LAPTOP USE IN THE CLASSROOM: A COST/BENEFIT ANALYSIS BASED ON

PARTICIPATION

A THESIS

Presented to

The Faculty of the Department of Economics and Business

The Colorado College

In Partial Fulfillment of the Requirements for the Degree

Bachelor of Arts

By

Matthew Vargas

May 2012

Laptop Use in the Classroom: A Cost/Benefit Analysis Based on Participation

Matthew Vargas

May 2012

Economics

Abstract

The College classroom environment has changed since the advent of the personal computers.

More and more students are frequently bringing their laptops into the classroom at colleges

around the country. While those students bring their laptops to the classroom, instructors’

perceptions of laptop use continue to change. Therefore, the issue of this generation is whether

or not students understand their own perceptions of the costs and benefits of laptop use given the

costs of diminishment of learning in the classroom and the benefits of improved learning through

software and programs on the laptop. The purpose of this thesis was to determine whether or not

students whom bring their laptop to the classroom understand if their use of laptops are

improving or diminishing their learning experience based on participation. This research

surveyed six classes in the Economics Department during Block Three of the 2011-2012

Colorado College school year. The survey information was then be used for regression analysis

in order to determine dependent variable impact on the independent variables: LISTEN,

ASKQUES, ANSQUES, and DISCUSS.

KEYWORDS: (Laptops, Participation, Classroom, College, Cost/Benefit)

TABLE OF CONTENTS

ABSTRACT iii

1. CHAPTER 1: INTRODUCTION 1

1.1 Laptops in the Classroom…………………………………………... 1

1.2 Participation in the Classroom……………………………………… 2

1.3 Students Cost-Benefit Perspective…………………………………. 3

1.4 Instructors Cost-Benefit Perspective……………………………….. 3

1.5 Impact of Multitasking on Classroom Activity…………………….. 4

1.6 Survey Population…………………………………………............... 5

1.7 Summary of Chapters…………………………………………......... 6

2. CHAPTER 2: LITERATURE REVIEW 8

2.1 Multitasking…………………………………………........................ 8

2.2 Internet Use…………………………………………......................... 10

2.3 General Impact of Laptop’s in the Classroom……………………… 11

2.4 Instructors’ Attitudes on Laptop Use……………………………….. 13

2.5 Costs and Benefits of Laptop Use…………………………………... 16

2.6 Participation…………………………………………........................ 17

3. CHAPTER 3: THEORY AND METHODOLOGY 19

3.1 Methods and Theory…………………………………………........... 19

3.2 Assumptions and Justifications……………………………………... 21

4. CHAPTER 4: DATA 23

4.1 Survey Questionnaire Information………………………………….. 23

4.2 Limitations …………………………………………......................... 24

4.3 Independent Variables…………………………………………........ 24

4.4 Dependent Variables…………………………………………........... 25

4.5 Summary Statistics …………………………………………............. 26

4.6 Correlations…………………………………………......................... 29

5. CHAPTER 5: REGRESSION ANALYSIS 31

5.1 Classroom Observations…………………………………………..... 31

5.2 Regression Analysis…………………………………………............ 35

5.3 LISTEN…………………………………………............................... 37

5.4 ASKQUES………………………………………….......................... 39

5.5 ANSQUES………………………………………….......................... 42

5.6 DISCUSS…………………………………………............................ 45

6. CHAPTER 6: CONCLUSION 48

7. SOURCES CITED 55

LIST OF TABLES

4.5 Summary Statistics…………………. ………. ……………………………................... 28

4.6 Correlations……….. …………………………….…………………………………….. 30

5.2 Regression Error Checks ………………………………………………………………. 36

LIST OF FIGURES

5.3 Regression Analysis for LISTEN…………….………………………………………….. 38

5.4 Regression Analysis for ASKQUES…………………………………………………….. 40

5.5 Regression Analysis for ANSQUES………………………………………....................... 44

5.6 Regression Analysis for DISCUSS………………………………………………………. 45

1

CHAPTER I

INTRODUCTION

Laptops in the classroom

The widespread availability and use of Information and Communication

Technology (ICT) in the education world allows students to utilize their laptops as an

alternative resource for learning in the classroom. Computers provide a reliable means to

access, process, store, and share information with relative ease. With this newfound

ability to keep information on a device that enables storing and sharing of data along with

network access, the United States Census Bureau listed personal computers as the most

important technological tool in America within the ladder half of the 20th Century.

1

Students use effortless note-taking software such as Microsoft Word and have

progressively universal access to internet through Wi-Fi integration in laptops and at

college campuses’ around the country. The fact that schools have implemented Wi-Fi

internet access at their campuses has allowed students to take advantage of the use of

laptops in the classroom. The emergence of laptops as a scholarly tool in the classroom

raises the issue of multitasking; how are students learning and listening at the same time?

1 van Dijk, Jan and Ken Hacker, 2000, The Digital Divide as a Complex and Dynamic Phenomenon.,

Utrecht University and New Mexico State University.

2

This research hypothesizes: Students who use laptops in class, and particularly

those who multi-task more, participate less than students who don’t use laptops in class,

with a resulting negative impact on their learning experience (whether self-identified or

perceived by their instructor).

Participation in the Classroom

Participation in the classroom refers to open or whole-class discussion. This

allows the instructor to pose questions to the class in order to derive a conversation

among the students. In this research, participation from students was defined to include

the following actions: asking and answering questions, listening to the lecture or

discussion, and taking thoughtful and thorough notes on the class lecture. Many small

college classes, particularly at the Colorado College campus where the primary research

and data collection for this study takes place, include participation in the classroom as

part of the overall grade. Therefore, students who participate in the classroom will

ultimately receive higher participation grades then their peers who don’t participate.

With the emergence of laptops, accessibility and convenience have translated into

utilization of the internet not only as a means of classroom material, but also for social

activities such as Facebook and Instant Messenger. This has become a problem not only

for students who frequently use their laptop for non-academic activity, but also for

professors due to their distracting nature. Some professors, particularly at law schools,

have banned the use of laptops in their class because of concerns about diminishing

participation and interaction from their students. Do students weigh the costs and the

benefits of laptops before entering the classroom? This question will be analyzed

through a survey given out to students in economics classes at the Colorado College

3

campus. The results of this survey will be used to determine the level of students’

participation in six classes during the third block of the school year in the economics

department and whether participation varies with laptop usage in the classroom.

Students’ Cost-Benefit Perspective

From the perspective of the student, laptops provide an easy and comfortable tool

for in the classroom. The issue with laptop use in the classroom is the fact that students

don’t necessarily listen to the instructor’s lecture or discussion. Although laptops provide

easy and faster note-taking, there are disadvantages to engaging in other activities, such

as playing games and surfing the internet. These activities provide a tempting alternative

to actively engaging in class and students find themselves caught in a psychological

battle over whether or not multitasking is cost-beneficial to their academic needs. The

research in this thesis will analyze and identify student perceptions of laptop use based on

a cost-benefit model.

Instructors’ Cost-Benefit Perspective

From the perspective of the instructor, laptops can seem like a wall or a barrier

between teacher and student.2 Although laptops seem like an academic tool for the use of

programs like Microsoft Excel and Stata, the instructor’s perception of the student who

uses a laptop is that there are too many tempting alternatives to class related activities at

their disposal. Instructors want to see their students as active listeners in their classes, but

2Sarah Lohnes and Charles Kinzer, “Questioning Assumptions About Students' Expectations for

Technology in College Classrooms,” Innovate: Journal of Online Education 3, no. 5 (April 2007): 14.

4

the often accepted and encouraged use of laptops in the classroom allows students to

multitask between the class material and social avenues of interest.3 Instructors, who are

often frustrated with the use of laptops in the classroom, ask their students to refrain from

bringing their laptops to class because of the distraction they might become.4 This

research will not only identify how instructors, particularly at the Colorado College

campus, perceive the use of laptops by themselves and their students, but also will

analyze instructors as a judge of academic productivity toward their students.

The Impact of Multitasking on Classroom Activity

Multitasking in this research refers to simultaneity of activities.5 A major concern

associated with laptop use in the classroom is the temptation of class related use and

recreational activities through internet and applications. Studies show that multitasking

not only affects overall performance of the mind, but also affects performance in the

classroom.6 The problem of multitasking makes laptop use in the classroom seem like a

dangerous endeavor for a student’s academic performance. Yet, the perception of a

number of students who use laptops in the classroom is that their use of laptops improves

their learning and knowledge. At the same time, the alternative theory of laptop use – that

they detract from learning and knowledge also holds true and is even accepted by some

students. If a student consciously knows that their performance and participation will

suffer if they bring a laptop to the classroom, will they still bring the laptop to the

3 Charles J. Abate, “You say multitasking like it's a good thing,” The NEA higher education journal 27, no.

5 (Fall 2008): 7-14. 4 L J. Fink, “Why We Banned Use of Laptops and "Scribe Notes" in Our Classroom,” American Journal of

Pharmaceutical Education 74, no. 6: 1-2. 5 Helene Hembrooke and Geri Gay, “The Laptop and the Lecture: The effects of Multitasking in Learning

Environments,” Journal of Computing in Higher Education 15, no. 1 (Fall 2003): 1-19. 6 Abate, 7-14

5

classroom? The research done in this thesis will tackle the question of whether or not

multitasking in the classroom effects the amount students participate. The issue of

multitasking will be surveyed and analyzed in order to determine its effects on students’

analysis of costs and benefits of laptops.

