Evaluation of Evidence-Based Practices in 

Online Learning 

A Meta-Analysis and Review of Online Learning Studies 

Evaluation of Evidence-Based Practices in 

Online Learning: A Meta-Analysis and 

Review of Online Learning Studies 

U.S. Department of Education 

Office of Planning, Evaluation, and Policy Development 

Policy and Program Studies Service 

Revised September 2010 

Prepared by 

Barbara Means 

Yukie Toyama 

Robert Murphy 

Marianne Bakia 

Karla Jones 

Center for Technology in Learning 

 
This report was prepared for the U.S. Department of Education under Contract number ED-04-

CO-0040 Task 0006 with SRI International. Bernadette Adams Yates served as the project 

manager. The views expressed herein do not necessarily represent the positions or policies of the 

Department of Education. No official endorsement by the U.S. Department of Education is 

intended or should be inferred. 

U.S. Department of Education 

Arne Duncan 

Secretary 

Office of Planning, Evaluation and Policy Development 

Carmel Martin 

Assistant Secretary 

Policy and Program Studies Service Office of Educational Technology 

Alan Ginsburg Karen Cator 

Director Director 

September 2010 

This report is in the public domain. Authorization to reproduce this report in whole or in part is granted. 

Although permission to reprint this publication is not necessary, the suggested citation is: U.S. 

Department of Education, Office of Planning, Evaluation, and Policy Development, Evaluation of 

Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies,

Washington, D.C., 2010. 

This report is also available on the Department’s Web site at 

www.ed.gov/about/offices/list/opepd/ppss/reports.html. 

On request, this publication is available in alternate formats, such as braille, large print, or computer 

diskette. For more information, please contact the Department’s Alternate Format Center at 

(202) 260-0852 or (202) 260-0818.
iii

Contents 

EXHIBITS ...................................................................................................................................................................... V

ACKNOWLEDGMENTS ................................................................................................................................................ VII

ABSTRACT ................................................................................................................................................................... IX

EXECUTIVE SUMMARY ............................................................................................................................................... XI

Literature Search .................................................................................................................................................... xii

Meta-Analysis ....................................................................................................................................................... xiii

Narrative Synthesis ................................................................................................................................................ xiv

Key Findings .......................................................................................................................................................... xiv

Conclusions ......................................................................................................................................................... xviii

1. INTRODUCTION ......................................................................................................................................................... 1

Context for the Meta-analysis and Literature Review .............................................................................................. 2

Conceptual Framework for Online Learning ............................................................................................................ 3

Findings From Prior Meta-Analyses ......................................................................................................................... 6

Structure of the Report .............................................................................................................................................. 7

2. METHODOLOGY ........................................................................................................................................................ 9

Definition of Online Learning .................................................................................................................................. 9

Data Sources and Search Strategies ........................................................................................................................ 10

Electronic Database Searches ................................................................................................................................. 10

Additional Search Activities ................................................................................................................................... 10

Screening Process ................................................................................................................................................... 11

Effect Size Extraction ............................................................................................................................................. 13

Coding of Study Features ....................................................................................................................................... 14

Data Analysis .......................................................................................................................................................... 15

3. FINDINGS ................................................................................................................................................................. 17

Nature of the Studies in the Meta-Analysis ............................................................................................................ 17

Main Effects ............................................................................................................................................................ 18

Test for Homogeneity ............................................................................................................................................. 27

Analyses of Moderator Variables ........................................................................................................................... 27

Practice Variables ................................................................................................................................................... 28

Condition Variables ................................................................................................................................................ 30

Methods Variables .................................................................................................................................................. 31

4. NARRATIVE SYNTHESIS OF STUDIES COMPARING VARIANTS OF ONLINE LEARNING ........................................ 37

Blended Compared With Pure Online Learning ..................................................................................................... 38

Media Elements ...................................................................................................................................................... 40

Learning Experience Type ...................................................................................................................................... 41

Computer-Based Instruction ................................................................................................................................... 43

Supports for Learner Reflection .............................................................................................................................. 44

Moderating Online Groups ..................................................................................................................................... 46

Scripts for Online Interaction .................................................................................................................................. 46

Delivery Platform ................................................................................................................................................... 47

Summary ................................................................................................................................................................. 48

5. DISCUSSION AND IMPLICATIONS ............................................................................................................................ 51

Comparison With Meta-Analyses of Distance Learning ........................................................................................ 52

Implications for K–12 Education ............................................................................................................................ 53
iv

REFERENCES ............................................................................................................................................................... 55

Reference Key ........................................................................................................................................................ 55

APPENDIX META-ANALYSIS METHODOLOGY ....................................................................................................... A-1

Terms and Processes Used in the Database Searches .......................................................................................... A-1

Additional Sources of Articles ............................................................................................................................. A-3

Effect Size Extraction .......................................................................................................................................... A-4

Coding of Study Features .................................................................................................................................... A-5
v

 Exhibits 

Exhibit 1. Conceptual Framework for Online Learning ................................................................................................ 5

Exhibit 2. Bases for Excluding Studies During the Full-Text Screening Process ....................................................... 13

Exhibit 3. Effect Sizes for Contrasts in the Meta-Analysis ......................................................................................... 20

Exhibit 4a. Purely Online Versus Face-to-Face (Category 1) Studies Included in the Meta-Analysis ........................ 21

Exhibit 4b. Blended Versus Face-to-Face (Category 2) Studies Included in the Meta-Analysis ................................ 24

Exhibit 5. Tests of Practices as Moderator Variables .................................................................................................. 29

Exhibit 6. Tests of Conditions as Moderator Variables ............................................................................................... 30

Exhibit 7. Studies of Online Learning Involving K–12 Students ................................................................................ 32

Exhibit 8. Tests of Study Features as Moderator Variables ......................................................................................... 34

Exhibit 9. Learner Types for Category 3 Studies ........................................................................................................ 37

Exhibit A-1. Terms for Initial Research Database Search ........................................................................................ A-2

Exhibit A-2. Terms for Additional Database Searches for Online Career Technical Education and Teacher 

Professional Development ............................................................................................................................... A-2

Exhibit A-3. Sources for Articles in the Full-Text Screening ................................................................................... A-3

Exhibit A-4. Top-level Coding Structure for the Meta-analysis ............................................................................... A-6

vii

Acknowledgments 

This revision to the 2009 version of the report contains corrections made after the discovery of 

several transcription errors by Shanna Smith Jaggars and Thomas Bailey of the Community 

College Research Center of Teachers College, Columbia University. We are indebted to Jaggars 

and Bailey for their detailed review of the analysis. 

We would like to acknowledge the thoughtful contributions of the members of our Technical 

Work Group in reviewing study materials and prioritizing issues to investigate. The advisors 

consisted of Robert M. Bernard of Concordia University, Richard E. Clark of the University of 

Southern California, Barry Fishman of the University of Michigan, Dexter Fletcher of the 

Institute for Defense Analysis, Karen Johnson of the Minnesota Department of Education, Mary 

Kadera of PBS, James L. Morrison an independent consultant, Susan Patrick of the North 

American Council for Online Learning, Kurt D. Squire of the University of Wisconsin, Bill 

Thomas of the Southern Regional Education Board, Bob Tinker of The Concord Consortium, 

and Julie Young of the Florida Virtual School. Robert M. Bernard, the Technical Work Group’s 

meta-analysis expert, deserves a special thanks for his advice and sharing of unpublished work 

on meta-analysis methodology as well as his careful review of an earlier version of this report. 

Many U.S. Department of Education staff members contributed to the completion of this report. 

Bernadette Adams Yates served as project manager and provided valuable substantive guidance 

and support throughout the design, implementation and reporting phases of this study. We would 

also like to acknowledge the assistance of other Department staff members in reviewing this 

report and providing useful comments and suggestions, including David Goodwin, Daphne 

Kaplan, Tim Magner, and Ze’ev Wurman. 

We appreciate the assistance and support of all of the above individuals; any errors in judgment 

or fact are of course the responsibility of the authors. 

The Study of Education Data Systems and Decision Making was supported by a large project 

team at SRI International. Among the staff members who contributed to the research were Sarah 

Bardack, Ruchi Bhanot, Kate Borelli, Sara Carriere, Katherine Ferguson, Reina Fujii, Joanne 

Hawkins, Ann House, Katie Kaattari, Klaus Krause, Yessica Lopez, Lucy Ludwig, Patrik Lundh, 

L. Nguyen, Julie Remold, Elizabeth Rivera, Luisana Sahagun Velasco, Mark Schlager, and Edith 

Yang. 

ix

Abstract 

A systematic search of the research literature from 1996 through July 2008 identified more than 

a thousand empirical studies of online learning. Analysts screened these studies to find those that 

(a) contrasted an online to a face-to-face condition, (b) measured student learning outcomes, (c) 

used a rigorous research design, and (d) provided adequate information to calculate an effect 

size. As a result of this screening, 50 independent effects were identified that could be subjected 

to meta-analysis. The meta-analysis found that, on average, students in online learning 

conditions performed modestly better than those receiving face-to-face instruction. The 

difference between student outcomes for online and face-to-face classes—measured as the 

difference between treatment and control means, divided by the pooled standard deviation—was 

larger in those studies contrasting conditions that blended elements of online and face-to-face 

instruction with conditions taught entirely face-to-face. Analysts noted that these blended 

conditions often included additional learning time and instructional elements not received by 

students in control conditions. This finding suggests that the positive effects associated with 

blended learning should not be attributed to the media, per se. An unexpected finding was the 

small number of rigorous published studies contrasting online and face-to-face learning 

conditions for K–12 students. In light of this small corpus, caution is required in generalizing to 

the K–12 population because the results are derived for the most part from studies in other 

settings (e.g., medical training, higher education).

xi

Executive Summary 

Online learning—for students and for teachers—is one of the fastest growing trends in 

educational uses of technology. The National Center for Education Statistics (2008) estimated 

that the number of K-12 public school students enrolling in a technology-based distance 

education course grew by 65 percent in the two years from 2002-03 to 2004-05. On the basis of a 

more recent district survey, Picciano and Seaman (2009) estimated that more than a million K–

12 students took online courses in school year 2007–08. 

Online learning overlaps with the broader category of distance learning, which encompasses 

earlier technologies such as correspondence courses, educational television and 

videoconferencing. Earlier studies of distance learning concluded that these technologies were 

not significantly different from regular classroom learning in terms of effectiveness. Policymakers reasoned that if online instruction is no worse than traditional instruction in terms of 

student outcomes, then online education initiatives could be justified on the basis of cost 

efficiency or need to provide access to learners in settings where face-to-face instruction is not 

feasible. The question of the relative efficacy of online and face-to-face instruction needs to be 

revisited, however, in light of today’s online learning applications, which can take advantage of a 

wide range of Web resources, including not only multimedia but also Web-based applications 

and new collaboration technologies. These forms of online learning are a far cry from the 

televised broadcasts and videoconferencing that characterized earlier generations of distance 

education. Moreover, interest in hybrid approaches that blend in-class and online activities is 

increasing. Policy-makers and practitioners want to know about the effectiveness of Internetbased, interactive online learning approaches and need information about the conditions under 

which online learning is effective. 

