addictive links: adaptive navigation support in college-level courses

Addictive Links: Adaptive Navigation Support for College-Level Courses

Peter Brusilovsky with: Sergey Sosnovsky, Michael Yudelson, Sharon Hsiao

School of Information Sciences, University of Pittsburgh

User Model

Collects information about individual user

Provides adaptation effect

Adaptive System

User Modeling side

Adaptation side

User-Adaptive Systems

Classic loop user modeling - adaptation in adaptive systems

Adaptive Software Systems

•  Intelligent Tutoring Systems –  adaptive course sequencing –  adaptive . . .

•  Adaptive Hypermedia Systems –  adaptive presentation –  adaptive navigation support

•  Adaptive Help Systems •  Adaptive . . .

Adaptive Hypermedia

•  Hypermedia systems = Pages + Links

•  Adaptive presentation

–  content adaptation

•  Adaptive navigation support

–  link adaptation

•  Direct guidance •  Hiding, restricting, disabling •  Generation •  Ordering •  Annotation •  Map adaptation

Adaptive Navigation Support

Adaptive Link Annotation

Adaptive Link Annotation: InterBook  

1. Concept role 2. Current concept state

3. Current section state 4. Linked sections state

4

3

2

1

√"

Metadata-based mechanism

Personalized Information Access

Adaptive���Hypermedia

Adaptive���IR

Web���Recommenders

Navigation Search Recommendation

Metadata-based mechanism

Keyword-based mechanism

Community-based mechanism

Adaptation Mechanisms

Adaptive Link Annotation: ScentTrails Con

tent

-bas

ed m

echa

nism

Adaptive Link Annotation: CoWeb Soc

ial n

avig

atio

n m

echa

nism

The Value of ANS

•  Lower navigation overhead – Access the content at the right time – Find relevant information faster

•  Better learning outcomes – Achieve the same level of knowledge faster – Better results with fixed time

•  Encourages non-sequential navigation

The Case of QuizPACK •  QuizPACK: Quizzes for

Parameterized Assessment of C Knowledge

•  Each question is a pattern of a simple C program. When it is delivered to a student the special parameter is dynamically instantiated by a random value within the pre-assigned borders.

•  Used mostly as a self-assessment tool in two C-programming courses

QuizPACK: Value and Problems

•  Good news: –  activity with QuizPACK significantly correlated with

student performance in classroom quizzes – Knowledge gain rose from 1.94 to 5.37

•  But: – Low success rate - below 40% – The system is under-used (used less than it deserves)

•  Less than 10 sessions at average •  Average Course Coverage below 40%

Adding Motivation (2003) •  Students need some better motivation to work with non-

mandatory educational content… •  Added classroom quizzes:

–  Five randomly initialized questions out of 20-30 questions assigned each week

•  Good results - activity, percentage of active questions, course coverage - all increased 2-3 times! But still not as much as we want. Could we do better?

•  Maybe students bump into wrong questions? Too easy? Too complicated? Discouraging…

•  Let’s try something that worked in the past

Questions of the current quiz, served by QuizPACK

List of annotated links to all quizzes available for a student in the current course

Refresh and help icons

QuizGuide = QuizPACK+ANS

Demo: QuizPack

•  KT Portal - http://adapt2.sis.pitt.edu/cbum/

•  Create your account or use test / test

Topic-Based Adaptation

Concept A

Concept B

Concept C

n  Each topic is associated with a number of educational activities to learn about this topic

n  Each activity classified under 1 topic

QuizGuide: Adaptive Annotations •  Target-arrow abstraction:

–  Number of arrows – level of knowledge for the specific topic (from 0 to 3). Individual, event-based adaptation.