Survey Population

The survey participants for this study were current students at Colorado College, a

small school in Colorado Springs, CO. Colorado College is a small liberal arts school

with an undergraduate population of 2000 students. Classes at this school range from as

few as 5 students per class to 29. Colorado College uses a unique teaching system called

the “block system”. In this system, students only take one course at a time. Classroom

lectures are generally 3 hours long, 5 days a week on a single subject. This results in a

very different teaching and learning method than is used in almost all other schools. For

example, it is unusual for a professor to lecture for an entire 3 hour period. The class

time is often broken into smaller segments, with more discussion and project work

occurring inside of the classroom. The use of laptops by students in the classroom may

be more beneficial in this learning environment than in the more traditional semester-

based environment

This research surveyed six classes from the Economics department at the college,

including both students and professors. The Economics department at Colorado College

was the primary source of survey analysis because economics students should have an

understanding of the costs and benefits of actions. Therefore, economics students were

the ideal population in order to further understand the implications of laptop use in the

6

classroom from a cost benefit perspective. The surveys given out to students and

instructors will inquire the use and acceptance of laptops in the classroom. The questions

in particular were produced in order to gain a better understanding of individual student’s

understanding of laptop use with regard to academic accountability. As an additional

component of the research, I observed each class during the third week of the block for

about 15-20 minutes in order to gain a better understanding of classroom environment

with the presence of laptops. The classes included in the survey were: Principles of

Micro and Macroeconomics, Principles of Financial Accounting, Intermediate

Microeconomic Theory, Consumer Marketing, and Advanced Topics in Mathematical

Economics: Addiction.

Summary of Chapters

The different chapters in this thesis will relate to research done by describing

different theories from other theses and scholarly articles, showing of data relevant to the

hypothesis studied, analysis of results of the data, and the conclusions based on the

research performed in this thesis. Chapter 2 will discuss and summarize existing theories

that address the assumptions and elements essential to the problem described: classroom

participation and student laptop use. This chapter will use theories outside of this thesis

to adopt a model for the analysis of the data relevant to classroom participation and

laptop use. Chapter 3 will analyze the data collected from the class surveys taken at

Colorado College. This chapter will present the data collected from the surveys provided

by students in the economics classes and provide a summary of the empirical evidence

gathered toward accepting or rejecting the hypothesis posed based on the results of the

survey data. This chapter will also outline the questions and limitations of the data.

7

Chapter 4 will formulate an argument for or against my hypotheses posed. These

arguments will show my own personal reflection but also how these reflections could

have been different. The final chapter will provide concluding remarks which will raise

further questions about the research, and also discuss limitations to the overall research

studied.

8

CHAPTER 2

LITERATURE REVIEW

Education, as a field of study, deals mainly with methods of teaching and learning

in schools. The research done will target the use of laptops in the classroom only rather

than the entire campus. To learn affectively in a classroom setting, participation

represents an element of “learning,” and teachers use participation as a component of a

student’s overall grade. With the emergence of easy-to-use information and

communication technologies (ICT), students and teachers alike have begun changing the

ways they teach and learn in the classroom. In colleges all around the country, more and

more students bring their laptops to the classroom to supplement their learning

experience. What are the costs and benefits of students bringing their laptops to the

classroom? What type of analysis does a student go through to determine how many

units of learning they want to accumulate as opposed to units of non-academic computer

use?

9

Multitasking

Most research shows that multitasking negatively effects academic engagement

and performance. Abate (2008) confirms this analysis by examining myths surrounding

the positive nature of multitasking.1 He concludes three things: multitasking is more

inefficient then performing individual tasks, multitasking can limit our knowledge of the

tasks performed, and difference in age has no impact on multitasking learning. These

results show that multitasking limits the amount of knowledge college students can

absorb and maintain. Abate’s work shows a negative effect laptops could have during

lecture or discussion classes.

Paridon and Kaufmann (2010) discuss the repercussion of multitasking as it

relates to performance.2 The methodology of this experiment was to determine the

effects of multitasking as it relates to performance in lane changes and two work related

tasks. The results found that multitasking generates mistakes and mental strain. This

conclusion is important for this thesis because the purpose of research is to measure

multitasking and its effects on performance in the classroom related to participation.

Kraushaar and Novak examine the effects of laptop use and multitasking in the

classroom during lectures.3 The research done in this article defines and determines the

use of multitasking while in lecture. Krausbaar and Novak assess the effects of laptops in

1 Charles J. Abate, “You say multitasking like it's a good thing,” The NEA higher education journal 27, no.

5 (Fall 2008): 7-14. 2 Hiltraut M Paridon and Marlen Kaufmann, “Multitasking in work-related situations and its relevance for

occupational health and safety: Effects on performance, subjective strain and physiological parameters,”

Europe’s Journal of Psychology 6, no. 4 (November 2010): 110-124.

3 James M Kraushaar and David C. Novak, “Examining the affects of student multitasking with laptops

during the lecture,” Journal of Information Systems Education 21, no. 2: 241-251.

10

the classroom based on surveys conducted with 97 students in three different sections at

the junior level at the university. They collected data on gender, GPA, SAT scores, and

the university admission score. The research concluded that students who frequently

multitasked with software had lower academic performance; students who multitasked

longer had lower academic scores, and students with high ratios of multitasking showed

lower academic scores. The methodology of Kraushaar and Novak’s research is relevant

to the research done in this thesis because they provided surveys to students in their

classroom, similar to what this thesis will accomplish as a means of data collection. The

result of Kraushaar and Novak’s research is relevant because part of the analysis done

was based on the amount of multitasking in the classroom during lectures/discussion.

However, this thesis will fill an important void in this body of research by determining

the effects of multitasking on the perceptions of the teachers and students. Kraushaar and

Novak only research the effects of multitasking from the perspective of the student. This

thesis will hope to fill the gap from the perspective of the teacher and cross analyze that

with the perspectives of the students. This thesis will also attempt to determine the cost-

benefit analysis of bringing a laptop to class with the purpose of multitasking.

Internet Use

Research on Internet use among students at college generally shows that internet

use connects to student health and academic performance. Knowledge in this area shows

internet use can have adverse affects on sleeping patterns, behavior, and academic

11

success. These issues can result in addictive behavior, withdrawal symptoms, negative

effects on social and study activities, and uncontrollability.4

Anderson analyzes the affects of excessive internet use among students at the

college level.5 Anderson surveys students to determine internet dependency. Excessive

use of the internet means limited use of the internet without health or academic

repercussions. This research shows that different majors exhibit larger or smaller

amounts of internet use. Economics and business, which had 152 surveyed students,

exhibited about 88 minutes of internet use per day. In the research done by Anderson, the

focus was on the amount of internet use regardless of where students were using the

internet most: the classroom, dorm room, library, etc. This thesis will hope fill this gap

by taking into account specifically economics students as well as internet use in

economics classes. In this thesis, the focus will be on internet use only when it occurs in

the classroom, and its impact on class performance.

There has also been analysis on internet addiction. Whang, Lee, and Chang

(2003) discuss internet use patterns in Korea.6 Although the base of this research is not

American, there is still relevant data and discussion in this article. There are those who

use the internet as a means of social activity because they are not comfortable with real

life social activities. Students who use laptops in class and are not participating in

discussion could have personality issues along with a different analysis structure based on

costs and benefits. The benefits of interacting with a laptop as opposed to interaction

4 Wei Wang., Internet dependency and psychosocial maturity among college students. Ph.D. diss.,

Academic Press. 5 Keith J Anderson, “Internet use among college students: An exploratory study,” Journal of American

College Health 50, no. 1: 21-26. 6 Leo S Whang,, Sujin Lee, and Geunyoung Chang, “Internet over-users' psychological profiles: a behavior

sampling analysis on internet addiction,” CyberPsychology & Behavior 6 (November 2003): 143-150.

12

with the class could be better even if the cost is a lower possible grade. This question of

personality and different cost-benefit analysis will be explored in this thesis.

General Impact of Laptop’s in the Classroom

A number of research studies have considered the general impact of laptops in the

classroom. There are those who believe that because laptops present a resource in which

learning can be supplemental toward classroom activities, discussion, and lecture; plenty

of students would confidently say that having a laptop in class supplements their learning

experience.7 With this in mind, many students in fact bring their computers to class in

order to take notes and engage in social activities not affiliated with the academic

surrounding.

Kolar, Sabatini, and Fink (2002) discuss the impact of forcing students to bring

their laptop to class in the engineering department.8 They discuss whether bringing a

laptop makes a difference in student learning. They found that with the presence of

laptops, students outperformed their non-laptop counterparts in class participation and

they needed less time to do homework. This research done by Kolar, Sabatini, and Fink

addresses the same question that this thesis hopes to answer; however, this research was

done in 1998-99 which means it is outdated compared to the present (2011-2012).

Indeed compared to 1998-9 social media, gaming, and other non-academic applications

have dramatically increased in accessibility and number. The problem in this research is

7 Sharon Lauricella and Robin Kay, 2010, “Assessing laptop use in higher education classrooms: The

Laptop Effectiveness Scale (LES),” Australasian Journal of Educational Technology 26, no. 2: 151-163. 8 R. L Kolar, D. A. Sabatini, and L. D. Fink, “Laptops in the classroom: Do they make a difference?,”

Journal of Engineering Education (October 2002) : 397-401.

13

the fact that the use of computers has become more user-friendly and also more software

can be used and accessed.