The findings presented here are derived from (a) a systematic search for empirical studies of the 

effectiveness of online learning and (b) a meta-analysis of those studies from which effect sizes 

that contrasted online and face-to-face instruction could be extracted or estimated. A narrative 

summary of studies comparing different forms of online learning is also provided. 

These activities were undertaken to address four research questions: 

1. How does the effectiveness of online learning compare with that of face-to-face 

instruction? 

2. Does supplementing face-to-face instruction with online instruction enhance learning? 

3. What practices are associated with more effective online learning? 

4. What conditions influence the effectiveness of online learning? 

This meta-analysis and review of empirical online learning research are part of a broader study 

of practices in online learning being conducted by SRI International for the Policy and Program 

Studies Service of the U.S. Department of Education. The goal of the study as a whole is to 

provide policy-makers, administrators and educators with research-based guidance about how to 

implement online learning for K–12 education and teacher preparation. An unexpected finding of 

the literature search, however, was the small number of published studies contrasting online and 
xii

face-to-face learning conditions for K–12 students. Because the search encompassed the research 

literature not only on K–12 education but also on career technology, medical and higher 

education, as well as corporate and military training, it yielded enough studies with older learners 

to justify a quantitative meta-analysis. Thus, analytic findings with implications for K–12 

learning are reported here, but caution is required in generalizing to the K–12 population because 

the results are derived for the most part from studies in other settings (e.g., medical training, 

higher education). 

This literature review and meta-analysis differ from recent meta-analyses of distance learning in 

that they 

! Limit the search to studies of Web-based instruction (i.e., eliminating studies of videoand audio-based telecourses or stand-alone, computer-based instruction); 

! Include only studies with random-assignment or controlled quasi-experimental designs; 

and 

! Examine effects only for objective measures of student learning (e.g., discarding effects 

for student or teacher perceptions of learning or course quality, student affect, etc.). 

This analysis and review distinguish between instruction that is offered entirely online and 

instruction that combines online and face-to-face elements. The first of the alternatives to 

classroom-based instruction, entirely online instruction, is attractive on the basis of cost and 

convenience as long as it is as effective as classroom instruction. The second alternative, which 

the online learning field generally refers to as blended or hybrid learning, needs to be more

effective than conventional face-to-face instruction to justify the additional time and costs it 

entails. Because the evaluation criteria for the two types of learning differ, this meta-analysis 

presents separate estimates of mean effect size for the two subsets of studies. 

Literature Search 

The most unexpected finding was that an extensive initial search of the published literature from 

1996 through 2006 found no experimental or controlled quasi-experimental studies that both 

compared the learning effectiveness of online and face-to-face instruction for K–12 students and 

provided sufficient data for inclusion in a meta-analysis. A subsequent search extended the time 

frame for studies through July 2008. 

The computerized searches of online databases and citations in prior meta-analyses of distance 

learning as well as a manual search of the last three years of key journals returned 1,132 

abstracts. In two stages of screening of the abstracts and full texts of the articles, 176 online 

learning research studies published between 1996 and 2008 were identified that used an 

experimental or quasi-experimental design and objectively measured student learning outcomes. 

Of these 176 studies, 99 had at least one contrast between an included online or blended learning 

condition and face-to-face (offline) instruction that potentially could be used in the quantitative 

meta-analysis. Just nine of these 99 involved K–12 learners. The 77 studies without a face-toface condition compared different variations of online learning (without a face-to-face control 

condition) and were set aside for narrative synthesis. 
xiii

Meta-Analysis 

Meta-analysis is a technique for combining the results of multiple experiments or quasiexperiments to obtain a composite estimate of the size of the effect. The result of each 

experiment is expressed as an effect size, which is the difference between the mean for the 

treatment group and the mean for the control group, divided by the pooled standard deviation. Of 

the 99 studies comparing online and face-to-face conditions, 45 provided sufficient data to 

compute or estimate 50 independent effect sizes (some studies included more than one effect). 

Four of the nine studies involving K–12 learners were excluded from the meta-analysis: Two 

were quasi-experiments without statistical control for preexisting group differences; the other 

two failed to provide sufficient information to support computation of an effect size. 

Most of the articles containing the 50 effects in the meta-analysis were published in 2004 or 

more recently. The split between studies of purely online learning and those contrasting blended 

online/face-to-face conditions against face-to-face instruction was fairly even, with 27 effects in 

the first category and 23 in the second. The 50 estimated effect sizes included seven contrasts 

from five studies conducted with K–12 learners—two from eighth-grade students in social 

studies classes, one for eighth- and ninth-grade students taking Algebra I, two from a study of 

middle school students taking Spanish, one for fifth-grade students in science classes in Taiwan, 

and one from elementary-age students in special education classes. The types of learners in the 

remaining studies were about evenly split between college or community college students and 

graduate students or adults receiving professional training. All but two of the studies involved 

formal instruction. The most common subject matter was medicine or health care. Other content 

types were computer science, teacher education, mathematics, languages, science, social science, 

and business. Among the 48 contrasts from studies that indicated the time period over which 

instruction occurred, 19 involved instructional time frames of less than a month, and the 

remainder involved longer periods. In terms of instructional features, the online learning 

conditions in these studies were less likely to be instructor-directed (8 contrasts) than they were 

to be student-directed, independent learning (17 contrasts) or interactive and collaborative in 

nature (22 contrasts). 

Effect sizes were computed or estimated for this final set of 50 contrasts. Among the 50 

individual study effects, 11 were significantly positive, favoring the online or blended learning 

condition. Three contrasts found a statistically significant effect favoring the traditional face-toface condition.1

 

 

1

 When a " < .05 level of significance is used for contrasts, one would expect approximately 1 in 20 contrasts to 

show a significant difference by chance. For 50 contrasts, then, one would expect 2 or 3 significant differences by 

chance. The finding of 3 significant contrasts associated with face-to-face instruction is within the range one 

would expect by chance; the 11 contrasts associated with online or hybrid instruction exceeds what one would 

expect by chance. 
xiv

Narrative Synthesis 

In addition to the meta-analysis comparing online learning conditions with face-to-face 

instruction, analysts reviewed and summarized experimental and quasi-experimental studies 

contrasting different versions of online learning. Some of these studies contrasted purely online 

learning conditions with classes that combined online and face-to-face interactions. Others 

explored online learning with and without elements such as video, online quizzes, assigned 

groups, or guidance for online activities. Five of these studies involved K–12 learners. 

Key Findings 

The main finding from the literature review was that 

! Few rigorous research studies of the effectiveness of online learning for K–12 students 

have been published. A systematic search of the research literature from 1994 through 

2006 found no experimental or controlled quasi-experimental studies comparing the 

learning effects of online versus face-to-face instruction for K–12 students that provide 

sufficient data to compute an effect size. A subsequent search that expanded the time 

frame through July 2008 identified just five published studies meeting meta-analysis 

criteria. 

The meta-analysis of 50 study effects, 43 of which were drawn from research with older learners, 

found that2

 

! Students in online conditions performed modestly better, on average, than those learning 

the same material through traditional face-to-face instruction. Learning outcomes for 

students who engaged in online learning exceeded those of students receiving face-toface instruction, with an average effect size of +0.20 favoring online conditions.3

 The 

mean difference between online and face-to-face conditions across the 50 contrasts is 

statistically significant at the p < .001 level.4 Interpretations of this result, however, 

should take into consideration the fact that online and face-to-face conditions generally 

differed on multiple dimensions, including the amount of time that learners spent on task. 

The advantages observed for online learning conditions therefore may be the product of 

aspects of those treatment conditions other than the instructional delivery medium per se. 

 

2

 The meta-analysis was run also with just the 43 studies with older learners. Results were very similar to those for 

the meta-analysis including all 50 contrasts. Variations in findings when K-12 studies are removed are described 

in footnotes. 3

 The + sign indicates that the outcome for the treatment condition was larger than that for the control condition. A 

– sign before an effect estimate would indicate that students in the control condition had stronger outcomes than 

those in the treatment condition. Cohen (1992) suggests that effect sizes of .20 can be considered “small,” those of 

approximately .50 “medium,” and those of .80 or greater “large.” 4

 The p-value represents the likelihood that an effect of this size or larger will be found by chance if the two 

populations under comparison do not differ. A p-value of less than .05 indicates that there is less than 1 chance in 

20 that a difference of the observed size would be found for samples drawn from populations that do not differ. 
xv

! Instruction combining online and face-to-face elements had a larger advantage relative 

to purely face-to-face instruction than did purely online instruction. The mean effect size 

in studies comparing blended with face-to-face instruction was +0.35, p < .001. This 

effect size is larger than that for studies comparing purely online and purely face-to-face 

conditions, which had an average effect size of +0.05, p =.46. In fact, the learning 

outcomes for students in purely online conditions and those for students in purely face-toface conditions were statistically equivalent. An important issue to keep in mind in 

reviewing these findings is that many studies did not attempt to equate (a) all the 

curriculum materials, (b) aspects of pedagogy and (c) learning time in the treatment and 

control conditions. Indeed, some authors asserted that it would be impossible to have 

done so. Hence, the observed advantage for blended learning conditions is not necessarily 

rooted in the media used per se and may reflect differences in content, pedagogy and 

learning time. 

! Effect sizes were larger for studies in which the online instruction was collaborative or 

instructor-directed than in those studies where online learners worked independently.5

The type of learning experience moderated the size of the online learning effect (Q = 

6.19, p < .05).6 The mean effect sizes for collaborative instruction (+0.25) and for 

instructor-directed instruction (+0.39) were significantly positive whereas the mean effect 

size for independent learning (+0.05) was not. 

! Most of the variations in the way in which different studies implemented online learning 

did not affect student learning outcomes significantly. Analysts examined 13 online 

learning practices as potential sources of variation in the effectiveness of online learning 

compared with face-to-face instruction. Of those variables, the two mentioned above (i.e., 

the use of a blended rather than a purely online approach and instructor-directed or 

collaborative rather than independent, self-directed instruction) were the only statistically 

significant influences on effectiveness. The other 11 online learning practice variables 

that were analyzed did not affect student learning significantly. However, the relatively 

small number of studies contrasting learning outcomes for online and face-to-face 

instruction that included information about any specific aspect of implementation 

impeded efforts to identify online instructional practices that affect learning outcomes. 

! The effectiveness of online learning approaches appears quite broad across different 

content and learner types. Online learning appeared to be an effective option for both 

undergraduates (mean effect of +0.30, p < .001) and for graduate students and 

professionals (+0.10, p < .05) in a wide range of academic and professional studies. 

Though positive, the mean effect size is not significant for the seven contrasts involving 

K–12 students, but the number of K–12 studies is too small to warrant much confidence 

in the mean effect estimate for this learner group. Three of the K–12 studies had 

 

5

 Online experiences in which students explored digital artifacts and controlled the specific material they wanted to 

view were categorized as independent learning experiences. 6

 Online experiences in which students explored digital artifacts and controlled the specific material they wanted to 

view were categorized as “active” learning experiences. This contrast is not statistically significant (p=.13) when 

the five K-12 studies are removed from the analysis. 
xvi

significant effects favoring a blended learning condition, one had a significant negative 

effect favoring face-to-face instruction, and three contrasts did not attain statistical 

significance. The test for learner type as a moderator variable was nonsignificant. No 

significant differences in effectiveness were found that related to the subject of 

instruction. 

