 0 0

2 0 0 0 0 0 0 0 0 0 0 0 0

3 0 0 0 0 0 0 0 0 0 0 0 0

4 0 0 0 0 0 1 1 1 0 0 0 0

5 0 1 1 0 0 1 0 1 0 1 1 0

152

6 0 0 0 0 0 0 0 0 0 0 0 0

7 0 0 0 0 0 0 0 0 0 0 0 0

8 0 0 0 0 0 0 0 0 0 0 0 0

9 0 1 1 0 0 0 0 0 0 1 1 0

10 0 0 0 0 0 0 0 0 0 0 0 0

11 0 1 1 1 0 0 1 0 0 1 1 1

12 0 0 0 0 0 0 0 0 0 0 0 0

13 0 0 0 0 0 0 0 0 0 0 0 0

14 0 0 0 0 0 0 0 0 0 0 0 0

15 0 0 0 0 0 0 0 0 0 0 0 0

16 0 0 0 0 0 0 0 0 0 0 0 0

17 1 1 1 1 0 0 0 0 1 1 1 1

18 0 0 0 0 0 0 0 0 0 0 0 0

19 0 0 0 0 0 0 0 0 0 0 0 0

20 0 0 0 0 0 0 0 0 0 0 0 0

21 0 0 0 0 0 0 0 0 0 0 0 0

22 0 0 0 0 0 0 0 0 0 0 0 0

23 0 0 0 0 0 0 0 0 0 0 0 0

24 1 1 1 1 1 1 1 1 1 1 1 1

25 0 0 0 0 0 0 0 0 0 0 0 0

26 0 0 0 0 0 0 0 0 0 0 0 0

27 0 0 0 0 0 0 0 0 0 0 0 0

28 0 0 0 0 0 0 0 0 0 0 0 0

29 0 1 1 1 0 0 0 0 0 1 1 1

30 0 0 0 0 0 0 0 0 0 0 0 0

153

Example results of module 6: postgraduate study identification


No. 1st M.Level

ANN CHD SVM 2nd M.Level

ANN CHD SVM 3rd M.Level

ANN CHD SVM

1 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

2 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

3 0.1 0.1 0.1 0.1 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2

4 0.2 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.2

5 0.3 0.2 0.2 0.3 0.2 0.2 0.2 0.2 0.3 0.4 0.3 0.3

6 0.3 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.2 0.1 0.1 0.2

7 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2

8 0.3 0.2 0.2 0.3 0.1 0.1 0.1 0.1 0.3 0.3 0.3 0.3

9 0.2 0.1 0.1 0.2 0.2 0.1 0.1 0.2 0.3 0.2 0.2 0.3

10 0.3 0.2 0.2 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.3 0.4

11 0.1 0.1 0.1 0.1 0.4 0.3 0.4 0.4 0.2 0.2 0.2 0.2

12 0.1 0.3 0.1 0.1 0.2 0.1 0.1 0.2 0.3 0.3 0.3 0.3

13 0.1 0.2 0.1 0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.2 0.3

14 0.1 0.1 0.1 0.1 0.3 0.2 0.2 0.3 0.4 0.3 0.3 0.4

15 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

16 0.2 0.2 0.2 0.2 0.2 0.1 0.1 0.2 0.3 0.2 0.2 0.3

17 0.2 0.2 0.2 0.2 0.2 0.1 0.1 0.2 0.2 0.2 0.2 0.2

18 0.4 0.4 0.3 0.4 0.3 0.2 0.2 0.3 0.2 0.1 0.1 0.2

19 0.2 0.3 0.2 0.2 0.2 0.1 0.1 0.2 0.1 0.1 0.1 0.1

20 0.3 0.2 0.2 0.3 0.2 0.2 0.1 0.2 0.1 0.1 0.1 0.1

21 0.3 0.2 0.2 0.3 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.1

22 0.3 0.2 0.2 0.3 0.2 0.1 0.1 0.2 0.2 0.2 0.2 0.2

23 0.4 0.3 0.4 0.4 0.2 0.1 0.1 0.2 0.1 0.1 0.1 0.1

24 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.3 0.3 0.3 0.3

25 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2

154

26 0.2 0.2 0.1 0.2 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2

27 0.2 0.1 0.1 0.2 0.1 0.2 0.1 0.1 0.1 0.1 0.1 0.1

28 0.3 0.3 0.3 0.3 0.2 0.1 0.1 0.2 0.2 0.2 0.2 0.2

29 0.2 0.2 0.2 0.2 0.2 0.1 0.1 0.2 0.3 0.3 0.3 0.3

30 0.2 0.1 0.1 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

31 0.3 0.2 0.2 0.3 0.1 0.1 0.1 0.2 0.3 0.3 0.3 0.3

32 0.3 0.2 0.2 0.3 0.2 0.2 0.2 0.3 0.1 0.1 0.1 0.1

33 0.1 0.2 0.1 0.1 0.2 0.2 0.2 0.2 0.3 0.2 0.2 0.3