Fried (2006) also asked the question of whether or not in-class laptops aid

learning or inhibit it.9 Fried found that students who brought their laptop to class were

spending considerable time multitasking and students learning was inhibited. This

research discusses laptop use in college classrooms. Some issues with the research were

the facts that teachers were not involved with the survey and there was no third party

surveyor of the classrooms.

There is also the question of how students behave towards the use of laptops in

discussion. There is a personality issue at work that could affect the use of laptops in the

classroom10

. Barkhuus (2005) discusses the issue of ‘shy’ students becoming more prone

to not participate even when computer software was implemented to show anonymity

natures in lecture/discussion. The same students seemed to be the ones participating in

the classroom whereas the shy students would only answer if they were confident that

they knew the correct answer. This raises the issue of personality becoming a

determinant of laptop use. As previously stated, the cost-benefit analysis a shy student

engages in could be much different than that of a person who is comfortable speaking in

class. Laptops could only supplement the change in cost-benefit analysis. The problem

with the research Barkhuus engages in is the fact that the teacher was not involved in the

process of surveys. The research in this thesis hopes to answer the question of the

teachers’ perception and show the cost-benefit analysis of the student.

9 Carrie B Fried, “In-class laptop use and its effects on student learning,” Computers and Education 50

(September 2006): 906-914. 10 Louise Barkhuus, "Bring your own laptop unless you want to follow the lecture": The case of wired

technology in the classroom. Ph.D. diss. UCSD, Group '05.

14

Instructors’ Attitudes on Laptop Use

Recent research shows that instructors have full knowledge of the uses of laptops

by students, and some have gone so far as to prohibit their use in the classroom. Students

have perceived the use of mobile devices as a distraction from learning in the classroom,

but they bring in the laptops anyway.11

The research shows that instructors believe that

laptops become distractions for their students, and the students also realize this fact. The

thriving and increasing use of ICT has instructors hesitant and ambivalent toward the use

of laptops in the classroom, but the use of laptops could still be beneficial in their point of

view. The methodology of this research was based on survey and observational data. The

study surveyed 127 students and 30 instructors from different departments at the

college.12

The questionnaire addressed the activities and perceptions of laptops and cell

phones during class. The questionnaire was geared toward finding answers to how

students use laptops and cell phones in the classroom, the perceptions of how that use

may improve or detract from the class activities (lecture, discussion), and also the age

distribution of students who use those devices in the classroom. Although there are no

example questions from the surveys and interviews in this research, the assumption is the

questions asked pertained to laptop use during lecture, consequences of said use, and

knowledge of the implications of such use in the classroom. In their discussion, they note

that most of the interviewed students thought that multitasking was an effective means of

learning in the classroom, balancing their use of a laptop with listening to the lecture.13

Their findings determined that instructors seem to know what the uses of these mobile

11 Ronen Hammer, Miki Rone, Amit Sharon, Tali Lankry, Yoni Huberman and Victoria Zamtsov, “Mobile

Culture in college lectures: Instructors' and students' perspectives,” Interdisciplinary Journal of E-Learning

and Learning Objects 6: 293-304. 12 Ibid 13 Ibid

15

devices are in the classroom, and that instructors not only know the uses but don’t really

enforce any discipline affiliated with the use of mobile devices for non-academic reasons.

The other findings centered around students were that not only do students know and

accurately perceive the use of mobile devices as a disruption, but also that the students in

general still believe the use of mobile devices in the classroom for non-academic

purposes represents a legitimate classroom activity. The research done by Hammer and

co. relates to the research done in this thesis because the methods of finding data and the

overall prejudice of laptop use in the classroom are immediately relevant; in addition,

their research and data collection methodology serve as a basis for a model. The research

done by Hammer is relevant to this thesis, but the main problem to be addressed is the

issue of participation as opposed to distractive application. This thesis will seek to find

not only the amount of distraction, but also the amount of participation as a result of the

use of laptops in the classroom.

There are some instructors who have banned the use of laptops in the classroom

as a result of their distractive nature.14

There were four reasons Yamamoto found for

banned laptops: distraction due to performing non-class related activities, laptops

interfere with classroom discussion by building a barrier between professor and student,

laptops encourage poor note-taking, and the negative effect on students because students

were relying on computers to answer all their questions instead of focusing on the

material. Even though his study was for a law school classroom, there are some

important discussion questions that are related to this thesis. It is clear from a summary

14 Kevin Yamamoto, “Banning Laptops in the Classroom: Is it Worth the Hassles?,” Journal of Legal

Education 57, no. 4 (December 2007): 1-46.

16

of previous research there may be some disconnect between perceptions of laptop use and

its actual effects. This thesis will determine not only the impact of laptops in the

classroom, but also to determine if the cost/benefit analysis that students and instructors

use when thinking about laptop use represents the real effects of laptops on participation.

It is clear from a summary of previous research that there may be some disconnect

between perceptions of laptop use and its actual effects. The goal of this thesis is to

determine not only the impact of laptops in the classroom, but also to determine if the

cost/benefit analysis that students and instructors use when thinking about laptop use

represents the real effects of laptops on participation.

Costs and Benefits of Laptop Use

The introduction of laptops has changed the way education professionals perceive

their use at the university and college level; particularly in the classroom, laptops pose an

intriguing and possibly useful tool for educational improvements. Students who bring

their laptops to class have a wide assortment of tools that translate to benefits and costs at

their disposal. The benefits of laptop use in the classroom range from Microsoft Word

for taking notes, Excel for math related activities, and even the use of the internet as a

tool for research on class discussion and lecture. The costs for the student are affiliated

with social activity.

Yan and Zhao (2006) discuss the implications of laptop use from the perspective

of the instructor. For instructors, the perceived cost of laptops includes difficulty

following the pacing of the class, and the benefits of laptops are enhancement of

17

students’ learning and accessibility to helpful programs like Excel and Word.15

Not only

are the costs and benefits an issue, but pressure from the school or other teachers make

the costs and benefits of laptops skewed from the perspective of the instructor. Yan and

Zhao’s research found that “Using laptops makes it difficult to manage the classroom and

students… puts more reliance on others… requires a lot of extra time…and… makes it

difficult to use existing teaching materials that have been accumulated by teachers”.

Research done by Edward Brent discusses the issue of computers in the

classroom. He concludes that from the perspective of the student, “the needs of the

students must be considered for the successful use of computers”.16

The research is

important because the costs of laptops in the classroom have to be considered with the

benefits of their use in order for the successful integration of laptops in the classroom.

Participation

Schools like the one being surveyed for the research in this thesis, Colorado

College, make participation in the classroom a high priority. Many classes make

participation up to 20 percent of the total grade. How do laptops and participation

correlate? Caron and Gely (2004) apply this question to the University of Cincinnati Law

School. They use the term “Active Learning” to describe a students’ attentiveness in the

classroom during discussion. “Active learning is based on two premises: learning by its

15

Bo Yan and Yong Zhao, 2006, Benefits or problems, what teachers care about most when integrating

technology, Michigan State University.

16Edward Brent, ”Computers in the Undergraduate Classroom: Lessons from the First 2,000 Students,”

Social Science Computer Review 17 (May 1999): 162-175.

18

nature is an active process, and different people learn in different ways.”17

They go on to

say that “Active learning recognizes that, during classroom time, students should be

engaged in behavior and activities other then listening.” These two ideas show that

participation is not only important in the classroom, but that students should be actively

participating during the discussion. Although technology provides an avenue for easy

note taking and accessible internet research, the costs of diminished active learning are

“getting in the way of active learning.” 18

This idea is important because the main

question this thesis is answering is the effect laptops have on participation in Economics

classes. Another law school situation of participation comes from the University of

Kentucky. The instructors at the university banned laptop use in the classroom because

they found students were “sending e-mail messages to one another, placing orders with

internet vendors, and doing all-sorts of other non-class-related things”.19

The main issue

these instructors presented as a benefit of laptops was the easy accessibility of class

related programs compared to the cost of less participation and active learning. The

process of banning laptops was implemented because of lack of active learning and

participation; the final discussion of the research in this thesis will recommend a policy

for laptop the regulation of laptop use in the classroom based on the findings of this

study. The research on participation as a whole is important for this research not only

because participation is the main essence of how other students not only interact with

each other, but also how instructors perceive participation as a means of understanding

and following the material in the classroom.

17 L P. Caron and Rafael Gely, “Taking Back the Law School Classroom: Using Technology to Foster

Active Student Learning,” Journal of Legal Education 54 (February 2004) : 4-38. 18 Ibid 19 L J. Fink, “Why We Banned Use of Laptops and "Scribe Notes" in Our Classroom,” American Journal of

Pharmaceutical Education 74, no. 6: 1-2.

19

CHAPTER 3:

THEORY AND METHODOLOGY

On a theoretical basis, the research done in this thesis follows and expands on the

work done by Hammer, Ronen, Sharon, Lankry, Huberman, and Zamtsov.1 Their

research was based on the study of college instructors’ and students’ attitude toward

mobile devices.

Methods and Theory

The methods and theories of this research are similar in format to those of

Hammer, Ronen, Sharon, Lankry, Huberman, and Zamstov, however the group of people

surveyed and the specific nature of laptop use in the classroom differentiate this thesis

from their work. The research of Hammer and co., they mainly looked at mobile devices’

effect on the classroom environment and learning among students. The group of people

they surveyed was from different departments of the college. This thesis deals with the

Economics department of Colorado College and will survey the students and instructors

from Block 3 of the school year. In addition, the focus of the survey data collection is

different from previous research because the majority of the analysis of data will pertain

to students and instructors cost-benefits analysis of laptop use in the classroom during

1 Ronen Hammer, Miki Rone, Amit Sharon, Tali Lankry, Yoni Huberman and Victoria Zamtsov, “Mobile

Culture in college lectures: Instructors' and students' perspectives,” Interdisciplinary Journal of E-Learning

and Learning Objects 6: 293-304.