! Effect sizes were larger for studies in which the online and face-to-face conditions varied 

in terms of curriculum materials and aspects of instructional approach in addition to the 

medium of instruction. Analysts examined the characteristics of the studies in the metaanalysis to ascertain whether features of the studies’ methodologies could account for 

obtained effects. Six methodological variables were tested as potential moderators: (a) 

sample size, (b) type of knowledge tested, (c) strength of study design, (d) unit of 

assignment to condition, (e) instructor equivalence across conditions, and (f) equivalence 

of curriculum and instructional approach across conditions. Only equivalence of 

curriculum and instruction emerged as a significant moderator variable (Q = 6.85, p < 

.01). Studies in which analysts judged the curriculum and instruction to be identical or 

almost identical in online and face-to-face conditions had smaller effects than those 

studies where the two conditions varied in terms of multiple aspects of instruction (+0.13 

compared with +0.40, respectively). Instruction could differ in terms of the way activities 

were organized (for example as group work in one condition and independent work in 

another) or in the inclusion of instructional resources (such as a simulation or instructor 

lectures) in one condition but not the other. 

The narrative review of experimental and quasi-experimental studies contrasting different online 

learning practices found that the majority of available studies suggest the following: 

! Blended and purely online learning conditions implemented within a single study 

generally result in similar student learning outcomes. When a study contrasts blended 

and purely online conditions, student learning is usually comparable across the two 

conditions. 

! Elements such as video or online quizzes do not appear to influence the amount that 

students learn in online classes. The research does not support the use of some frequently 

recommended online learning practices. Inclusion of more media in an online application 

does not appear to enhance learning. The practice of providing online quizzes does not 

seem to be more effective than other tactics such as assigning homework. 

! Online learning can be enhanced by giving learners control of their interactions with 

media and prompting learner reflection. Studies indicate that manipulations that trigger 

learner activity or learner reflection and self-monitoring of understanding are effective 

when students pursue online learning as individuals. 

! Providing guidance for learning for groups of students appears less successful than does 

using such mechanisms with individual learners. When groups of students are learning 

together online, support mechanisms such as guiding questions generally influence the 

way students interact, but not the amount they learn. 
xvii
xviii

Conclusions 

In recent experimental and quasi-experimental studies contrasting blends of online and face-toface instruction with conventional face-to-face classes, blended instruction has been more 

effective, providing a rationale for the effort required to design and implement blended 

approaches. When used by itself, online learning appears to be as effective as conventional 

classroom instruction, but not more so. 

However, several caveats are in order: Despite what appears to be strong support for blended 

learning applications, the studies in this meta-analysis do not demonstrate that online learning is 

superior as a medium, In many of the studies showing an advantage for blended learning, the 

online and classroom conditions differed in terms of time spent, curriculum and pedagogy. It was 

the combination of elements in the treatment conditions (which was likely to have included 

additional learning time and materials as well as additional opportunities for collaboration) that 

produced the observed learning advantages. At the same time, one should note that online 

learning is much more conducive to the expansion of learning time than is face-to-face 

instruction. 

In addition, although the types of research designs used by the studies in the meta-analysis were 

strong (i.e., experimental or controlled quasi-experimental), many of the studies suffered from 

weaknesses such as small sample sizes; failure to report retention rates for students in the 

conditions being contrasted; and, in many cases, potential bias stemming from the authors’ dual 

roles as experimenters and instructors. 

Finally, the great majority of estimated effect sizes in the meta-analysis are for undergraduate 

and older students, not elementary or secondary learners. Although this meta-analysis did not 

find a significant effect by learner type, when learners’ age groups are considered separately, the 

mean effect size is significantly positive for undergraduate and other older learners but not for 

K–12 students. 

Another consideration is that various online learning implementation practices may have 

differing effectiveness for K–12 learners than they do for older students. It is certainly possible 

that younger online students could benefit from practices (such as embedding feedback, for 

example) that did not have a positive impact for college students and older learners. Without new 

random assignment or controlled quasi-experimental studies of the effects of online learning 

options for K–12 students, policy-makers will lack scientific evidence of the effectiveness of 

these emerging alternatives to face-to-face instruction.

1

1. Introduction

Online learning has roots in the tradition of distance education, which goes back at least 100 

years to the early correspondence courses. With the advent of the Internet and the World Wide 

Web, the potential for reaching learners around the world increased greatly, and today’s online 

learning offers rich educational resources in multiple media and the capability to support both 

real-time and asynchronous communication between instructors and learners as well as among 

different learners. Institutions of higher education and corporate training were quick to adopt 

online learning. Although K–12 school systems lagged behind at first, this sector’s adoption of elearning is now proceeding rapidly. 

The National Center for Education Statistics estimated that 37 percent of school districts had 

students taking technology-supported distance education courses during school year 2004–05 

(Zandberg and Lewis 2008). Enrollments in these courses (which included two-way interactive 

video as well as Internet-based courses), were estimated at 506,950, a 60 percent increase over 

the estimate based on the previous survey for 2002-03 (Selzer and Lewis 2007). Two district 

surveys commissioned by the Sloan Consortium (Picciano and Seaman 2007; 2008) produced 

estimates that 700,000 K–12 public school students took online courses in 2005–06 and over a 

million students did so in 2007–08—a 43 percent increase.7

 Most of these courses were at the 

high school level or in combination elementary-secondary schools (Zandberg and Lewis 2008). 

These district numbers, however, do not fully capture the popularity of programs that are entirely 

online. By fall 2007, 28 states had online virtual high school programs (Tucker 2007). The 

largest of these, the Florida Virtual School, served over 60,000 students in 2007–08. In addition, 

enrollment figures for courses or high school programs that are entirely online reflect just one 

part of overall K–12 online learning. Increasingly, regular classroom teachers are incorporating 

online teaching and learning activities into their instruction. 

Online learning has become popular because of its potential for providing more flexible access to

content and instruction at any time, from any place. Frequently, the focus entails (a) increasing 

the availability of learning experiences for learners who cannot or choose not to attend traditional 

face-to-face offerings, (b) assembling and disseminating instructional content more costefficiently, or (c) enabling instructors to handle more students while maintaining learning 

outcome quality that is equivalent to that of comparable face-to-face instruction. 

Different technology applications are used to support different models of online learning. One 

class of online learning models uses asynchronous communication tools (e.g., e-mail, threaded 

discussion boards, newsgroups) to allow users to contribute at their convenience. Synchronous 

technologies (e.g., webcasting, chat rooms, desktop audio/video technology) are used to 

approximate face-to-face teaching strategies such as delivering lectures and holding meetings 

with groups of students. Earlier online programs tended to implement one model or the other. 

More recent applications tend to combine multiple forms of synchronous and asynchronous 

online interactions as well as occasional face-to-face interactions. 

 

7

 The Sloan Foundation surveys had very low response rates, suggesting the need for caution with respect to their 

numerical estimates. 
2

In addition, online learning offerings are being designed to enhance the quality of learning 

experiences and outcomes. One common conjecture is that learning a complex body of 

knowledge effectively requires a community of learners (Bransford, Brown and Cocking 1999; 

Riel and Polin 2004; Schwen and Hara 2004; Vrasidas and Glass 2004) and that online 

technologies can be used to expand and support such communities. Another conjecture is that 

asynchronous discourse is inherently self-reflective and therefore more conducive to deep 

learning than is synchronous discourse (Harlen and Doubler 2004; Hiltz and Goldman 2005; 

Jaffee et al. 2006). 

This literature review and meta-analysis have been guided by four research questions: 

1. How does the effectiveness of online learning compare with that of face-to-face 

instruction? 

2. Does supplementing face-to-face instruction with online instruction enhance learning? 

3. What practices are associated with more effective online learning? 

4. What conditions influence the effectiveness of online learning? 

Context for the Meta-analysis and Literature Review 

The meta-analysis and literature review reported here are part of the broader Evaluation of 

Evidence-Based Practices in Online Learning study that SRI International is conducting for the 

Policy and Program Studies Service of the U.S. Department of Education. The overall goal of the 

study is to provide research-based guidance to policy-makers, administrators and educators for 

implementing online learning for K–12 education. This literature search, analysis, and review 

has expanded the set of studies available for analysis by also addressing the literature concerning 

online learning in career technical education, medical and higher education, corporate and 

military training, and K–12 education. 

In addition to examining the learning effects of online learning, this meta-analysis has considered 

the conditions and practices associated with differences in effectiveness. Conditions are those 

features of the context within which the online technology is implemented that are relatively 

impervious to change. Conditions include the year in which the intervention took place, the 

learners’ demographic characteristics, the teacher’s or instructor’s qualifications, and state 

accountability systems. In contrast, practices concern how online learning is implemented (e.g., 

whether or not an online course facilitator is used). In choosing whether or where to use online 

learning (e.g., to teach mathematics for high school students, to teach a second language to 

elementary students), it is important to understand the degree of effectiveness of online learning 

under differing conditions. In deciding how to implement online learning, it is important to 

understand the practices that research suggests will increase effectiveness (e.g., community 

building among participants, use of an online facilitator, blending work and training). 
3

Conceptual Framework for Online Learning 

Modern online learning includes offerings that run the gamut from conventional didactic lectures 

or textbook-like information delivered over the Web to Internet-based collaborative role-playing 

in social simulations and highly interactive multiplayer strategy games. Examples include 

primary-grade students working on beginning reading skills over the Internet, middle school 

students collaborating with practicing scientists in the design and conduct of research, and 

teenagers who dropped out of high school taking courses online to attain the credits needed for 

graduation. The teachers of K–12 students may also participate in online education, logging in to 

online communities and reference centers and earning inservice professional development credit 

online. 

To guide the literature search and review, the research team developed a conceptual framework 

identifying three key components describing online learning: (a) whether the activity served as a 

replacement for or an enhancement to conventional face-to-face instruction, (b) the type of 

learning experience (pedagogical approach), and (c) whether communication was primarily 

synchronous or asynchronous. Each component is described in more detail below. 

One of the most basic characteristics for classifying online activities is its objective—whether 

the activity serves as a replacement for face-to-face instruction (e.g., a virtual course) or as an 

enhancement of the face-to-face learning experience (i.e., online learning activities that are part 

of a course given face-to-face). This distinction is important because the two types of 

applications have different objectives. A replacement application that is equivalent to 

conventional instruction in terms of learning outcomes is considered a success if it provides 

learning online without sacrificing student achievement. If student outcomes are the same 

whether a course is taken online or face-to-face, then online instruction can be used costeffectively in settings where too few students are situated in a particular geographic locale to 

warrant an on-site instructor (e.g., rural students, students in specialized courses). In contrast, 

online enhancement activities that produce learning outcomes that are only equivalent to (not 

better than) those resulting from face-to-face instruction alone would be considered a waste of 

time and money because the addition does not improve student outcomes. 