34 0.2 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.2

35 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

36 0.1 0.2 0.1 0.1 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.1

37 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.1 0.1 0.1 0.2

38 0.2 0.3 0.2 0.2 0.2 0.1 0.1 0.2 0.3 0.4 0.3 0.3

39 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

40 0.1 0.2 0.1 0.1 0.3 0.3 0.3 0.3 0.2 0.1 0.1 0.1

41 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2

42 0.2 0.3 0.2 0.2 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.2

43 0.4 0.4 0.4 0.4 0.1 0.2 0.1 0.2 0.4 0.4 0.4 0.4

44 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.2

45 0.1 0.2 0.1 0.1 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

46 0.2 0.1 0.1 0.2 0.1 0.1 0.1 0.2 0.1 0.1 0.1 0.1

47 0.2 0.1 0.1 0.2 0.1 0.2 0.1 0.2 0.3 0.3 0.3 0.3

48 0.2 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.4 0.4 0.4 0.4

49 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.3 0.2 0.2 0.3

50 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2

155


No. 1st

Test ANN CHD SVM

2nd

Test ANN CHD SVM

3rd

Test ANN CHD SVM

1 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

2 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

3 0.1 0.1 0.1 0.1 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2

4 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.1

5 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.3 0.4 0.3 0.4

6 0.3 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.2 0.1 0.1 0.1

7 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2

8 0.3 0.2 0.2 0.2 0.1 0.1 0.1 0.1 0.3 0.3 0.3 0.3

9 0.2 0.1 0.1 0.1 0.2 0.1 0.1 0.1 0.3 0.2 0.2 0.2

10 0.3 0.2 0.2 0.2 0.4 0.4 0.4 0.4 0.4 0.4 0.3 0.4

11 0.1 0.1 0.1 0.1 0.4 0.3 0.4 0.4 0.2 0.2 0.2 0.2

12 0.1 0.3 0.1 0.1 0.2 0.1 0.1 0.1 0.3 0.3 0.3 0.3

13 0.1 0.2 0.1 0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.2 0.3

14 0.1 0.1 0.1 0.1 0.3 0.2 0.2 0.2 0.4 0.3 0.3 0.3

15 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

16 0.2 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.3 0.2 0.2 0.2

17 0.2 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.2 0.2 0.2 0.2

18 0.4 0.4 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.1 0.1 0.1

19 0.2 0.3 0.2 0.2 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1

20 0.3 0.2 0.2 0.2 0.2 0.2 0.1 0.2 0.1 0.1 0.1 0.1

21 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.1

22 0.3 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.2 0.2 0.2 0.2

23 0.4 0.3 0.4 0.4 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1

24 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.3 0.3 0.3 0.3

25 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2

26 0.2 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2

156

27 0.2 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.1 0.1

28 0.3 0.3 0.3 0.3 0.2 0.1 0.1 0.1 0.2 0.2 0.2 0.2