20

class. Economics students are already learning the importance of trade-offs and the costs

and benefits of their actions, so asking this population of college students how they

analyze and determine how their use of laptop’s effects their learning experience is

essential and important toward answering the questions posed in this thesis. Economics

instructors know full well the importance of opportunity costs of giving up one good for

another, therefore their input on the utilization of laptops during their classes is important

for understanding the costs and benefits of laptops.

Surveys were distributed to students and instructors in the six economics classes

are provided during block three of the 2011-2012 school year. These surveys inquired as

to the presence and use of laptops in the classroom during lecture or discussion. Using

the answers compiled through the surveys, the data was then coded based on the use of

laptops affecting participation during class. Once coded, a regression analysis will be

done to determine which factors (if any) affect classroom participation in the classroom.

The model is based off of four different equations pulled from the data collected

from the distributed surveys. Each equation will have the same functional values

attributed to the variables such as X1 and X2; however the summation of the values will

result in four different outcomes. These equations will be determining the effects of

different activities on laptops available to students and their effects on classroom

participation.

LISTEN = βINSTRUC1 + βINSTRUC2 + βINSTRUC3 + βINSTRUC4 +

βINSTRUC5 + βINSTRUC6 + βLAPTOPUSE + βINSTRUCACC + βGENDER +

βLAPTOPIMPR + βLAPTOPDIMIN

21

ASKQUES = βINSTRUC1 + βINSTRUC2 + βINSTRUC3 + βINSTRUC4 +

βINSTRUC5 + βINSTRUC6 + βLAPTOPUSE + βINSTRUCACC + βGENDER +

βLAPTOPIMPR + βLAPTOPDIMIN

ANSQUES = βINSTRUC1 + βINSTRUC2 + βINSTRUC3 + βINSTRUC4 +

βINSTRUC5 + βINSTRUC6 + βLAPTOPUSE + βINSTRUCACC + βGENDER +

βLAPTOPIMPR + βLAPTOPDIMIN

DISCUSS = βINSTRUC1 + βINSTRUC2 + βINSTRUC3 + βINSTRUC4 +

βINSTRUC5 + βINSTRUC6 + βLAPTOPUSE + βINSTRUCACC + βGENDER +

βLAPTOPIMPR + βLAPTOPDIMIN

This was the chosen method, which is a building block off of the previous

research, because this thesis is determining the costs and benefits of laptop use in the

classroom during class and whether or not students and instructors are aware of the costs

and benefits of laptop use before bringing their laptop to class. The different methods of

data collection and theory were chosen because this thesis is more of an economic study

rather than a health or psychiatric analysis of laptop use during class.

Assumptions and Justifications

The assumptions of this research are as follows: 1) multitasking diminishes

learning, 2) laptop use diminishes learning, and 3) social applications on laptops are so

enticing to students that the student will override their own cost-benefit analysis and use

them anyway. These hypothesis are justifiable based on previous research theory which

22

determined that: 1) multitasking is an ineffective way to learn, 2) laptop use becomes a

distraction in the classroom to other students and the instructor, and 3) social use of

laptops is the primary student use in the classroom rather than note taking.2

This thesis will ask and answer a new set of theoretical questions in order to

create a better understanding, building upon previous research, of laptop use in the

classroom: 1) If the assumptions stated above are correct, do students and instructors

make a cost-benefit analysis to determine their use of laptops in the classroom? 2) What

is that cost-benefit analysis? And 3) Do students and professors act on that cost-benefit

analysis? The methods of data collection and the survey population have been developed

to enable answering these questions through an economic study to determine the cost-

benefit analysis of students and instructors.

2 Charles J. Abate, “You say multitasking like it's a good thing,” The NEA higher education journal 27, no.

5 (Fall 2008): 7-14.

23

CHAPTER 4

DATA

The data collected in this research was collected through a survey in which the

students and instructors answered questions regarding their use of laptops in the

classroom. The survey was executed over a period of five days for both the students and

the instructors, with the final data coming in over the weekend of November 18th- 20

th.

The survey results totaled 63 responses from the students, and responses from all six

instructors who were asked to participate in the survey and observation process.

Survey Questionnaire Information

The students in the classes surveyed were given a questionnaire containing

questions about the use of laptops in the classroom, whether for academic or recreational

means. There were twelve total questions for the students covering the use of laptops,

some personal background questions pertaining to sex and race, type of laptop used in the

classroom, and their instructor. However, during the data analysis, some of the questions

were pulled out due to some students not answering all the questions on the survey. This

resulted in a deduction of several questions, and the final results were based on the 10

remaining questions. Survey answers were coded and then used to determine summary

statistics, correlation, and regression analysis based on the costs and benefits of the

application of laptops in the classroom during class lecture.

24

The instructors in the classes surveyed were given a different questionnaire

containing questions regarding the issue of laptop use in the classroom, acceptable use of

laptops in the classroom, and similar questions pertaining to sex and race to that of the

students’ questionnaire, as well as the type of laptop used in the classroom. The answers

will help determine the instructors’ perspectives on the utilization of laptops in the

classroom during class sessions.

Limitations in Survey

It is important to describe some limitations in the coding process. Originally,

there were about thirty total variables that could have been studied. One of the main

limitations, going along with the reduction from thirty to ten variables, stems from the

number of responses and the response rate of the students. There were in fact some

questions in which only thirty eight of the original sixty five students actually responded.

Other questions were not answered by everyone, resulting in voiding those students from

the analysis entirely due to skewed results. Omission was required in order to get the

best possible results for the data. As a result of the omissions, fifty observations were

taken into analysis and ten variables were chosen as relevant to the hypothesis. The

variables chosen are described below.

Independent Variables

LISTEN is defined as “On the following scale, how much do you participate in

class based on listening to the lecture”. This information is important for study because a

student’s attention to the instructor’s lecture is important for the process of determining a

25

final grade. ASKQUES is defined as “On the following scale, how much do you

participate in class based on asking questions?” This information is important for study

because many instructors evaluate attentiveness toward the lecture or discussion by

determining who among the group is asking quality questions about the subject matter.

ANSQUES is defined as “On the following scale, how much do you participate in class

based on answering questions?” This information is important for study for similar

reasons as the previous variable, ASKQUES. If a student is answering questions posed

by the instructor, then the instructor might assume that student is paying attention to the

subject of the lecture, rewarding them a higher participation grade. DISCUSS is defined

as “On the following scale, how much do you participate in class based on discussion?”

This information is important because one of the main factors that’s included in class

participation as a grade is the amount a student engages in discussion. Therefore, this is

an important independent variable for study based on the chosen dependent variables.

Each of these variables was based on a Likert Scale, with the value of 1 associated

with the answer “never”, 2 associated with “rarely”, 3 associated with “sometimes”, 4

associated with “often”, and 5 associated with “very often”. These variables represent

the information being proved or disproved via the hypothesis.

Dependent Variables

An explanation of the ten dependent variables is required in order to fully

understand the statistics and analysis further described in the chapter. Some of the

variables, INSTUC1-6, are based on the questions in which the students identified the

instructor the students had during block 3 of the 2011-2012 school year. This question

26

was based on the 0 (no) and 1 (yes) dummy variable scale, to ensure that one instructor

was not perceived as more important or better than another. This information will be

used to comparison with the opinions of the instructors from their own surveys.

Similarly, the variable GENDER was also based on the 0 (male) and 1 (female) scale, to

ensure that one gender was not perceived as better than the other. LAPTOPUSE was

based on the Likert scale with the value of 1 associated with “never”, 2 associated with

“rarely”, 3 associated with “sometimes”, 4 associated with “often”, and 5 associated with

“very often”. This variable determined the frequency of laptop use during class

discussion or lecture. INSTRUCACC is based on the students’ perception of how

instructors were holding them accountable for participation during the lecture or

discussions. The responses were scored as 1 (yes) and 2 (no) on whether or not students

believed instructors were holding those students accountable for their participation. The

final two variables, LAPTOPDIMIN and LAPTOPIMPR, are similar in coding type. The

responses to these questions about how laptops diminish and how laptops improve

learning in the classroom were categorized in similar response type and then given a

number between 1 and 7. For LAPTOPDIMIN, the value of 1 was associated with the

response of “social media”, 2 with “distraction”, 3 with “internet surfing”, 4 with

“email”, 5 with “videos”, 6 with “other”, and 7 with “not detrimental to learning”. For

LAPTOPIMPR, the value of 1 was associated with the response of “ information access”,

2 with “note taking”, 3 with “material based software and graphics”, 4 with “lab use”, 5

with “organization”, 6 with “doesn’t help”, and 7 with “other”. These variables were

taken from the survey and used to help determine the impact of participation and will

hopefully help prove or disprove the hypothesis.

27

Summary Statistics

In Table 1, the summary statistics of the variables chosen in the regression

analysis are shown. Some important things to consider when analyzing these statistics

are the importance of the means of the answers given and the standard deviations in some

cases. Two variables in particular stand out: Laptops diminishment of learning and

Laptops improvement of learning in the classroom during lecture or discussion. The

mean of Laptop diminishment is 2.9, very close to 3 with a deviation of roughly 1.8.