A second important dimension is the type of learning experience, which depends on who (or 

what) determines the way learners acquire knowledge. Learning experiences can be classified in 

terms of the amount of control that the student has over the content and nature of the learning 

activity. In traditional didactic or expository learning experiences, content is transmitted to the 

student by a lecture, written material, or other mechanisms. Such conventional instruction is 

often contrasted with active learning in which the student has control of what and how he or she 

learns. Another category of learning experiences stresses collaborative or interactive learning

activity in which the nature of the learning content is emergent as learners interact with one 

another and with a teacher or other knowledge sources. Technologies can support any of these 

three types of learning experience: 

! Expository instruction—Digital devices transmit knowledge. 

! Active learning—The learner builds knowledge through inquiry-based manipulation of 

digital artifacts such as online drills, simulations, games, or microworlds. 
4

! Interactive learning—The learner builds knowledge through inquiry-based collaborative 

interaction with other learners; teachers become co-learners and act as facilitators. 

This dimension of learning-experience type is closely linked to the concept of learner control 

explored by Zhang (2005). Typically, in expository instruction, the technology delivers the 

content. In active learning, the technology allows students to control digital artifacts to explore 

information or address problems. In interactive learning, technology mediates human interaction 

either synchronously or asynchronously; learning emerges through interactions with other 

students and the technology. 

The learner-control category of interactive learning experiences is related to the so-called “fifth 

generation” of distance learning, which stresses a flexible combination of independent and group 

learning activities. Researchers are now using terms such as “distributed learning” (Dede 2006) 

or “learning communities” to refer to orchestrated mixtures of face-to-face and virtual 

interactions among a cohort of learners led by one or more instructors, facilitators or coaches 

over an extended period of time (from weeks to years). 

Finally, a third characteristic commonly used to categorize online learning activities is the extent 

to which the activity is synchronous, with instruction occurring in real time whether in a physical 

or a virtual place, or asynchronous, with a time lag between the presentation of instructional 

stimuli and student responses. Exhibit 1 illustrates the three dimensions in the framework 

guiding this meta-analysis of online learning offerings. The descriptive columns in the table 

illustrate uses of online learning comprising dimensions of each possible combination of the 

learning experience, synchronicity, and objective (an alternative or a supplement to face-to-face 

instruction). 
5

Exhibit 1. Conceptual Framework for Online Learning 

Learning 

Experience 

Dimension Synchronicity

Face-to-Face 

Alternative 

Face-to-Face 

Enhancement 

Expository

Synchronous

Live, one-way webcast of online lecture course 

with limited learner control (e.g., students 

proceed through materials in set sequence)

Viewing webcasts to supplement in-class learning 

activities 

Asynchronous Math course taught through online video lectures 

that students can access on their own schedule

Online lectures on advanced topics made 

available as a resource for students in a 

conventional math class

Active

Synchronous

Learning how to troubleshoot a new type of 

computer system by consulting experts through 

live chat 

Chatting with experts as the culminating activity for 

a curriculum unit on network administration 

Asynchronous Social studies course taught entirely through 

Web quests that explore issues in U.S. history

Web quest options offered as an enrichment 

activity for students completing their regular social 

studies assignments early

Interactive

Synchronous

Health-care course taught entirely through an 

online, collaborative patient management 

simulation that multiple students interact with at 

the same time

Supplementing a lecture-based course through a 

session spent with a collaborative online 

simulation used by small groups of students 

Asynchronous

Professional development for science teachers 

through “threaded” discussions and message 

boards on topics identified by participants

Supplemental, threaded discussions for preservice teachers participating in a face-to-face 

course on science methods

Exhibit reads: Online learning applications can be characterized in terms of (a) the kind of learning experience they provide, (b) whether 

computer-mediated instruction is primarily synchronous or asynchronous and (c) whether they are intended as an alternative or a supplement to 

face-to-face instruction. 
6

Many other features also apply to online learning, including the type of setting (classroom, 

home, informal), the nature of the content (both the subject area and the type of learning such as 

fact, concept, procedure or strategy), and the technology involved (e.g., audio/video streaming, 

Internet telephony, podcasting, chat, simulations, videoconferencing, shared graphical 

whiteboard, screen sharing). 

The dimensions in the framework in Exhibit 1 were derived from prior meta-analyses in distance 

learning. Bernard et al. (2004) found advantages for asynchronous over synchronous distance 

education. In examining a different set of studies, Zhao et al. (2005) found that studies of 

distance-learning applications that combined synchronous and asynchronous communication 

tended to report more positive effects than did studies of distance learning applications with just 

one of these interaction types.8

 Zhao et al. also found (a) advantages for blended learning (called 

“Face-to-Face Enhancement” in the Exhibit 1 framework) over purely online learning 

experiences and (b) advantages for courses with more instructor involvement compared with 

more “canned” applications that provide expository learning experiences. Thus, the three 

dimensions in Exhibit 1 capture some of the most important kinds of variation in distance 

learning and together provide a manageable framework for differentiating among the broad array 

of online activities in practice today. 

Findings From Prior Meta-Analyses 

Prior meta-analyses of distance education (including online learning studies and studies of other 

forms of distance education) and of Web-based or online learning have been conducted. Overall, 

results from Bernard et al. (2004) and other reviews of the distance education literature 

(Cavanaugh 2001; Moore 1994) indicate no significant differences in effectiveness between 

distance education and face-to-face education, suggesting that distance education, when it is the 

only option available, can successfully replace face-to-face instruction. Findings of a recent 

meta-analysis of job-related courses comparing Web-based and classroom-based learning 

(Sitzmann et al. 2006) were even more positive. They found online learning to be superior to 

classroom-based instruction in terms of declarative knowledge outcomes, with the two being 

equivalent in terms of procedural learning. 

However, a general conclusion that distance and face-to-face instruction result in essentially 

similar learning ignores differences in findings across various studies. Bernard et al. (2004) 

found tremendous variability in effect sizes (an effect size is the difference between the mean for 

the treatment group and the mean for the control group, divided by the pooled standard 

deviation), which ranged from –1.31 to +1.41.9

 From their meta-analysis, which included coding 

for a wide range of instructional and other characteristics, the researchers concluded that selected 

 

8

 Both of these meta-analyses included video-based distance learning as well as Web-based learning and also 

included studies in which the outcome measure was student satisfaction, attitude or other nonlearning measures. 

The meta-analysis reported here is restricted to an analysis of effect sizes for objective student learning measures 

in experimental, controlled quasi-experimental, and crossover studies of applications with Web-based 

components. 9

 Cohen (1992) suggests that effect sizes of .20 can be considered “small,” those of approximately .50 “medium,” 

and those of .80 or greater “large.” 
7

conditions and practices were associated with differences in outcomes. For example, they found 

that distance education that used synchronous instruction was significantly negative in its effect, 

with an average effect size of –0.10, whereas the average effect size for studies using 

asynchronous instruction was significantly positive (+0.05). However, the studies that Bernard et 

al. categorized as using synchronous communication involved “yoked” classrooms; that is, the 

instructor’s classroom was the center of the activity, and one or more distant classrooms 

interacted with it in “hub and spoke” fashion. These satellite classes are markedly different from 

today’s Web-based communication among the multiple nodes in a “learning network.” 

Machtmes and Asher’s earlier (2000) meta-analysis of telecourses sheds light on this issue.10

Although detecting no difference between distance and face-to-face learning overall, they found 

results more favorable for telecourses when classrooms had two-way, as opposed to one-way, 

interactions. 

Although earlier meta-analyses of distance education found it equivalent to classroom instruction 

(as noted above), several reviewers have suggested that this pattern may change. They argue that 

online learning as practiced in the 21st century can be expected to outperform earlier forms of 

distance education in terms of effects on learning (Zhao et al. 2005). 

The meta-analysis reported here differs from earlier meta-analyses because its focus has been 

restricted to studies that did the following: 

! Investigated significant use of the Web for instruction 

! Had an objective learning measure as the outcome measure 

! Met higher quality criteria in terms of study design (i.e., an experimental or controlled 

quasi-experimental design) 

Structure of the Report 

Chapter 2 describes the methods used in searching for appropriate research articles, in screening 

those articles for relevance and study quality, in coding study features, and in calculating effect 

sizes. Chapter 3 describes the 50 study effects identified through the article search and screening 

and presents findings in the form of effect sizes for studies contrasting purely online or blended 

learning conditions with face-to-face instruction. Chapter 4 provides a qualitative narrative 

synthesis of research studies comparing variations of online learning interventions. Finally, 

chapter 5 discusses the implications of the literature search and meta-analysis for future studies 

of online learning and for K–12 online learning practices. 

 

10 Like the present meta-analysis, Machtmes and Asher limited their study corpus to experiments or quasiexperiments with an achievement measure as the learning outcome. 

9

2. Methodology 

This chapter describes the procedures used to search for, screen and code controlled studies of 

the effectiveness of online learning. The products of these search, screening and coding activities 

were used for the meta-analysis and narrative literature review, which are described in chapters 3 

and 4, respectively. 

Definition of Online Learning 

For this review, online learning is defined as learning that takes place partially or entirely over 

the Internet. This definition excludes purely print-based correspondence education, broadcast 

television or radio, videoconferencing, videocassettes, and stand-alone educational software 

programs that do not have a significant Internet-based instructional component. 

In contrast to previous meta-analyses, this review distinguishes between two purposes for online 

learning: 

! Learning conducted totally online as a substitute or alternative to face-to-face learning 

! Online learning components that are combined or blended (sometimes called “hybrid”) 

with face-to-face instruction to provide learning enhancement

As indicated in chapter 1, this distinction was made because of the different implications of 

finding a null effect (i.e., no difference in effects between the treatment and the control group) 

under the two circumstances. Equivalence between online learning and face-to-face learning 

justifies using online alternatives, but online enhancements need to be justified by superior 

learning outcomes. These two purposes of online learning defined the first two categories of 

study in the literature search: 

! Studies comparing an online learning condition with a face-to-face control condition 

(Category 1) 

! Studies comparing a blended condition with a face-to-face control condition without the 

online learning components (Category 2). 

In addition, researchers sought experimental and controlled quasi-experimental studies that 

compared the effectiveness of different online learning practices. This third study category 

consisted of the following: 

! Studies testing the learning effects of variations in online learning practices such as 

online learning with and without interactive video (Category 3). 
10

Data Sources and Search Strategies 

Relevant studies were located through a comprehensive search of publicly available literature 

published from 1996 through July 2008.11 Searches of dissertations were limited to those 

published from 2005 through July 2008 to allow researchers to use UMI ProQuest Digital 

Dissertations for retrieval. 

Electronic Database Searches 

Using a common set of keywords, searches were performed in five electronic research databases: 

ERIC, PsycINFO, PubMed, ABI/INFORM, and UMI ProQuest Digital Dissertations. The 

appendix lists the terms used for the initial electronic database search and for additional searches 

for studies of online learning in the areas of career technical education and teacher education. 