–  Color Intensity – learning goal (current, prerequisite for current, not-relevant, not-ready). Group, time-based adaptation.

n  Topic–quiz organization:

QuizGuide: Architecture

User Model

QuizGuide QuizPACK

Student's Browser

QuizGuide: Motivation

•  Adaptive navigation support increased student's activity and persistence of using the system

Average activity

050

100150200250300

2002 2003 2004

Average num. of sessions

0

5

10

15

20

2002 2003 2004

Average course coverage

0%10%20%30%40%50%60%

2002 2003 2004

Active students

0%

20%

40%

60%

80%

100%

2002 2003 2004

n  Within the same class QuizGuide session were much longer than QuizPACK sessions: 24 vs. 14 question attempts at average.

n  Average Knowledge Gain for the class rose from 5.1 to 6.5

QuizGuide: Success Rate?

n Well, that worked too, but the scale is not comparable

n One-way ANOVA shows that mean success value for QuizGuide is significantly larger then the one for QuizPACK: F(1, 43) = 5.07 (p-value = 0.03).

A new value of ANS?

•  The scale of the effect is too large… May be just a good luck?

• New effect after 15 years of research? • Not quite new, rather ignored and

forgotten - ELM-ART data

ELM-ART: Navigation Support

Round 2: Let’s Try it Again…

•  Maybe the effect could only be discovered in full-scale classroom studies – while past studies were lab-based?

•  Another study with the same system – QuizGuide+QuizPACK vs. QuizPACK

•  A study with another system using similar kinds of adaptive navigation support – NavEx+WebEx vs. WebEx

•  NavEx - a value-added ANS front-end for WebEx - interactive example exploration system

WebEx - Code Examples

Concept-based student modeling

Example 2 Example M

Example 1

Problem 1

Problem 2 Problem K

Concept 1

Concept 2

Concept 3

Concept 4

Concept 5

Concept N

Examples

Problems

Concepts

NavEx = WebEx + ANS

Does it work?

•  The increase of the amount of work for the course

Clicks - Overall

0

50

100

150

200

250

300

Non-adaptive Adaptive

Examples

Quizzes

Lectures - Overall

0

2

4

6

8

10

12

Non-adaptive Adaptive

Examples

Quizzes

Learning Objects - Overall

0

5

10

15

20

25

30

Non-adaptive Adaptive

Examples

Quizzes

Is It Really Addictive?

•  Are they coming more often? Mostly, but there is no stable effect

•  But when they come, they stay… like with an addictive game

Clicks - Per Session

0

5

10

15

20

Non-adaptive Adaptive

Examples

Quizzes

Learning Objects - Per

Session

0

1

2

3

4

Non-adaptive Adaptive

Examples

Quizzes

Why It Is Working?

•  Progress-based annotation – Displays the progress achieved so far – Does it work as a reward mechanism? – Open Student Modeling

•  State-based annotation – Not useful, ready, not ready – Access activities in the right time – Appropriate difficulty, keep motivation

A Deeper Look

The Diversity of Work

•  C-Ratio: Measures the breadth of exploration •  Goal distance: Measures the depth

Self-motivated Work - C-Ratio

(%)

0

0.2

0.4

0.6

Non-adaptive Adaptive

Quizzes

Examples

Self-motivated Work - Goal

Distance (LO's)

0

5

10

15

20

Non-adaptive Adaptive

Quizzes

Examples

Round 3: Trying another domain…

•  Is it something relevant to C programming or to simple kind of content?

•  New changes: – SQL Programming instead of C – Programming problems (code writing) instead of

questions (code evaluation) – Comparison of concept-based and topic-based

mechanisms in the same domain and with the same kind of content

•  SQL-KnoT delivers online SQL problems, checks student’s answers and provides a corrective feedback

•  Every problem is dynamically generated using a template and a set of databases

•  All problems have been assigned to 1 of the course topics and indexed with concepts from the SQL ontology

SQL Knowledge Tester

•  To investigate possible influence of concept-based adaptation in the present of topic-based adaptation we developed two versions of QuizGuide:

Topic-based Topic-based+Concept-Based

Concept-based vs Topic-based ANS

•  Two Database Courses (Fall 2007): §  Undergraduate (36 students) §  Graduate (38 students)

•  Each course divided into two groups: §  Topic-based navigation §  Topic-based + Concept-Based Navigation