29 0.2 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.3 0.3 0.3 0.3

30 0.2 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

31 0.3 0.2 0.2 0.2 0.1 0.1 0.1 0.1 0.3 0.3 0.3 0.3

32 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.1

33 0.1 0.2 0.1 0.1 0.2 0.2 0.2 0.2 0.3 0.2 0.2 0.2

34 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.1

35 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

36 0.1 0.2 0.1 0.1 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.1

37 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.1 0.1 0.1 0.1

38 0.2 0.3 0.2 0.2 0.2 0.1 0.1 0.1 0.3 0.4 0.3 0.4

39 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

40 0.1 0.2 0.1 0.1 0.3 0.3 0.3 0.3 0.2 0.1 0.1 0.1

41 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2

42 0.2 0.3 0.2 0.3 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.1

43 0.4 0.4 0.4 0.4 0.1 0.2 0.1 0.2 0.4 0.4 0.4 0.4

44 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2

45 0.1 0.2 0.1 0.1 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

46 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

47 0.2 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.3 0.3 0.3 0.3

48 0.2 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.4 0.4 0.4 0.4

49 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.3 0.2 0.2 0.2

50 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2

157

Results of the MANN-OWSR, SVM and ensemble in overall GPA and GPA each semester

No. 1st Test

SVM OWSR Ens 2nd Test

SVM OWSR Ens 3rd Test

SVM OWSR Ens

1 0.2 0.2 1 0 0.2 1 0 0.2 1 0 0.2 0.2

2 0.1 0.1 1 0 0.1 1 0 0.1 1 0 0.1 0.1

3 0.2 0.2 1 0 0.2 1 0 0.2 1 0 0.2 0.2

4 0.1 0.1 1 0 0.1 1 0 0.1 1 0 0.1 0.1

5 0.2 0.1 0 0.1 0.1 0 0.1 0.1 0 0.1 0.2 0.1

6 0.1 0.1 1 0 0.1 1 0 0.1 1 0 0.1 0.1

7 0.1 0.1 1 0 0.1 1 0 0.1 1 0 0.1 0.1

8 0.2 0.2 1 0 0.2 1 0 0.2 1 0 0.2 0.2

9 0.3 0.2 0 0.1 0.2 0 0.1 0.2 0 0.1 0.3 0.2

10 0.1 0.1 1 0 0.1 1 0 0.1 1 0 0.1 0.1

11 0.2 0.2 1 0 0.2 1 0 0.2 1 0 0.2 0.2

12 0.3 0.3 1 0 0.3 1 0 0.3 1 0 0.3 0.3

13 0.2 0.1 0 0.1 0.1 0 0.1 0.1 0 0.1 0.2 0.1

14 0.2 0.2 1 0 0.2 1 0 0.2 1 0 0.2 0.2

15 0.2 0.2 1 0 0.2 1 0 0.2 1 0 0.2 0.2

16 0.1 0.1 1 0 0.1 1 0 0.1 1 0 0.1 0.1

17 0.4 0.4 1 0 0.4 1 0 0.4 1 0 0.4 0.4

18 0.2 0.3 0 0.1 0.2 1 0 0.3 0 0.1 0.2 0.3

19 0.2 0.2 1 0 0.2 1 0 0.2 1 0 0.2 0.2

20 0.2 0.2 1 0 0.2 1 0 0.2 1 0 0.2 0.2

21 0.2 0.1 0 0.1 0.1 0 0.1 0.1 0 0.1 0.2 0.1

22 0.4 0.4 1 0 0.4 1 0 0.4 1 0 0.4 0.4

23 0.2 0.2 1 0 0.1 0 0.1 0.1 0 0.1 0.2 0.2

24 0.3 0.2 0 0.1 0.2 0 0.1 0.2 0 0.1 0.3 0.2

158

25 0.2 0.1 0 0.1 0.1 0 0.1 0.1 0 0.1 0.2 0.1

26 0.3 0.4 0 0.1 0.3 1 0 0.3 1 0 0.3 0.4

27 0.2 0.2 1 0 0.2 1 0 0.2 1 0 0.2 0.2

28 0.2 0.2 1 0 0.2 1 0 0.2 1 0 0.2 0.2

29 0.1 0.1 1 0 0.1 1 0 0.1 1 0 0.1 0.1

30 0.4 0.3 0 0.1 0.3 0 0.1 0.3 0 0.1 0.4 0.3

* Please note that “ Ens” is ensemble and “CHD” is CHAID algorithms

an intelligent recommendation framework for student ... › id › eprint › 25781 › 1 ›...

Documents