Additional Search Activities 

The electronic database searches were supplemented with a review of articles cited in recent 

meta-analyses and narrative syntheses of research on distance learning (Bernard et al. 2004; 

Cavanaugh et al. 2004; Childs 2001; Sitzmann et al. 2006; Tallent-Runnels et al. 2006; WestEd 

with Edvance Research 2008; Wisher and Olson 2003; Zhao et al. 2005), including those for 

teacher professional development and career technical education (Whitehouse et al. 2006; Zirkle 

2003). The analysts examined references from these reviews to identify studies that might meet 

the criteria for inclusion in the present review. 

Abstracts were manually reviewed for articles published since 2005 in the following key 

journals: American Journal of Distance Education, Journal of Distance Education (Canada), 

Distance Education (Australia), International Review of Research in Distance and Open 

Education, and Journal of Asynchronous Learning Networks. In addition, the Journal of 

Technology and Teacher Education and Career and Technical Education Research were 

searched manually. Finally, the Google Scholar search engine was used with a series of 

keywords related to online learning (available from the authors). Article abstracts retrieved 

through these additional search activities were reviewed to remove duplicates of articles 

identified earlier. 

 

11 Literature searches were performed in two waves: in March 2007 for studies published from 1996–2006 and in 

July 2008 for studies published from 2007 to July 2008. 
11

Screening Process 

Screening of the research studies obtained through the search process described above was 

carried out in two stages. The intent of the two-stage approach was to gain efficiency without 

risking exclusion of potentially relevant, high-quality studies of online learning effects. 

Initial Screen for Abstracts From Electronic Databases 

The initial electronic database searches (excluding the additional searches conducted for teacher 

professional development and career technical education) yielded 1,132 articles.12 Citation 

information and abstracts of these studies were examined to ascertain whether they met the 

following three initial inclusion criteria: 

1. Does the study address online learning as this review defines it? 

2. Does the study appear to use a controlled design (experimental/quasi-experimental 

design)? 

3. Does the study report data on student achievement or another learning outcome? 

At this early stage, analysts gave studies “the benefit of the doubt,” retaining those that were not 

clearly outside the inclusion criteria on the basis of their citations and abstracts. As a result of 

this screening, 316 articles were retained and 816 articles were excluded. During this initial 

screen, 45 percent of the excluded articles were removed primarily because they did not have a 

controlled design. Twenty-six percent of excluded articles were eliminated because they did not 

report learning outcomes for treatment and control groups. Twenty-three percent were eliminated 

because their intervention did not qualify as online learning, given the definition used for this 

meta-analysis and review. The remaining six percent of excluded articles posed other difficulties, 

such as being written in a language other than English. 

Full-text Screen 

From the other data sources (i.e., references in earlier reviews, manual review of key journals, 

recommendation from a study advisor, and Google Scholar searches), researchers identified and 

retrieved an additional 186 articles, yielding a total of 502 articles that they subjected to a fulltext screening for possible inclusion in the analysis.Nine analysts who were trained on a set of 

full-text screening criteria reviewed the 502 articles for both topical relevance and study quality. 

A study had to meet content relevance criteria to be included in the meta-analysis. Thus, 

qualifying studies had to 

1. Involve learning that took place over the Internet. The use of the Internet had to be a 

major part of the intervention. Studies in which the Internet was only an incidental 

component of the intervention were excluded. In operational terms, to qualify as online 

learning, a study treatment needed to provide at least a quarter of the 

 

12 This number includes multiple instances of the same study identified in different databases. 
12

instruction/learning of the content assessed by the study’s learning measure by means 

of the Internet. 

2. Contrast conditions that varied in terms of use of online learning. Learning outcomes 

had to be compared against conditions falling into at least one of two study categories: 

Category 1, online learning compared with offline/face-to-face learning, and Category 

2, a combination of online plus offline/face-to-face learning (i.e., blended learning) 

compared with offline/face-to-face learning alone. 

3. Describe an intervention study that had been completed. Descriptions of study designs, 

evaluation plans or theoretical frameworks were excluded. The length of the 

intervention/treatment could vary from a few hours to a quarter, semester, year or 

longer. 

4. Report a learning outcome that was measured for both treatment and control groups. 

A learning outcome needed to be measured in the same way across study conditions. A 

study was excluded if it explicitly indicated that different examinations were used for 

the treatment and control groups. The measure had to be objective and direct; learner or 

teacher/instructor self-report of learning was not considered a direct measure. 

Examples of learning outcome measures included scores on standardized tests, scores 

on researcher-created assessments, grades/scores on teacher-created assessments (e.g., 

assignments, midterm/final exams), and grades or grade point averages. Examples of 

learning outcome measures for teacher learners (in addition to those accepted as 

student outcomes) included assessments of content knowledge, analysis of lesson plans 

or other materials related to the intervention, observation (or logs) of class activities, 

analysis of portfolios, or supervisor’s rating of job performance. Studies that used only 

nonlearning outcome measures (e.g., attitude, retention, attendance, level of 

learner/instructor satisfaction) were excluded. 

Studies also had to meet basic Quality (method) criteria to be included. Thus, qualifying studies 

had to 

5. Use a controlled design (experimental or quasi-experimental). Design studies, 

exploratory studies or case studies that did not use a controlled research design were 

excluded. For quasi-experimental designs, the analysis of the effects of the intervention 

had to include statistical controls for possible differences between the treatment and 

control groups in terms of prior achievement. 

6. Report sufficient data for effect size calculation or estimation as specified in the 

guidelines provided by the What Works Clearinghouse (2007) and by Lipsey and 

Wilson (2000). 

Studies that contrasted different versions of online learning (Category 3) needed to meet Criteria 

1 and 3–5 to be included in the narrative research summary. 
13

An analyst read each full text, and all borderline cases were discussed and resolved either at 

project meetings or through consultation with task leaders. To prevent studies from being 

mistakenly screened out, two analysts coded studies on features that were deemed to require 

significant degrees of inference. These features consisted of the following: 

! Failure to have students use the Internet for a significant portion of the time that they 

spent learning the content assessed by the study’s learning measure 

! Lack of statistical control for prior abilities in quasi-experiments 

From the 502 articles, analysts identified 522 independent studies (some articles reported more 

than one study). When the same study was reported in different publication formats (e.g., 

conference paper and journal article), only the more formal journal article was retained for the 

analysis. 

Of the 522 studies, 176 met all the criteria of the full-text screening process. Exhibit 2 shows the 

bases for exclusion for the 346 studies that did not meet all the criteria. 

Exhibit 2. Bases for Excluding Studies During the Full-Text Screening Process 

Primary Reason for Exclusion

Number 

Excluded

Percentage 

Excluded

Did not use statistical control 137 39

Was not online as defined in this review 90 26

Did not analyze learning outcomes 52 15

Did not have a comparison group that received a comparable 

treatment 22 7

Did not fit into any of the three study categories 39 11

Excluded for other reasonsa 6 2

Exhibit reads: The most common reason for a study’s exclusion from the analysis was failure to use statistical 

control (in a quasi-experiment). 

a

Other reasons for exclusion included (a) did not provide enough information, (b) was written in a language other than 

English, and (c) used different learning outcome measures for the treatment and control groups. 

Effect Size Extraction 

Of the 176 independent studies, 99 had at least one contrast between online learning and face-toface/offline learning (Category 1) or between blended learning and face-to-face/offline learning 

(Category 2). These studies were subjected to quantitative analysis to extract effect sizes. 

Of the 99 studies, only nine were conducted with K–12 students (Chang 2008; Englert et al. 

2007; Long and Jennings 2005; O’Dwyer, Carey and Kleiman 2007; Parker 1999; Rockman et 

al. 2007; Stevens 1999; Sun, Lin and Yu 2008; Uzunboylu 2004). Of them, four were excluded 

from the meta-analysis: Chang (2008), Parker (1999), and Uzunboylu (2004) did not provide 

sufficient statistical data to compute effect sizes, and the Stevens (1999) study was a quasiexperiment without a statistical control for potential existing differences in achievement. 
14

An effect size is similar to a z-score in that it is expressed in terms of units of standard deviation. 

It is defined as the difference between the treatment and control means, divided by the pooled 

standard deviation. Effect sizes can be calculated (a) from the means and standard deviations for 

the two groups or (b) on the basis of information provided in statistical tests such as t-tests and 

analyses of variance. Following the guidelines from the What Works Clearinghouse (2007) and 

Lipsey and Wilson (2000), numerical and statistical data contained in the studies were extracted 

so that Comprehensive Meta-Analysis software (Biostat Solutions 2006) could be used to 

calculate effect sizes (g). The precision of each effect estimate was determined by using the 

estimated standard error of the mean to calculate the 95-percent confidence interval for each 

effect. 

The review of the 99 studies to obtain the data for calculating effect size produced 50 

independent effect sizes (27 for Category 1 and 23 for Category 2) from 45 studies. Fifty-four 

studies did not report sufficient data to support calculating effect size. 

Coding of Study Features 

All studies that provided enough data to compute an effect size were coded for their study 

features and for study quality. Building on the project’s conceptual framework and the coding 

schemes used in several earlier meta-analyses (Bernard et al. 2004; Sitzmann et al. 2006), a 

coding structure was developed and pilot-tested with several studies. The top-level coding 

structure, incorporating refinements made after pilot testing, is shown in Exhibit A-4 of the 

appendix. 

To determine interrater reliability, two researchers coded 20 percent of the studies, achieving an 

interrater reliability of 86 percent across those studies. Analysis of coder disagreements resulted 

in the refinement of some definitions and decision rules for some codes; other codes that 

required information that articles did not provide or that proved difficult to code reliably were 

eliminated (e.g., whether or not the instructor was certified). A single researcher coded the 

remaining studies. 
15

Data Analysis 

Before combining effects from multiple contrasts, effect sizes were weighted to avoid undue 

influence of studies with small sample sizes (Hedges and Olkin 1985). For the total set of 50 

contrasts and for each subset of contrasts being investigated, a weighted mean effect size 

(Hedges’ g+) was computed by weighting the effect size for each study contrast by the inverse of 

its variance. The precision of each mean effect estimate was determined by using the estimated 

standard error of the mean to calculate the 95 percent confidence interval. Using a fixed-effects 

model, the heterogeneity of the effect size distribution (the Q-statistic) was computed to indicate 

the extent to which variation in effect sizes was not explained by sampling error alone. 

Next, a series of post-hoc subgroup and moderator variable analyses were conducted using the 

Comprehensive Meta-Analysis software. A mixed-effects model was used for these analyses to 

model within-group variation.13 A between-group heterogeneity statistic (QBetween) was computed 

to test for statistical differences in the weighted mean effect sizes for various subsets of the 

effects (e.g., studies using blended as opposed to purely online learning for the treatment group). 

Chapter 3 describes the results of these analyses.