•  All students had access to the same set of SQL-KnoT problems available in adaptive (QuizGuide) and in non-adaptive mode (Portal)

Study Design

•  Total number of attempts made by all students: in adaptive mode (4081), in non-adaptive mode (1218)

•  Students in general were much more willing to access the adaptive version of the system, explored more content with it and to stayed with it longer:

Questions

0255075100

Quizzes

0510152025 Topics

0123456

Sessions

012345 Session Length

0510152025

Adaptive Non-adaptive

It works again! Like magic…

•  Did concept-based adaptation increase the magnitude of the motivational effect? §  No significant difference in the average numbers of attempts,

problems answered, topics explored §  No significant difference in the session length

•  Was there any other observable difference? §  Average number of attempts per question

§  Resulting Knowledge Level (averaged across all concepts)

Attempts per question

0

24

6

Knowledge level

0

0.2

0.4

0.6

Combined Topic-based

Concept-based ANS: Added Value?

•  Question-based Patterns:

•  Topic-based Patterns:

Repetition 0: incorrect previous

attempt

Repetition 1: correct previous

attempt

Sequence Repetition Go-Back Skipping

Next Topic Jump-Forward Jump-Backward Combined

Pattern Analysis

Topic Pattern Distribution

0%10%20%30%40%50%60%70%80%

Next-Topic Jump-Fwrd Jump-Bkwd

Combined (undegraduate)Combined (graduate)Topic-based (undegraduate)Topic-based (graduate)

Pattern Analysis (2)

Question Pattern Distribution

0%

10%

20%

30%

40%

50%

60%

Sequence Go-Back Skipping Repetition-0 Repetition-1

Combined (undegraduate)Combined (graduate)Topic-based (undegraduate)Topic-based (graduate)

Pattern Analysis (3)

•  Difference in the ratio of Repetition1 pattern explains: §  difference in the average number of attempts per question §  difference in the cumulative resulting knowledge level

§  Students repeat the same question again and again: §  They “get addicted“ to the concept-based icons §  Is it a good thing for us?

− YES – they react to the navigational cues, they work more − NO – we expect them to concentrate on those questions where they have

smaller progress instead of drilling in the same question

Discussion

Round 4: The Issue of Complexity •  Let’s now try it for Java… •  What is the research goal? •  Java is a more sophisticated domain than C

– OOP versus Procedural – Higher complexity

•  Will it work for complex questions?

•  Will it work similarly? 0% 20% 40% 60% 80% 100%

C

Java

language complexity

Easy

Moderate

Hard

Meet QuizJET!

Naviga&on  Area Presenta&on  Area

JavaGuide

!! !! JavaGuide

(Fall 2008) QuizJET

(Spring 2008) !! parameters (n=22) (n=31)

Overall User Statistics

Attempts 125.50 41.71 Success Rate 58.31% 42.63% Distinct Topics 11.77 4.94 Distinct Questions 46.18 17.23

Average User Session Statistics

Attempts 30.34 21.50 Distinct Topics 2.85 2.55 Distinct Questions 11.16 8.88

Magic… Here We Go Again!

•  Significantly more Attempts on the easy questions in JavaGuide than in QuizJET

•  F(1, 153) = 7.081, p = .009, partial η2 = .044

The Effect Depends on Complexity

•  Significant higher Success Rate

•  F(1, 153) = 40.593, p <.001, partial η2 = .210 •  On average 2.5 times

more likely to answer a quiz correctly with adaptive navigation support

68.73% 67.00%

39.32% 38.00% 28.23%

11.90% 0%

20%

40%

60%

80%

100%

Easy Moderate Hard Su

cces

s Rat

e

Complexity Level

Success Rate

JavaGuide

QuizJET

Different Pattern For Success Rate

0.94

0.61

0.29

1.85

1.01

0.44

0

0.5

1

1.5

2

Easy Moderate Hard

Att

empt

s/Q

uest

ion

Complexity Level

Average Attempts per Question

68.73% 67.00%

39.32% 38.00% 28.23%

11.90% 0%

20%

40%

60%

80%

100%

Easy Moderate Hard

Succ

ess R

ate

Complexity Level

Success Rate

JavaGuide

QuizJET

Prerequisite-based guidance prepared students to attempt complex questions after exploring easier ones