Therefore, it can be assumed the majority of students observed in the survey thought of

the diminishment of laptops as a result of these factors: social media, as a general

distraction from the lecture or discussion, and internet surfing. The mean of Laptop

improvement is 3.72, with a deviation of roughly 2.2. Therefore, it can be assumed from

these results that the majority of students observed in the survey thought that laptops

improved learning from these factors: information access, note taking, software and

graphics, lab use, and organization. Now, of course, with these assumptions, there is

always the issue of some types of responses being unchecked due to the distance from the

mean. For example, in the question about laptop improvement, I know there are 13

answers in which students felt laptops didn’t help them during class. However, given

these statistics, it is only assumed that students would have answered one through five

(close to six). LAPTOPUSE is also important to discuss. This question was based on the

Likert scale, and the mean of the answers was 1.56. This means that the responses of the

students converged between ‘rarely’ and ‘never’ regarding laptop use in the classroom

during lecture of discussion.

28

Table 1

SUMMARY STATISTICS

LAPTOPDIMIN 50 2.9 1.843355 1 7 LAPTOPIMPR 50 3.72 2.213502 1 7 GENDER 50 .38 .4903144 0 1 INSTRUCACC 50 1.3 .46291 1 2 DISCUSS 50 4.6 .6700594 2 5 ANSQUES 50 3.26 1.026387 1 5 ASKQUES 50 3.2 1.160577 1 5 LISTEN 50 3.6 .9689043 1 5 LAPTOPUSE 50 1.56 .9510467 1 5 INSTRUC6 50 .18 .3880879 0 1 INSTRUC5 50 .14 .3505098 0 1 INSTRUC4 50 .1 .3030458 0 1 INSTRUC3 50 .18 .3880879 0 1 INSTRUC2 50 .16 .370328 0 1 INSTRUC1 50 .24 .4314191 0 1 responseid 0 Variable Obs Mean Std. Dev. Min Max

. summarize

29

Correlations

Correlations were tested which are shown in Table 2. The assumption is that a

high correlation is above .3. Anytime the mean is above .3, the variables correlated

against each other could essentially be asking and answering the same question. To put it

simply, high correlations mean the variables are generating similar question, which could

result in a skewed result for regression analysis. Many of the correlations in the set of

variables chosen are lower than the critical value of .3, which means that the variables

chosen for the dependent variables are not the same question as the other variables.

Although, as shown, many of the high correlations are a result of randomness since; for

example, LAPTOPDIMIN is not asking the same question as GENDER, it just happened

that way. LAPTOPUSE is not asking the same question as INSTRUC5, it just happened

that way. INSTRUC6 really doesn’t have much to do with LAPTOPDIMIN as a

question. This issue can occur when executing this type of analysis, but the important

thing to understand about this correlation is the fact that, when analyzed, I found the

correlations which had a value of above .3 to not be an issue due to the fact that there is

no way GENDER as a question is the same as the way laptops could diminish classroom

participation. Therefore, from the data shown in the correlations showing heavy

randomness in some cases, there are no obvious issues with similar question types.

Obviously the independent variables are going to have high correlations with each other

because they are asking a very similar questions pertaining to the hypothesis. Overall,

the correlations shown in Table 2 work and therefore the variables chosen can be used in

the regression analysis.

30

Table 2

CORRELATIONS

LAPTOPDIMIN 1.0000 LAPTOP~N

LAPTOPDIMIN -0.2009 -0.1042 0.5392 0.3236 -0.1076 -0.4313 0.3881 LAPTOPIMPR -0.2312 -0.1326 0.3925 -0.0694 -0.3545 -0.0880 1.0000 GENDER 0.1511 0.0404 -0.2595 -0.3344 -0.2428 1.0000 INSTRUCACC 0.0727 -0.0126 0.0341 -0.0185 1.0000 LAPTOPUSE 0.0850 0.3110 -0.0575 1.0000 INSTRUC6 -0.1562 -0.1890 1.0000 INSTRUC5 -0.1345 1.0000 INSTRUC4 1.0000 INSTRUC4 INSTRUC5 INSTRUC6 LAPTOP~E INSTRU~C GENDER LAPTOP~R

LAPTOPDIMIN 0.2171 0.1049 0.0895 0.1157 -0.0462 -0.2750 0.0257 LAPTOPIMPR -0.0914 -0.1128 -0.0931 0.0468 -0.1205 -0.0687 0.1074 GENDER -0.2749 -0.2439 -0.0381 0.2857 -0.0540 0.2203 -0.0450 INSTRUCACC 0.0910 0.1140 -0.1246 0.0658 0.2453 -0.1667 -0.1931 LAPTOPUSE 0.2702 0.2478 0.3287 -0.0256 -0.1850 -0.2016 0.1084 INSTRUC6 0.1411 0.0544 0.1363 0.2040 -0.2633 -0.2045 -0.2195 INSTRUC5 -0.0120 -0.1706 -0.1032 -0.0174 -0.2267 -0.1761 -0.1890 INSTRUC4 -0.0000 0.0000 0.1772 0.1005 -0.1873 -0.1455 -0.1562 INSTRUC3 0.2497 0.2356 0.2388 -0.1099 -0.2633 -0.2045 1.0000 INSTRUC2 -0.0455 0.0665 -0.0580 -0.2303 -0.2453 1.0000 INSTRUC1 -0.3027 -0.1793 -0.3282 0.0565 1.0000 DISCUSS 0.0629 0.1837 0.1246 1.0000 ANSQUES 0.6813 0.6408 1.0000 ASKQUES 0.7260 1.0000 LISTEN 1.0000 LISTEN ASKQUES ANSQUES DISCUSS INSTRUC1 INSTRUC2 INSTRUC3

(obs=50)> C5 INSTRUC6 LAPTOPUSE INSTRUCACC GENDER LAPTOPIMPR LAPTOPDIMIN. corr LISTEN ASKQUES ANSQUES DISCUSS INSTRUC1 INSTRUC2 INSTRUC3 INSTRUC4 INSTRU

31

CHAPTER 5

REGRESSION ANALYSIS

The findings of the regression analysis are interesting if review them in

conjunction with the findings of observational data. Prior to presenting the regression

data analysis, I will discuss the observations done in the six economics courses during

Block 3 of the 2011-2012 school year at Colorado College. As part of the process of data

collection, I observed each class session once for 15-20 minutes from November 15th –

18th. It may be important to note that courses at Colorado College are different from

other Colleges and Universities. Each student takes only one course at a time for a period

of 3 ½ weeks. The content of each block is equivalent to one semester’s worth of course

content at a regular university. Each class period is generally 3 hours long, whereas at a

regular university, a class period is generally between 50 and 75 minutes.

Classroom Observations

The first class I observed was Principles of Macroeconomics, a 152 level course

in the economics department with Instructor 6. This class had 23 students in the

classroom at the time of observation, and none of the students was using a laptop during

the lecture. I observed a high level of concentration from each of the students, either

listening to the instructor or taking notes. When questions were posed by Instructor 6,

32

students reacted quickly by answering the questions. Also, one other important feature

that I observed was Instructor 6’s use of his or her laptop. Instructor 6 used a laptop for

displaying a PowerPoint presentation, which I found to supplement the students attention

to the content of the class lecture. When Instructor 6 responded to the survey, the

answers given were that students were rarely encouraged to use laptops, students were

participating often, and students were held academically accountable for their

participation through the use of letter grade changes based on participation or, in some

cases, resulting in making students answer questions if not listening. This data will be

further analyzed after discussing regressions.

I then observed the Consumer Marketing 326 course with Instructor 1. This class,

as opposed to the other, had 24 total students with 4 of those students using laptops. One

person was observed using their laptop for note taking, while the other three people were

using their laptops to surf the web during lecture. As I watched, two of those people

were actually talking to each other during the lecture while using their laptops. This

observation should support my hypothesis because these students’ participation was

being negatively impacted by the presence of laptops. I observed that the majority of the

students who were not using laptops were not only listening to the lecture, but were

active in asking and answering questions of Instructor 1. It seemed from my observations

of this class that the students with laptops were less active than the students without

laptops. I should also note that the Instructor was using a laptop in order to show videos

on the class topic. When surveyed, the instructor said that he rarely encouraged laptop

use, yet from my observation, students were using their laptops freely as if it didn’t

matter. I can surmise from this observation that those particular students using laptops,

33

while they were aware of the costs of laptop use, chose to disregard the cost of their

laptop use due to the higher expected benefits associated with recreational use.

The third class observed was Principles of Microeconomics with Instructor 2.

This class had 19 students present at the time of observation and none of them had a

laptop. The teacher had a laptop for the purpose of displaying a PowerPoint presentation

for the topic of discussion, and it seemed to be successful in drawing in the attention of

the students. The students were active listeners, taking notes, asking questions

frequently, and answering questions posed to them. There is no question that the absence

of laptop has allowed the students in the class to have the ability to pay more attention

during lecture and not become distracted. The Instructor 2’s preferences toward laptops

were that laptops were rarely encouraged in the classroom because the teacher wanted the

students to pay close attention to the lecture. Interestingly, Instructor 2 was the only

instructor to answer ‘no’ to the question of keeping students academically accountable for

their participation. I find this interesting because none of the students in the class were

using a laptop and although the use is rarely encouraged, one would think students would

bring their laptops anyway without the presence of a cost toward their final grade. This

might mean that students were able to infer a cost from using laptops in class even though

the professor did not make that cost explicit.