 

13 Meta-analysts need to choose between a mixed-effects and a fixed-effects model for investigating moderator 

variables. A fixed-effects analysis is more sensitive to differences related to moderator variables, but has a greater 

likelihood of producing Type I errors (falsely rejecting the null hypothesis). The mixed-effects model reduces the 

likelihood of Type I errors by adding a random constant to the standard errors, but does so at the cost of 

increasing the likelihood of Type II errors (incorrectly accepting the null hypothesis). Analysts chose the more 

conservative mixed-effects model for this investigation of moderator variables. 

17

3. Findings 

This chapter presents the results of the meta-analysis of controlled studies that compared the 

effectiveness of online learning with that of face-to-face instruction. The next chapter presents a 

narrative synthesis of studies that compared different versions of online learning with each other 

rather than with a face-to-face control condition. 

Nature of the Studies in the Meta-Analysis 

As indicated in chapter 2, 50 independent effect sizes could be abstracted from the study corpus 

of 45 studies.14 The number of students in the studies included in the meta-analysis ranged from 

16 to 1,857, but most of the studies were modest in scope. Although large-scale applications of 

online learning have emerged, only five studies in the meta-analysis corpus included more than 

400 learners. The types of learners in these studies were about evenly split between students in 

college or earlier years of education and learners in graduate programs or professional training. 

The average learner age ranged from 13 to 44. Nearly all the studies involved formal instruction, 

with the most common subject matter being medicine or health care. Other content types 

included computer science, teacher education, social science, mathematics, languages, science 

and business. Roughly half of the learners were taking the instruction for credit or as an 

academic requirement. Of the 48 contrasts for which the study indicated the length of instruction, 

19 involved instructional time frames of less than a month and the remainder involved longer 

periods. 

In terms of instructional features, the online learning conditions in these studies were less likely 

to be instructor-directed (8 contrasts) than they were to be student-directed, independent learning 

(17 contrasts) or interactive and collaborative in nature (22 contrasts). Online learners typically 

had opportunities to practice skills or test their knowledge (41 effects were from studies 

reporting such opportunities). Opportunities for learners to receive feedback were less common; 

however, it was reported in the studies associated with 23 effects. The opportunity for online 

learners to have face-to-face contact with the instructor during the time frame of the course was 

present in the case of 21 out of 50 effects. The details of instructional media and communication 

options available to online learners were absent in many of the study narratives. Among the 50 

contrasts, analysts could document the presence of one-way video or audio in the online 

condition for 14 effects. Similarly, 16 contrasts involved online conditions which allowed 

students to communicate with the instructor with asynchronous communication only; 8 allowed 

both asynchronous and synchronous online communication; and 26 contrasts came from studies 

that did not document the types of online communication provided between the instructor and 

learners. 

 

14 After the first literature search, which yielded 29 independent effects, the research team ran additional analyses to 

find out how many more studies could be included if the study design criterion were relaxed to include quasiexperiments with pre- and posttests with no statistical adjustments made for preexisting differences. The relaxed 

standard would have increased the corpus for analysis by just 10 studies, nearly all of which were in Category 1 

and which had more positive effect sizes than the Category 1 studies with stronger analytic designs. Analysts 

decided not to include those studies in the meta-analysis. Instead, the study corpus was enlarged by conducting a 

second literature search in July 2008. 
18

Among the 50 individual contrasts between online and face-to-face instruction, 11 were 

significantly positive, favoring the online or blended learning condition. Three significant 

negative effects favored traditional face-to-face instruction. The fact that multiple comparisons 

were conducted should be kept in mind when interpreting this pattern of findings. Because 

analysts used a " < .05 level of significance for testing differences, one would expect 

approximately 1 in 20 contrasts to show a significant difference by chance alone. For 50 

contrasts, then, one would expect 2 or 3 significant differences by chance. The finding of 3 

significant contrasts favoring face-to-face instruction is within the range one would expect by 

chance; the 11 contrasts favoring online or hybrid instruction exceeds what one would expect by 

chance. 

Exhibit 3 illustrates the 50 effect sizes derived from the 45 articles.15 Exhibits 4a and 4b present 

the effect sizes for Category 1 (purely online versus face-to-face) and Category 2 (blended versus 

face-to-face) studies, respectively, along with standard errors, statistical significance, and the 95-

percent confidence interval. 

Main Effects 

The overall finding of the meta-analysis is that classes with online learning (whether taught 

completely online or blended) on average produce stronger student learning outcomes than do 

classes with solely face-to-face instruction. The mean effect size for all 50 contrasts was +0.20, p

< .001. 

The conceptual framework for this study, which distinguishes between purely online and blended 

forms of instruction, calls for creating subsets of the effect estimates to address two more 

nuanced research questions: 

! How does the effectiveness of online learning compare with that of face to-face 

instruction? Looking only at the 27 Category 1 effects that compared a purely online 

condition with face-to-face instruction, analysts found a mean effect of +0.05, p =.46. 

This finding is similar to that of previous summaries of distance learning (generally from 

pre-Internet studies), in finding that instruction conducted entirely online is as effective 

as classroom instruction but no better. 

 

15 Some references appear twice in Exhibit 3 because multiple effect sizes were extracted from the same article. 

Davis et al. (1999) and Caldwell (2006) each included two contrasts—online versus face-to-face (Category 1) and 

blended versus face-to-face (Category 2). Rockman et al. (2007) and Schilling et al. (2006) report findings for two 

distinct learning measures. Long and Jennings (2005) report findings from two distinct experiments, a “wave 1” in 

which teachers were implementing online learning for the first time and a “wave 2” in which teachers 

implemented online learning a second time with new groups of students. 
19

! Does supplementing face-to-face instruction with online instruction enhance learning?

For the 23 Category 2 contrasts that compared blended conditions of online plus face-toface learning with face-to-face instruction alone, the mean effect size of +0.35 was 

significant (p < .0001). Blends of online and face-to-face instruction, on average, had 

stronger learning outcomes than did face-to-face instruction alone. 

A test of the difference between Category 1 and Category 2 studies found that the mean effect 

size was larger for contrasts pitting blended learning against face-to-face instruction (g+ = +0.35) 

than for those of purely online versus face-to-face instruction (g+ = +0.05); the difference 

between the two subsets of studies was statistically significant (Q = 8.37, p < .01). 
20

Exhibit 3. Effect Sizes for Contrasts in the Meta-Analysis 

Exhibit reads: The effect size estimate for Schoenfeld-Tacher, McConnell and Graham (2001) was +0.80 

with a 95 percent probability that the true effect size lies between -0.10 and +1.70. 
21

Exhibit 4a. Purely Online Versus Face-to-Face (Category 1) Studies Included in the Meta-Analysis 

Authors Title Effect Size

95-Percent 

Confidence 

Interval

Test of Null 

Hypothesis

(2-tail)

Retention 

Rate 

(percentage) Number 

of Units 

Assigneda g SE

Lower 

Limit

Upper 

Limit Z-Value Online

Face-toFace

Beeckman et al. 

(2008)

Pressure ulcers: E-learning to improve 

classification by nurses and nursing students +0.294 0.097 0.104 0.484 3.03** Unknown Unknown

426 

participants

Bello et al. (2005) Online vs. live methods for teaching difficult 

airway management to anesthesiology residents +0.278 0.265 -0.241 0.797 1.05 100 100

56 

participants

Benjamin et al. 

(2007)

A randomized controlled trial comparing Web to 

in-person training for child care health 

consultants

+0.046 0.340 -0.620 0.713 0.14 Unknown Unknown

23 

participants

Beyea et al. (2008) Evaluation of a particle repositioning maneuver 

Web-based teaching module +0.790 0.493 -0.176 1.756 1.60 Unknown Unknown

17–20 

participantsb

Caldwell (2006) A comparative study of traditional, Web-based 

and online instructional modalities in a computer 

programming course +0.132 0.310 -0.476 0.740 0.43 100 100 60 students

Cavus, Uzonboylu 

and Ibrahim (2007)

Assessing the success rate of students using a 

learning management system together with a 

collaborative tool in Web-based teaching of 

programming languages +0.466 0.335 -0.190 1.122 1.39 Unknown Unknown 54 students

Davis et al. (1999) Developing online courses: A comparison of 

Web-based instruction with traditional instruction -0.379 0.339 -1.042 0.285 -1.12 Unknown Unknown

2 courses/ 

classrooms

Hairston (2007) Employees’ attitudes toward e-learning: 

Implications for policy in industry environments +0.028 0.155 -0.275 0.331 0.18 70 58.33 168 participants

Harris et al. (2008) Educating generalist physicians about chronic 

pain with live experts and online education -0.285 0.252 -0.779 0.209 -1.13 84.21 94.44 62 participants

Hugenholtzet al. 

(2008)

Effectiveness of e-learning in continuing medical 

education for occupational physicians +0.106 0.233 -0.351 0.564 0.46 Unknown Unknown 72 participants

Jang et al. (2005) Effects of a Web-based teaching method on 

undergraduate nursing students’ learning of 

electrocardiography -0.530 0.197 -0.917 -0.143 -2.69** 85.71 87.93 105 students
22

Exhibit 4a. Purely Online Versus Face-to-Face (Category 1) Studies Included in the Meta-Analysis (continued) 

Authors Title Effect Size

95-Percent 

Confidence 

Interval

Test of Null 

Hypothesis

(2-tail)

Retention 

Rate 

(percentage) Number 

of Units 

Assigneda g SE

Lower 

Limit

Upper 

Limit Z-Value Online

Face-toFace

Lowry (2007) Effects of online versus face-to-face 

professional development with a team-based 

learning community approach on teachers’ 

application of a new instructional practice -0.281 0.335 -0.937 0.370 -0.84 80 93.55 53 students

Mentzer, Cryan and 

Teclehaimanot 

(2007)

A comparison of face-to-face and Web-based 

classrooms 

-0.796 0.339 -1.460 -0.131 -2.35* Unknown Unknown 36 students

Nguyen et al. 

(2008)

Randomized controlled trial of an Internet-based 

versus face-to-face dyspnea self-management 

program for patients with chronic obstructive 

pulmonary disease: Pilot study +0.292 0.316 -0.327 0.910 0.93 Unknown Unknown

39 

participants

Ocker and 

Yaverbaum (1999)

Asynchronous computer-mediated 

communication versus face-to-face 

collaboration: Results on student learning, 

quality and satisfaction -0.030 0.214 -0.449 0.389 -0.14 Unknown Unknown 43 students

Padalino and Peres 

(2007)

E-learning: A comparative study for knowledge 

apprehension among nurses 0.115 0.281 -0.437 0.666 0.41 Unknown Unknown

49 

participants

Peterson and Bond 

(2004)

Online compared to face-to-face teacher 

preparation for learning standards-based 

planning skills -0.100 0.214 -0.520 0.320 -0.47 Unknown Unknown 4 sections 

Schmeeckle (2003) Online training: An evaluation of the 

effectiveness and efficiency of training law 

enforcement personnel over the Internet -0.106 0.198 -0.494 0.282 -0.53 Unknown Unknown 101 students

Schoenfeld-Tacher, 

McConnell and 

Graham (2001)

Do no harm: A comparison of the effects of 

online vs. traditional delivery media on a science 

course +0.800 0.459 -0.100 1.700 1.74 100 99.94 Unknown
23

Exhibit 4a: Purely Online versus Face-to-Face (Category 1) Studies Included in the Meta-analysis (continued) 

Authors Title Effect Size

95-Percent 

Confidence 

Interval

Test of Null 

Hypothesis

(2-tail)

Retention 

Rate 

(percentage) Number 

of Units 

Assigneda g SE

Lower 

Limit

Upper 

Limit Z-Value Online

Face-toFace

Sexton, Raven and 

Newman (2002)

A comparison of traditional and World Wide 

Web methodologies, computer anxiety, and 

higher order thinking skills in the inservice 

training of Mississippi 4-H extension agents -0.422 0.385 -1.177 0.332 -1.10 Unknown Unknown 26 students

Sun, Lin and Yu 

(2008)

A study on learning effect among different 

learning styles in a Web-based lab of science for 

elementary school students +0.260 0.188 -0.108 0.628 1.38 Unknown Unknown 4 classrooms

Turner et al. (2006) Web-based learning versus standardized 

patients for teaching clinical diagnosis: A 

randomized, controlled, crossover trial +0.242 0.367 -0.477 0.960 0.66 Unknown Unknown 30 students

Vandeweerd et al. 