Putting it Together…

Round 5: Social Navigation

•  Concept-based and topic-based navigation support work well to increase success and motivation

•  Knowledge-based approaches require some knowledge engineering – concept/topic models, prerequisites, time schedule

•  In our past work we learned that social navigation – guidance extracted from the work of a community of learners – might replace knowledge-based guidance

•  Social wisdom vs. knowledge engineering

Open Social Student Modeling

•  Key ideas – Assume simple topic-based design – No prerequsites or concept modeling – Show topic- and content- level knowledge progress of

a student in contrast to the same progress of the class •  Main challenge

– How to design the interface to show student and class progress over topics?

– We went through several attempts

QuizMap

53

Parallel Introspective Views

54

0

40

80

120

160

QuizJET+IV QuizJET+Portal JavaGuide

Attempts

Attempts

Results: Progress

Class vs. Peers

•  Peer progress was important, students frequently accessed content using peer models

•  The more the students compared to their peers, the higher post-quiz scores they received (r= 0.34 p=0.004)

•  Parallel IV didn’t allow to recognized good peers before opening the model

•  Progressor added clear peer progress

F(1,32)= 11.303, p<.01

71.35%

42.63% 58.31%

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

QuizJET+IV QuizJET+Portal JavaGuide

Success Rate

Success Rate

Results: Success

Why It Is Important?

•  Many systems demonstrated their educational effectiveness in a lab-like settings: once the students are pushed to use it - it benefits their learning

•  However, once released to real classes, these systems are under-used - most of them offer additional non-mandatory learning opportunities

•  “Students are only interested in points and grades” •  Convert all tools into credit-bearing activities? •  Or use alternative approaches to increase motivation

Progressor

59

The Value of Peers

205.73

113.05

80.81

125.5

0

50

100

150

200

250

Attempts

Progressor

QuizJET+IV

QuizJET+Portal

JavaGuide

68.39% 71.35%

42.63%

58.31%

0.00%

20.00%

40.00%

60.00%

80.00%

Success Rate

Progressor

QuizJET+IV

QuizJET+Portal

JavaGuide

The Secret

Take-home messages

•  A combination of progress-based and state-based adaptive link annotation increases the amount and the diversity of student work with non-mandatory educational content

•  The effect is stable and the scale of it is quite large

•  Properly organized Social Navigation might be at least as successful as the knowledge-based

•  Requires a long-term classroom study to observe

What we are doing now?

•  Exploring new generation of open social modeling tools in wide variety if classes and domains from US to Nigeria –  Interested to be a pilot site?

•  Exploring more advanced guidance and modeling approaches based on large volume of social data

•  Applying open social modeling to motivate readings

Acknowledgements

•  Joint work with – Sergey Sosnovsky – Michael Yudelson – Sharon Hsiao

•  Pitt “Innovation in Education” grant •  NSF Grants

– EHR 0310576 –  IIS 0426021 – CAREER 0447083

Try It!

•  http://adapt2.sis.pitt.edu/kt/

•  Brusilovsky, P., Sosnovsky, S., and Yudelson, M. (2009) Addictive links: The motivational value of adaptive link annotation. New Review of Hypermedia and Multimedia 15 (1), 97-118.

•  Hsiao, I.-H., Sosnovsky, S., and Brusilovsky, P. (2010) Guiding students to the right questions: adaptive navigation support in an E-Learning system for Java programming. Journal of Computer Assisted Learning 26 (4), 270-283.

•  Hsiao, I.-H., Bakalov, F., Brusilovsky, P., and König-Ries, B. (2013) Progressor: social navigation support through open social student modeling. New Review of Hypermedia and Multimedia [PDF]

Read About It!