The fourth class observed was Principles of Financial Accounting with

Instructor 3. There were 24 students in the class, and only one of those students was

using a laptop. During the observation period, it appeared that the student was taking

notes on the lecture. The students without laptops were listening intently to the lecture;

however, few questions were being asked or answered due to the nature of the lecture at

34

the time of observation. I believe the absence of laptops was due in large part to

Instructor 3’s attitude toward laptop use. When surveyed, Instructor 3 rarely encouraged

laptop use in the classroom. Instructor 3 also did hold students academically accountable

for their participation.

The fifth class surveyed was Addiction with Instructor 5. There were only

9 students in the class and only one of those students was using a laptop. This student

was using the laptop for the purpose of note taking and following the PowerPoint on the

laptop that the instructor was using during the lecture. This student was listening to the

lecture actively. In this instance, this observation may not support my hypothesis

because the presence of the laptop was improving on the ability to not only organize

material more effectively, but also to stay engaged through participation during lecture.

Instructor 5 rarely encouraged the use of laptops, and held students academically

accountable for their participation. In this case, the one student has most likely gone

through a cost-benefit analysis and determined that the presence of a laptop could

significantly benefit that student’s participation during lecture.

The final class surveyed was Intermediate Microeconomics with Instructor 4.

There were a total of 18 students in the class, and 7 of those students were using laptops.

I was only able to observe a few of those 7 people on their laptops, but the students I

observed were surfing the internet or reading the news. It did not seem like those

students with laptops were paying much attention, therefore I can assume that those

students with laptops were not participating in the lecture. The students without laptops

were listening attentively and taking notes. This observation supports my hypothesis that

students who multitask with their laptops will not participate as much as students without

35

laptops. The instructor rarely encouraged laptop use and made the students academically

accountable for their participation. This leads me to believe the students with laptops,

felt that the costs of not paying attention to the lecture (resulting in a lower grade) were

not high enough to outweigh the benefits of recreational activities on their laptops.

Therefore, students with laptops, purely on an observational basis, believe that the

benefits of laptop use are enough of an incentive to disregard the consequences of their

use during class lecture.

A common theme among all the observations was the fact that Instructors rarely

encouraged laptop use and kept the students academically accountable for their

participation during lecture. Therefore, students who brought laptops to class and did not

participate are assumed to understand the costs and yet to believe the benefits of laptop

use in a recreational manner outweigh the costs of lack of participation. Now regression

analysis results will be reviewed to identify common themes of laptop use and

participation from the survey data.

Regression Analysis

Four separate regressions were tested based on the four different equations

explained in Chapter 2. Each equation was tested for validity before actual regressions

were done, using the white test for Heteroskedasticity, normality of errors, correlation

analysis, autocorrelation, and Multicollinearity, as summarized in Table 3. After the tests

for regression validity were completed, each equation was regressed to determine the

dependent variables against the independent variable for best fit.

36

Table 3

REGRESSION ERROR CHECKS

Test Description Equation 1

(LISTEN)

Equation 2

(ASKQUES)

Equation 3

(ANSQUES)

Equation 4

(DISCUSS)

Heterosked

asticity

Test to determine whether standard errors are unbiased, and thus, statistical tests for significance in regression analysis are valid.

OK – Chi

squared =

2.79

OK – Chi

squared =

.35

OK – Chi

squared =

.23

OK – Chi

squared = 23

Needed to

test with

robust

standard

errors.

Normality

of Errors

Test to validate the assumption that errors follow a normal distribution. This is a key assumption to the validity of regression models.

OK OK OK OK

Correlation

analysis

Analysis of how well changes in one variable can be predicted by changes in another variable.

OK OK OK OK

Autocorrela

tion

Test of whether errors are

autocorrelated, in violation

of the assumptions for

regression analysis

N/A (no

time series)

N/A (no

time series)

N/A (no

time series)

N/A (no

time series)

Multicoline

arity

Test to determine whether two or more variables are highly correlated, which can cause inaccurate regression analysis results.

Corrected

by

dropping

INSTRUC1

Corrected by

dropping

INSTRUC1

Corrected by

dropping

INSTRUC1

Corrected

by dropping

INSTRUC1

37

LISTEN

Regressions were executed based on the four independent variables studied. The

first regression is shown in Figure 1. This regression was based on the independent

variable LISTEN, and the other dependent variables chosen were factored against

LISTEN. However, before actually doing the regression, some preliminary tests were

completed: Autocorrelation, Multicollinearity, the white test against the null hypothesis

for Heteroskedacitity, and normality of errors. Autocorrelation was simple to test, since

there is no time series data. Multicollinearity was tested and corrected by dropping the

dummy variable INSTRUC1. INSTRUC3 had a higher coefficient against INSTRUC1,

meaning that in response to the independent variable LISTEN, INSTRUC3 had a higher

positive impact of LISTEN than INSTRUC1. The White test was used with the null

hypothesis assuming constant variance and found that the Chi squared (2.79) was not

over the critical value (3.841) to warrant issues with Heteroskedasticity. Therefore, we

cannot reject the null hypothesis of constant variance. Having tested for any false

assumptions and corrected the dependent variables accordingly, regression for LISTEN

was then executed.

38

Figure 1

REGRESSION ANALYSIS FOR LISTEN

The regression for LISTEN shows results that determine laptops have an impact

on the amount of participation in the classroom, but the regression also shows the

dependent variables chosen cannot fully explain the relationship. The t-statistics support

this conclusion by identifying the only significant variable as Instructor 3. This means

that the Instructor (3 in particular) can have an impact on the listening patterns of

students. This is obvious intuitively and through the observations conducted, because the

instructor’s attitude toward classroom discussion or lecture should impact the amount

students are listening in the classroom. However, other than Instructor 3, no other

variables were found to be statistically significant against the independent variable

_cons 2.781222 .8327376 3.34 0.002 1.096851 4.465593 LAPTOPDIMIN .0771289 .1061166 0.73 0.472 -.1375122 .2917701 LAPTOPIMPR -.0920672 .0740891 -1.24 0.221 -.2419265 .0577921 GENDER -.2994961 .3314368 -0.90 0.372 -.9698902 .3708981 INSTRUCACC .2253678 .3436876 0.66 0.516 -.4698059 .9205416 LAPTOPUSE .1208041 .1796173 0.67 0.505 -.2425063 .4841145 INSTRUC6 .7917984 .4731629 1.67 0.102 -.1652639 1.748861 INSTRUC5 .4415679 .4912202 0.90 0.374 -.5520187 1.435154 INSTRUC4 .5292288 .5301784 1.00 0.324 -.5431582 1.601616 INSTRUC3 1.121891 .4344189 2.58 0.014 .2431954 2.000586 INSTRUC2 .6922705 .4529469 1.53 0.134 -.2239011 1.608442 LISTEN Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 46 49 .93877551 Root MSE = .9231 Adj R-squared = 0.0923 Residual 33.2321417 39 .852106196 R-squared = 0.2776 Model 12.7678583 10 1.27678583 Prob > F = 0.1769 F( 10, 39) = 1.50 Source SS df MS Number of obs = 50

> CC GENDER LAPTOPIMPR LAPTOPDIMIN. regress LISTEN INSTRUC2 INSTRUC3 INSTRUC4 INSTRUC5 INSTRUC6 LAPTOPUSE INSTRUCA

39

LISTEN. The coefficients are the beta values attached to the variables, defining the

effect when the variable increases by one. One interesting coefficient is LAPTOPIMPR

because the coefficient is negative, meaning that an increase in laptop improvement

negatively impacts the amount of listening students engage in during class session. This

makes sense according to the literature because as, for example, by Abate.1 Through the

observations of Abate, students who believe laptops are beneficial will most likely bring

their laptop to the classroom without fully taking into account the potential consequences

of said action. This result in the use of laptops while attempting to listen to the lecture

will ultimately have a negative effect on LISTEN. Although there is a relationship

between the dependent variables and the variance, it appears through the use of the R²

values that more work needs to be done. An R2 value of .2776 is a good start, but this

value identifies that more variables need to be researched in order to gain a better

understanding of laptop use and its affect on participation, especially to the listening

patterns of students.

ASKQUES

The next equation based on the independent variable ASKQUES and pertaining to

asking questions in the classroom, required the same checks for quality that the previous

regression required. No autocorrelation was present because there is no time series data.

Multicollinearity was removed when INSTRUC1 was removed from the regression to

prevent the summation of the variables from equally one. The regression was checked

for Heteroskedasticity, and using the white test, I found the Chi squared value to be 0.35,

1 Abate, Charles J. 2008, “You say multitasking like it's a good thing.” The NEA higher education journal

27, no. 5 (Fall 2008): 7-14.

40

well below the critical value. This means that we can’t reject the null hypothesis of

constant variance. Normality of errors was not present as well, due to the residuals

looking constant. Now that all the checks against errors have been done, the regression

was analyzed.