(2007)

Teaching veterinary radiography by e-learning 

versus structured tutorial: A randomized, singleblinded controlled trial +0.144 0.207 -0.262 0.550 0.70 Unknown Unknown 92 students

Wallace and 

Clariana (2000)

Achievement predictors for a computerapplications module delivered online +0.109 0.206 -0.295 0.513 0.53 Unknown Unknown 4 sections

Wang (2008) Developing and evaluating an interactive 

multimedia instructional tool: Learning outcomes 

and user experiences of optometry students -0.071 0.136 -0.338 0.195 -0.53 Unknown Unknown 4 sectionsc

Zhang (2005) Interactive multimedia-based e-learning: A study 

of effectiveness +0.381 0.339 -0.283 1.045 1.12 Unknown Unknown 51 students

Zhang et al. (2006) Instructional video in e-learning: Assessing the 

effect of interactive video on learning 

effectiveness +0.498 0.244 0.020 0.975 2.04* Unknown Unknown 69 students

Exhibit reads: The effect size for the Hugenholtz et al. (2008) study of online medical education was +0.11, which was not significantly different from 0. 

*p < .05, ** p < .01, SE = Standard error 

a

 The number given represents the assigned units at study conclusion. It excludes units that attrited. b

 Two outcome measures were used to compute one effect size. The first outcome measure was completed by 17 participants, and the second outcome measure was 

completed by 20 participants. 

c

 This study is a crossover study. The number of units represents those assigned to treatment and control conditions in the first round. 
24

Exhibit 4b. Blended Versus Face-to-Face (Category 2) Studies Included in the Meta-Analysis

Authors Title Effect Size

95-Percent 

Confidence 

Interval

Test of Null 

Hypothesis

(2-tail)

Retention 

Rate 

(percentage)

Number 

of Units 

Assigneda g SE

Lower 

Limit

Upper 

Limit Z-Value Online

FacetoFace

Aberson et al. 

(2003)

Evaluation of an interactive tutorial for teaching 

hypothesis testing concepts +0.580 0.404 -0.212 1.372 1.44 Unknown

.75 

2 sections

Al-Jarf (2004) The effects of Web-based learning on struggling 

EFL college writers +0.740 0.194 0.360 1.120 3.82*** Unknown Unknown 113 students

Caldwell (2006) A comparative study of traditional, Web-based 

and online instructional modalities in a computer 

programming course +0.251 0.311 -0.359 0.861 0.81 100 100 60 students

Davis et al. (1999) Developing online courses: A comparison of 

Web-based instruction with traditional instruction -0.335 0.338 -0.997 0.327 -0.99 Unknown Unknown

2 courses/ 

classrooms

Day, Raven and 

Newman (1998)

The effects of World Wide Web instruction and 

traditional instruction and learning styles on 

achievement and changes in student attitudes in 

a technical writing in agricommunication course +1.113 0.289 0.546 1.679 3.85*** 89.66 96.55 2 sections

DeBord, Aruguete 

and Muhlig (2004)

Are computer-assisted teaching methods 

effective? +0.110 0.188 -0.259 0.479 0.69 Unknown Unknown 112 students

El-Deghaidy and 

Nouby (2008)

Effectiveness of a blended e-learning 

cooperative approach in an Egyptian teacher 

education program +1.049 0.406 0.253 1.845 2.58** Unknown Unknown 26 students

Englert et al. (2007) Scaffolding the writing of students with 

disabilities through procedural facilitation using 

an Internet-based technology

+0.740 0.345 0.064 1.416 2.15* Unknown Unknown

6 classrooms 

from 

5 urban 

schools

Frederickson, Reed 

and Clifford (2005)

Evaluating Web-supported learning versus 

lecture-based teaching: Quantitative and 

qualitative perspectives +0.138 0.345 -0.539 0.814 0.40 Unknown Unknown 2 sections

Gilliver, Randall and 

Pok (1998) Learning in cyberspace: Shaping the future +0.477 0.111 0.260 0.693 4.31*** Unknown Unknown 24 classes

Long and Jennings 

(2005) [Wave 1] c The effect of technology and professional 

development on student achievement +0.025 0.046 -0.066 0.116 0.53 Unknown Unknown 9 schools
25

Exhibit 4b: Blended versus Face-to-Face (Category 2) Studies Included in the Meta-analysis (continued) 

Authors Title

Effect Size 

95-Percent

Confidence 

Interval 

Test of Null 

Hypothesis

(2-tail)

Retention Rate 

(percentage) Number

 of Units 

Assigneda g SE

Lower 

Limit

Upper 

Limit Z-Value Online

Face-toFace

Long and Jennings 

(2005) [Wave 2] c The effect of technology and professional 

development on student achievement +0.554 0.098 0.362 0.747 5.65*** Unknown Unknown 6 teachers

Maki and Maki 

(2002)

Multimedia comprehension skill predicts 

differential outcomes of Web-based and lecture 

courses +0.171 0.160 -0.144 0.485 1.06 91.01 88.10 155 students

Midmer, Kahan and 

Marlow (2006)

Effects of a distance learning program on 

physicians’ opioid- and benzodiazepineprescribing skills +0.332 0.213 -0.085 0.750 1.56m Unknown Unknown 88 students

O’Dwyer, Carey 

and Kleiman (2007)

A study of the effectiveness of the Louisiana 

algebra I online course +0.373 0.094 0.190 0.557 3.99*** 88.51 64.4 Unknownb

Rockman et al. 

(2007) [Writing] c ED PACE final report 

-0.239 0.102 -0.438 -0.039 -2.34* Unknown Unknown

28 

classrooms

Rockman et al. 

(2007) [Multiplechoice test] c

ED PACE final report

-0.146 0.102 -0.345 0.054 -1.43 Unknown Unknown

28 

classrooms

Schilling et al. 

(2006) [Search 

strategies] c

An interactive Web-based curriculum on 

evidence-based medicine: Design and 

effectiveness +0.585 0.188 0.216 0.953 3.11** 68.66 59.62 Unknown

Schilling et al. 

(2006) [Quality of 

care calculation] c

An interactive Web-based curriculum on 

evidence-based medicine: Design and 

effectiveness +0.926 0.183 0.567 1.285 5.05*** 66.42 86.54 Unknown

Spires et al. (2001) Exploring the academic self within an electronic 

mail environment +0.571 0.357 -0.130 1.271 1.60 Unknown 100.00 31 students

Suter and Perry 

(1997)

Evaluation by electronic mail

+0.140 0.167 -0.188 0.468 0.84 Unknown Unknown Unknown
26

Exhibit 4b: Blended versus Face-to-Face (Category 2) Studies Included in the Meta-analysis (continued) 

Authors Title

Effect Size 

95-Percent

Confidence 

Interval 

Test of Null 

Hypothesis

(2-tail)

Retention Rate 

(percentage) Number

 of Units 

Assigneda g SE

Lower 

Limit

Upper

Limit Z-Value Online

Face-toFace

Urban (2006) A comparison of computer-based distance 

education and traditional tutorial sessions in 

supplemental instruction for students at-risk for 

academic difficulties +0.264 0.192 -0.112 0.639 1.37 96.86 73.85 110 students

Zacharia (2007) Comparing and combining real and virtual 

experimentation: An effort to enhance students’ 

conceptual understanding of electric circuits +0.570 0.216 0.147 0.993 2.64** 100 95.56 88 students

Exhibit reads: The effect size for the Aberson et al. (2003) study of an interactive tutorial on hypothesis testing was +0.58, which was not significantly different from 0.

*p < .05, ** p < .01, *** p < .001, SE = Standard error. a This number represents the assigned units at study conclusion. It excludes units that attrited. b The study involved 18 online classrooms from six districts and two private schools; the same six districts were asked to identify comparable face-to-face classrooms, 

but the study does not report how many of those classrooms participated. c Two independent contrasts were contained in this article, which therefore appears twice in the table. 
27

Test for Homogeneity 

Both the Category 1 and Category 2 studies contrasted a condition with online elements with a 

condition of face-to-face instruction only. Analysts used the larger corpus of 50 effects that were 

either Category 1 or Category 2 to explore the influence of possible moderator variables. 

The individual effect size estimates included in this meta-analysis ranged from a low of –0.80 

(tendency for higher performance in the face-to-face condition) to a high of +1.11 (favoring 

online instruction). A test for homogeneity of effect size found significant differences across 

studies (Q = 168.86, p < .0001). Because of these significant differences in effect sizes, analysts 

investigated the variables that may have influenced the differing effect sizes. 

Analyses of Moderator Variables 

As noted in chapter 1, this meta-analysis has distinguished between practice variables, which can 

be considered part of intervention implementation, and conditions, which are status variables that 

are fairly impervious to outside influence. Relying on prior research, the research team identified 

variables of both types that might be expected to correlate with the effectiveness of online 

learning. The researchers also considered the potential influence of study method variables, 

which often vary with effect size; typically, more poorly controlled studies show larger effects. 

Each study in the meta-analysis was coded for these three types of variables—practice, status, 

and study method—using the coding categories shown in the appendix. 

Many of the studies did not provide information about features considered to be potential 

moderator variables, a predicament noted in previous meta-analyses (see Bernard et al. 2004). 

Many of the reviewed studies, for example, did not indicate (a) whether or not the online 

instructor had received training in the method of instruction, (b) rates of attrition from the 

contrasting conditions and (c) contamination between conditions. 

For some of the variables, the number of studies providing sufficient information to support 

categorization as to whether or not the feature was present was too small to support a meaningful 

analysis. Analysts identified those variables for which at least two contrasting subsets of studies, 

with each subset containing six or more study effects, could be constructed. In some cases, this 

criterion could be met by combining related feature codes; in a few cases, the inference was 

made that failure to mention a particular practice or technology (e.g., one-way video) denoted its 

absence. Practice, conditions and method variables for which study subsets met the size criterion 

were included in the search for moderator variables. 
28

Practice Variables 

Exhibit 5 shows the variation in effectiveness associated with 12 practice variables. Analysis of 

these variables addresses the third research question: 

What practices are associated with more effective online learning? 