Figure 2

REGRESSION ANALYSIS FOR ASKQUES

Figure 2 is the regression of the independent variable ASKQUES. The same

dependent variables are present as the variables in the previous regression. The results of

this regression show that there is a weak relationship between laptop use and asking

questions in the classroom. The results of this regression are different from the previous

_cons 2.420719 1.014953 2.39 0.022 .3677837 4.473654 LAPTOPDIMIN -.0103047 .1293365 -0.08 0.937 -.2719124 .251303 LAPTOPIMPR -.0852152 .0903008 -0.94 0.351 -.2678659 .0974355 GENDER -.3347165 .4039599 -0.83 0.412 -1.151802 .4823695 INSTRUCACC .2946603 .4188914 0.70 0.486 -.5526274 1.141948 LAPTOPUSE .3099866 .2189202 1.42 0.165 -.1328213 .7527944 INSTRUC6 .6341073 .5766977 1.10 0.278 -.5323739 1.800588 INSTRUC5 -.3697019 .5987062 -0.62 0.540 -1.580699 .8412957 INSTRUC4 .2156324 .646189 0.33 0.740 -1.091408 1.522673 INSTRUC3 .9808548 .529476 1.85 0.072 -.0901115 2.051821 INSTRUC2 .7888854 .5520582 1.43 0.161 -.3277576 1.905528 ASKQUES Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 66 49 1.34693878 Root MSE = 1.1251 Adj R-squared = 0.0602 Residual 49.3666237 39 1.26581086 R-squared = 0.2520 Model 16.6333763 10 1.66333763 Prob > F = 0.2572 F( 10, 39) = 1.31 Source SS df MS Number of obs = 50

> ACC GENDER LAPTOPIMPR LAPTOPDIMIN. regress ASKQUES INSTRUC2 INSTRUC3 INSTRUC4 INSTRUC5 INSTRUC6 LAPTOPUSE INSTRUC

41

regression because, according to the data, there are no statistically significant variables.

Most of the coefficients are expected. Instructors’ coefficients are usually an indication

of teaching methods. Therefore, Instructor 5 is having a negative impact because his

students don’t feel the instructor is holding them accountable academically, which results

in less questions being asked. Whereas Instructor 2, for example, has a positive

coefficient because students feel the instructor is holding them accountable academically

for their participation, resulting in more questions being asked. The same is true for

INSTRUCACC which suggests that students who feel instructors are holding them

academically accountable for their participation will ask more questions. It is important

to highlight LAPTOPUSE’s coefficient because its value is positive. This result suggests

that students who frequently use laptops ask more questions than their peers who don’t

use laptops. This is counter to the literature which suggests that students with laptops are

not participating during lecture or asking questions.2 This new data suggests instead that

students with laptops are participating more in class at least by asking more questions.

Another interesting coefficient is LAPTOPDIMIN. The coefficient is negative,

meaning that for an increase in the variable LAPTOPDIMIN, the fewer students are

asking questions in the classroom. This is counter-intuitive to the literature because

students who think laptops diminish their ability to ask questions would not bring their

laptop to the classroom and therefore would not be affected by the negative impact of

multitasking. However, this coefficient shows that when students believe that laptops

diminish their learning, they’re still participating less in is terms of asking questions. The

equation, given the variables chosen, does not have the a high R² value, meaning that the

2 Ibid

42

dependent variables may explain some form of relationship to the independent variable

but not the whole story. The R² of the regression is .2520 which does not allow for full

confidence in the variables chosen. Similar to the previous equation, this value does give

some hope in regard to the variables chosen as a building block for more research in the

future, it does not allow for this research to be fully confident that the dependent

variables that have been tested explain the variance fully.

ANSQUES

The third equation, based on the variable ANSQUES, was checked for errors

similar to the other two regressions. There was no Autocorrelation due to lack of time

series data. Multicollinearity was removed when INSTRUC1 was removed from the

regression to prevent the summation of the variables from equaling one. The regression

was checked for Heteroskedasticity, and using the white test, I found the Chi squared

value to be 0.23, well below the critical value. This means that we can’t reject the null

hypothesis of constant variance. Normality of errors was not present as well, due to the

residuals being constant. Now the regression was analyzed.

Figure 3 shows the regression analysis for the independent variable

ANSQUES. The results of this regression are the highest of all of the regressions

executed in this research. Looking at the values of t-statistics, three variables are

statistically significant: INSTRUC3, INSTRUC6, and LAPTOPUSE. What this means is

that the values for these variables are higher than the hypothesized critical value, meaning

that the variables’ impact on the independent variable higher than expected. The

coefficients of the variables for the instructors can be interpreted to mean the amount of

43

influence each instructor is having on the extent to which students answer questions in

the classroom. From the observations for this research, I believe these values are

meaningful in understanding how instructors impact students’ ability and willingness to

answer questions. This means that students in Instructor 6’s class are asking more

questions than students in Instructor 2’s class.

LAPTOPUSE is an interesting variable because of the coefficient being positive.

This is counter to the hypothesis which predicted that if there were an increase in the

frequency of laptop use among students; ANSQUES would decrease rather than increase.

It was also predicted from the literature that LAPTOPUSE would negatively impact the

student’s ability to participate.3 However, this new research data indicated the opposite:

When Laptop use increased so did the frequency of answering questions in class.

LAPTOPDIMIN and LAPTOPIMPR both negatively impact the independent

variable according to their coefficients. It has been assumed based on existing literature

that the perceived benefits and costs of laptops could impact a student’s perception and

choice of whether or not to bring a laptop to the classroom. However, according to the

results of this regression, students who believe laptop use helps their learning are

answering less questions and students who believe their laptop use is detrimental to their

learning (for various reasons) will also answer less questions during class. The R² value

of the regression is .3328, the highest of all the regression done in this research. This

value means that there is a relationship among the variables chosen in regard to the

independent variable; however this relationship is not strong. The variance of the

regression is not explained well enough by the variables chosen to be able to have full

3 Ibid

44

confidence. The R² value does give some hope of using these variables as a building

block for further research.

Figure 3

REGRESSION ANALYSIS FOR ANSQUES

_cons 3.126017 .8477443 3.69 0.001 1.411292 4.840741 LAPTOPDIMIN -.0702633 .108029 -0.65 0.519 -.2887725 .1482459 LAPTOPIMPR -.1089367 .0754242 -1.44 0.157 -.2614967 .0436232 GENDER .1369918 .3374096 0.41 0.687 -.5454835 .819467 INSTRUCACC -.3245858 .3498812 -0.93 0.359 -1.032287 .3831156 LAPTOPUSE .4233065 .1828542 2.31 0.026 .053449 .7931641 INSTRUC6 1.192176 .4816897 2.47 0.018 .2178669 2.166486 INSTRUC5 -.237511 .5000724 -0.47 0.637 -1.249003 .7739809 INSTRUC4 .6503914 .5397327 1.21 0.235 -.441321 1.742104 INSTRUC3 .884948 .4422475 2.00 0.052 -.0095821 1.779478 INSTRUC2 .2929248 .4611094 0.64 0.529 -.639757 1.225607 ANSQUES Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 51.62 49 1.05346939 Root MSE = .93973 Adj R-squared = 0.1617 Residual 34.440681 39 .883094383 R-squared = 0.3328 Model 17.179319 10 1.7179319 Prob > F = 0.0678 F( 10, 39) = 1.95 Source SS df MS Number of obs = 50

> ACC GENDER LAPTOPIMPR LAPTOPDIMIN. regress ANSQUES INSTRUC2 INSTRUC3 INSTRUC4 INSTRUC5 INSTRUC6 LAPTOPUSE INSTRUC

45

DISCUSS

The final regression, DISCUSS, was the only regression to have issues during

regression analysis. There was no Autocorrelation because there was no time series data.

Multicollinearity was removed when INSTRUC1 was removed from the regression to

ensure that the summation of the variables does not equal one. The regression was

checked for Heteroskedasticity, and using the white test, I found the Chi squared value to

be 23, well above the critical value. Therefore, the regression needed to be executed with

robust standard errors and the regression executed.

Figure 4

REGRESSION ANALYSIS FOR DISCUSS

_cons 3.876147 .5243086 7.39 0.000 2.815633 4.936661 LAPTOPDIMIN .0564009 .0650439 0.87 0.391 -.0751628 .1879646 LAPTOPIMPR .0025045 .0562834 0.04 0.965 -.1113394 .1163483 GENDER .708564 .2642106 2.68 0.011 .1741477 1.24298 INSTRUCACC .2040353 .1968128 1.04 0.306 -.1940562 .6021268 LAPTOPUSE .0680294 .1088429 0.63 0.536 -.152126 .2881849 INSTRUC6 .2677826 .2619561 1.02 0.313 -.2620737 .7976389 INSTRUC5 -.1707026 .22448 -0.76 0.452 -.6247564 .2833511 INSTRUC4 -.0164192 .2438086 -0.07 0.947 -.5095687 .4767303 INSTRUC3 -.1953147 .2831012 -0.69 0.494 -.767941 .3773116 INSTRUC2 -.4822264 .4215418 -1.14 0.260 -1.334875 .3704224 DISCUSS Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust

Root MSE = .6375 R-squared = 0.2795 Prob > F = 0.2351 F( 10, 39) = 1.36Linear regression Number of obs = 50

> ACC GENDER LAPTOPIMPR LAPTOPDIMIN, r. regress DISCUSS INSTRUC2 INSTRUC3 INSTRUC4 INSTRUC5 INSTRUC6 LAPTOPUSE INSTRUC

46

Figure 4 represents the regression analysis for DISCUSS with robust standard

errors factored into the regression. Looking at the values of t-statistics, the only variable

of significance was GENDER. This means that the variable GENDER impacted the

independent variable more than hypothesized. This result tells us that gender has a

significant impact on the amount of discussion students’ engage in during class. The

coefficients of the variables for the instructors equate to the amount of influence each

instructor is having on the amount of student discussion in the classroom. From the

observations for this research, I believe these values are meaningful toward

understanding how instructors impact students’ desire to participate in class discussion.

The results show that a student in Instructor 6’s class is participating in class discussion

more than a student of any other class and thus suggests that Instructor 6 is having a

positive impact on the amount of discussion during class. LAPTOPUSE is an interesting

variable because the coefficient was positive, similar to the previous regression. If there

were an increase in the frequency of laptop use among students, the hypothesis would

suggest that DISCUSS would decrease rather than increase. It was assumed from the

literature that LAPTOPUSE would negatively impact the ability to participate in

discussion but the results of this new regression suggest otherwise.4

LAPTOPDIMIN and LAPTOPIMPR both positively impact the independent

variable according to their coefficient. It is assumed in the literature that the perceived

use of laptops would have a negative impact on a student’s choice to bring a laptop to the

classroom. On the contrary, the results of this research suggest that the presence of

laptops in the classroom is beneficial to the amount that students participate in class

4 Ibid

47

discussion. Students who believe laptop use helps their learning are discussing more than

students who believe their laptop use is detrimental to their learning. The R² value of the

regression is .2795. This value means that there is a relationship among the variables

chosen with regard to the independent variable; however this relationship is not strong.

The variance of the regression is not explained well enough by the variables chosen to be

able to have full confidence. The R² value does give some hope of using these variables

as a building block for further research.

Based on all the regressions done in this research, it appears that some forms of

participation are impacted differently by the presence of laptops. LISTEN, ASKQUES,

and ANSQUES is affected by the perception of laptop use among students negatively in

regard to that use being beneficial, however DISCUSS is affected by that perception

positively. This result shows that the perception of laptop use has a different affect

among students across different forms of participation. Instructors also have an impact

on the different forms of participation, but this is obvious because the instructors teaching

methods would either deter of promote participation differently. The use of laptops

seems to have different results across different forms of participation as well. All the

independent variables were positively influenced by laptop use, which suggests that

laptop use may actually benefit the amount students participate in the classroom. This,

however, is counter to the literature on the subject and the observations done in the six

classes.5 Therefore, the data in this research suggests that the actual use of laptops may

help participation, but the perceptions of that use will negatively impact participation as a

whole.

5 Ibid

48

CHAPTER 6

CONCLUSION

According to all the data collected and the observations made in the six

economics classes at Colorado College during Block three of the 2011-2012 year, there is

some evidence that the presence of laptops in the classroom does benefit students’

participation, however the perceptions about the effects of laptop use hinders

participation. If Student A not only brings his or her laptop to the classroom but then also

opens that laptop up for use, the effect is an increase in participation; but even when

Student A has that laptop and believes that it helps his or her learning experience, that

student’s participation dips. The goal of this research was to determine whether students

who use laptops in class, and particularly those who multi-task more, participate less than

students who don’t use laptops in class, with a resulting negative impact on their learning

experience (whether self-identified or perceived by their instructor), and whether they do

a cost/benefit analysis that affects their decision about whether to bring a laptop to class.

Although the confidence in the variables chosen is not as strong as hoped (between .25

and .33 R²), one thing is clear: an increase in laptop use frequency has beneficial effects

on participation by students in the classroom during lecture or discussion. This was

found to be true for each regression and equation. Therefore, from these results we can

conclude, not with confidence but tentatively, that presence and use of laptops in the

49

classroom is helpful to students’ ability to participate, and based on the students own self-

identified honor system and instructors’ ability to act as an overseer of student

participation. Since these results are counter to the prevailing results in current and past

research, it suggests that additional research is needed to identify the variables that

caused our particular study group to differ from other research.

In order to validate and understand the results, a larger study should be performed

across different colleges that have different teaching environments. Additional variables,

such as length of class period and type of teaching style in the classroom (lecture,

discussion, lab work, etc) should be added to evaluate whether these differences influence

the level to which laptops can be beneficial to student success in the classroom. The

small class sizes may have provided different results than from a larger school with larger

class sizes. This allows instructors to have an informal relationship with their students,

which allows for more accountability from students and instructors and may have

contributed to the results of this research.

Another factor that was noted in this study was accountability. Most of the

classes in the study group exhibited a high level of student accountability. The small

class sizes and close student/teacher relationships at a small college such as Colorado

College, in conjunction with professors who set a high level of accountability, may result

in significantly different results when considering laptops in the classroom. When

professors set a high standard for classroom participation and base grades on those

expectations, in an environment in which the students have direct and regular contact

with professors, not only during the course of a class, but across multiple classes during

their 4 years in college, it can be assumed that the overall accountability is extremely

50

high. In such an environment, the choice to bring a laptop to class might be very

different than in another environment. This was reflected in the relatively small numbers

of students who actually brought laptops into the classroom in the study group. It might

also result in much less recreational use of laptops in the classroom, and more use of

beneficial tools. A wider study that included different departments, different class sizes,

and different colleges would be helpful to determine whether any of these factors affect

the results. Additional variables that measure both the level of accountability and the

existence of accountability might help to answer these questions. In addition, future

studies should include such baseline variables as classroom size, student/teach ratio, and

department size (as a measure of how likely the student would be to have the same

professor more than once).

This study’s results also bring into question many preconceived notions and

beliefs about topics such as multitasking. Multitasking, according to previous research,

should negatively impact participation in the classroom when students use a laptop.

However this study, subject to more confidence, shows that multitasking is not only not

hindering students’ ability to participate, but in fact may be helping participation. Based

on observations that were part of the study, it can be hypothesized that laptops have many

accessible programs which students can use to streamline the process of learning,

including note taking tools like Microsoft Word and information access imbedded into

Safari and Google Chrome, and that these tools actually help students to perform better in

the classroom. Though recreational use of laptops was also observed, the data suggests

that overall more laptops in the classroom are put to beneficial use to improve classroom

performance. For future research, more variables including the type of software and tools

51

being used while using laptops in the classroom may be helpful in order to fully

understand the benefits of laptops in learning environments.

It was also determined that students perceptions of laptop use did not benefit their

choice in laptop use and in fact hindered their participation in the classroom. In this

research, students’ perceptions are the best representation we have of their own

cost/benefit analysis. The results suggest that student’s cost/benefits analysis, while it

may have affected their choice to bring a laptop to class, did not result in a decision that

improved their performance. In fact, it appears to have resulted in decisions that

worsened their performance. This result is also counter to existing research on the

subject. Both student and professor perceptions about the costs and benefits of laptop use

seems to have not been reflective of their results. This suggests either those students do

not actually do cost/benefits analysis, or they do the analysis but don’t act in accordance

with it, or they do the cost/benefits analysis, act on that analysis, and yet see no positive

results from their choices. There is also the “perception is reality” problem. It is possible

that students, who think that laptop use will hurt their classroom performance, actually

act in such a way that their performance is hurt regardless of whether they use laptops or

not. Similarly, it is possible that students who have a negative perception about the

influence of laptops on their classroom performance, just have negative perceptions about

their classroom performance overall and it is reflected in their answers to questions about

laptop performance specifically. In order to truly understand whether students do a

cost/benefit analysis and how their perceptions affect performance, additional questions

should be added to future studies to compare perceptions about classroom participation

52

overall to specific perceptions about laptops, and also to ask more specifically about costs

and benefits of laptop use, perhaps with actual measures for comparing costs to benefits.

There are a number of additional factors that could have affected all of the results

and conclusions, and these should be accounted for in future research. The first is the

assumption that students were bound to the honor system in honestly and accurately

answering the questions presented in the survey. Giving students the freedom to give an

answer based on their use of laptops and their perceptions of that use is subject to false

answers. Although the instructors and students were told that the survey was anonymous

there is still the chance students would give a false answer in order to improve their

image or self-image. I can only stress the matter of importance of making sure the

survey population gives fully accurate answers to questions contained in the survey for

future research. For example, additional questions that ask for the same information in

both the positive and in the negative could be used to remove student bias

The size of the survey population was also a factor that arose in the data

collection process. Future research to validate the results should include a larger survey

population, covering more students and professors. I believe that having a population of

50 was limiting and resulted in difficulty getting statistically significant results with

strong confidence.

More variables for the regression analysis would also be beneficial for future

research. Given the variables chosen for this study, only 25-33 percent of the variation

for the independent variable was explained with the variables chosen. Identifying more

53

variables associated with laptop use and participation would be highly recommended in

order to further understand this relationship.

Although I didn’t take this into account during my analysis, I believe gender

would have a significant role to play when tracking participation and laptop use. From

my own observations, most of the men using laptops in the classes were playing games

and reading ESPN whereas the women in classes were frequently seen on Facebook. I

also believe, based on preconceptions of gender, taking into account the difference in

participation from males to females would also have been an interesting question to

answer based on use of laptops during class. Therefore, I believe that for future research

answering the question of how gender plays a role in laptop use would be beneficial

toward finding a definitive answer to laptop use and participation.

Based on the results of this study and how they differ from previous research, it is

critical that further research is performed to understand how best to apply technology in

the classroom and how students can apply tools to improve their learning. Given a

positive response, laptops would be ever more present in the classroom and instructors

would encourage that use more frequently. Given a negative response, laptop use would

never be encouraged and students and instructors would not have to think about bringing

their laptops to the classroom because they would know that use is detrimental to their

learning in general and participation more specifically.

Further research on perceptions and cost/benefit analysis is also recommended. If

technology tools are not being used to further education simply because perceptions are

negative, then we may be missing an important opportunity to optimize the learning

54

experience. In addition, if students are failing to do cost/benefits analysis that actually

allows them to improve their performance, then it may be possible to help educate them

about costs and benefits so that the choices they make actually have the results that they

expect.

I believe that in order to further understand how laptop use and participation in

the classroom are intertwined, further research that incorporates the additions

recommended in this report must be completed in order to have full confidence in the

results, and in order to take actions based on the conclusions.

55

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