Exhibit 5 and the two data exhibits that follow show significance results both for the various 

subsets of studies considered individually and for the test of the dimension used to subdivide the 

study sample (i.e., the potential moderator variable). For example, in the case of ComputerMediated Communication With Peers, both the 17 contrasts in which students in the online 

condition had only asynchronous communication with peers and the 6 contrasts in which online 

students had both synchronous and asynchronous communication with peers are shown in the 

table. The two subsets had mean effect sizes of +0.27 and +0.17, respectively, and only the 

former was statistically different from 0. The Q-statistic of homogeneity tests whether the 

variability in effect sizes for these contrasts is associated with the type of peer communication 

available. The Q-statistic for Computer-Mediated Communication With Peers (0.32) is not 

statistically different from 0, indicating that the addition of synchronous communication with 

peers is not a significant moderator of online learning effectiveness. 

The test of the moderator variable most central to this study—whether a blended online condition 

including face-to-face elements is associated with greater advantages over classroom instruction 

than is pure online learning—was discussed above. As noted there, the effect size for blended 

approaches contrasted against face-to-face instruction is larger than that for purely online 

approaches contrasted against face-to-face instruction. The other two practice variables included 

in the chapter 1 conceptual framework—learning experience type and synchronous versus 

asynchronous communication with the instructor—were tested in a similar fashion. The former 

was found to moderate significantly the size of the online learning effect (Q = 6.19, p < .05).16

The mean effect size for collaborative instruction (+0.25) as well as for instructor-directed 

instruction (+0.39) were significantly positive whereas the mean effect size for independent, 

active online learning (+0.05) was not.17 

Among the other 10 practices, which were not part of the conceptual model, none attained 

statistical significance.18 The amount of time that students in the treatment condition spent on 

task compared with students in the face-to-face condition did approach statistical significance as 

a moderator of effectiveness (Q = 3.62, p = .06).19 The mean effect size for studies with more 

time spent on task by online learners than learners in the control condition was +0.45 compared 

 

16 This contrast is not statistically significant (p = .13) when the five K-12 studies are removed from the analysis. 17 Online experiences in which students explored digital artifacts and controlled the specific material they wanted to 

view were categorized as “independent” learning experiences. 18 When the five K-12 studies are removed from the analysis, two additional practices are found to be statistically 

significant moderators of the effects of online learning – time spent on task and opportunities for face-to-face 

interactions with peers. 19 Time on task as a moderator becomes statistically significant (Q = 4.44, p < .05) when the five K-12 studies are 

removed from the analysis. 
29

Exhibit 5. Tests of Practices as Moderator Variables 

Variable Contrast

Number 

Studies

Weighted 

Effect Size

Standard 

Error

Lower 

Limit

Upper 

Limit

QStatistic

Pedagogy/ 

learning 

experiencea

Instructor-directed 

(expository) 8 0.386** 0.120 0.150 0.622

6.19* Independent 

(active) 17 0.050 0.082 -0.110 0.210

Collaborative 

(interactive) 22 0.249*** 0.075 0.102 0.397

Computermediated 

communication 

with instructor a

Asynchronous 

only 16 0.239* 0.108 0.027 0.451

1.20 Synchronous + 

Asynchronous 8 0.036 0.151 -0.259 0.331

Computermediated 

communication 

with peersa

Asynchronous 

only 17 0.272** 0.091 0.093 0.450

0.32 Synchronous + 

Asynchronous 6 0.168 0.158 -0.141 0.478

Treatment 

durationa

Less than 1 month 19 0.140 0.089 -0.034 0.314

More than 1 0.69

month 29 0.234*** 0.069 0.098 0.370

Media 

featuresa

Text-based only 14 0.208 0.111 -0.009 0.425

0.00 Text + other 

media 32 0.200** 0.066 0.071 0.329

Time on taska

Online > Face to 

Face 9 0.451*** 0.113 0.229 0.673

3.62 Same or Face to 

Face > Online 18 0.183* 0.083 0.020 0.346

One-way video 

or audio

Present 14 0.092 0.091 -0.087 0.271

Absent/Not 2.15

reported 36 0.254*** 0.062 0.133 0.375

Computerbased 

instruction 

elements 

Present 29 0.182** 0.065 0.054 0.311

 0.25 Absent/Not 

reported 21 0.234** 0.081 0.075 0.393

Opportunity for 

face-to-face 

time with 

instructor

During instruction 21 0.298*** 0.074 0.154 0.442

 3.70

Before or after 

instruction 11 0.050 0.118 -0.181 0.281

Absent/Not 

reported 18 0.150 0.091 -0.028 0.327

Opportunity for 

face-to-face 

time with peers

During instruction 21 0.300*** 0.072 0.159 0.442

 5.20

Before or after 

instruction 12 0.001 0.111 -0.216 0.218

Absent/Not 

reported 17 0.184* 0.093 0.001 0.367

Opportunity to 

practice

Present 41 0.212*** 0.056 0.102 0.322

Absent/Not 0.15

reported 9 0.159 0.124 -0.084 0.402

Feedback 

provided

Present 23 0.204** 0.078 0.051 0.356

Absent/Not 0.00

reported 27 0.203** 0.070 0.066 0.339

Exhibit reads: Studies in which time spent in online learning exceeded time in the face-to-face condition had a mean 

effect size of +0.45 compared with +0.18 for studies in which face-to-face learners had as much or more instructional 

time. 

*p < .05, **p < .01, ***p < .001. a The moderator analysis for this variable excluded studies that did not report information for this feature. 
30

with +0.18 for studies in which the learners in the face-to-face condition spent as much or more 

time on task . 

Condition Variables 

The strategy to investigate whether study effect sizes varied with publication year, which was 

taken as a proxy for the sophistication of available technology, involved splitting the study 

sample into two nearly equal subsets by contrasting studies published between 1997 and 2003 

against those published in 2004 through July 2008.

The studies were divided into three subsets of learner type: K–12 students, undergraduate 

students (the largest single group), and other types of learners (graduate students or individuals 

receiving job-related training). As noted above, the studies covered a wide range of subjects, but 

medicine and health care were the most common. Accordingly, these studies were contrasted 

against studies in other fields. Tests of these conditions as potential moderator variables 

addressed the study’s fourth research question: 

What conditions influence the effectiveness of online learning? 

None of the three conditions tested emerged as a statistically significant moderator variable. In 

other words, for the range of student types for which studies are available, the effectiveness of 

online learning was equivalent in older and newer studies, with undergraduate and older learners, 

and in both medical and other subject areas. Exhibit 6 provides the results of the analysis of 

conditions. 

Exhibit 6. Tests of Conditions as Moderator Variables 

Variable Contrast

Number of 

Contrasts 

Weighted 

Effect 

Size

Standard 

Error

Lower 

Limit

Upper 

Limit Q-Statistic

Year 

Published

1997–2003 13 0.195 0.105 -0.010 0.400

0.00 

2004 or after 37 0.203*** 0.058 0.088 0.317

Learner 

Type

K–12 students 7 0.1664 0.118 -0.065 0.397

Undergraduate 21 0.309*** 0.083 0.147 0.471 3.25

Graduate 

student/Other 21 0.100 0.084 -0.064 0.264

Subject 

Matter

Medical/ Health 

care

16 0.205* 0.090 0.028 0.382

0.00

Other 34 0.199** 0.062 0.0770 0.320

Exhibit reads: The positive effect associated with online learning over face-to-face instruction was 

significant both for studies published between 1997 and 2003 and for those published in 2004 or later; the 

effect size does not vary significantly with period of publication. 

*p < .05, **p < .01, ***p < .001. 
31

Because of the Evaluation of Evidence-Based Practices in Online Learning study’s emphasis on 

K-12 education, the online learning studies involving K-12 students were of particular interest. 

The meta-analysis includes seven contrasts from five studies of K-12 school students’ online 

learning. Exhibit 7 describes these studies. 

Given the small number of studies that addressed K-12 learners in the meta-analysis, attempts to 

test for statistical differences between the mean effect for K-12 learners and those for other types 

of learners should be viewed as merely suggestive. At +0.17, the average effect size for the seven 

contrasts involving K-12 learners appears similar to that for graduate and other students (+0.10) 

but less positive than that for undergraduates (+0.31). When learner type was tested as a 

moderator variable, however, the resulting Q-statistic was not significant. 

Methods Variables 

The advantage of meta-analysis is its ability to uncover general effects by looking across a range 

of studies that have operationalized the construct under study in different ways, studied it in 

different contexts, and used different methods and outcome measures. However, the inclusion of 

poorly designed and small-sample studies in the meta-analysis corpus poses concerns because 

doing so may give undue weight to spurious effects. Study methods variables were examined as 

potential moderators to explore this issue. The results are shown in Exhibit 8. 

The influence of study sample size was examined by dividing studies into three subsets, 

according to the number of learners for which outcome data were collected. Sample size was not 

found to be a statistically significant moderator of online learning effects. Thus, there is no 

evidence that the inclusion of small-sample studies in the meta-analysis was responsible for the 

overall finding of a positive outcome for online learning. 

Comparisons of the three designs deemed acceptable for this meta-analysis (random-assignment 

experiments, quasi-experiments with statistical control and crossover designs) indicate that study 

design is not significant as a moderator variable (see Exhibit 8). Moreover, in contrast with early 

meta-analyses in computer-based instruction, where effect size was inversely related to study 

design quality (Pearson et al. 2005), those experiments that used random assignment in the 

present corpus produced significant positive effects (+0.25, p < .001) while the quasiexperiments and crossover designs did not. 
32

Exhibit 7. Studies of Online Learning Involving K–12 Students 

The meta-analysis study corpus for this meta-analysis included five articles reporting on studies 

involving K–12 students. All of these studies compared student learning in a blended condition 

with student learning in a face-to-face condition. One of the studies (Long and Jennings 2005, 

Wave 1 study) was a randomized control trial and the others were quasi-experiments. One of the 

quasi-experiments (Rockman et al. 2007) provided two effect sizes that favored the face-to-face 

condition; the other studies provided five effects favoring the blended condition (with a range 

from +0.03 to +0.74). 

Rockman et al. (2007) used a quasi-experimental matched comparison design to evaluate the 

effectiveness of Spanish courses offered to middle schools (seventh and eighth grades) through 

the West Virginia Virtual School. This virtual school program used a blended model of 

instruction that combined face-to-face and virtual instruction as well as paper and pencil and 

Web-based activities. The program was delivered by a three-member teacher team that included 

a lead teacher (a certified Spanish teacher) who was responsible for the design and delivery of 

the daily lesson plan and weekly phone conversations with each class; an adjunct teacher (a 

certified Spanish teacher) who provided content-related feedback by means of e-mail and voicemail and who graded student tests and products; and a classroom facilitator (a certified teacher, 

but not a Spanish teacher) who guided students on site to ensure that they stayed on task and 

completed assignments on time. The hybrid Spanish course was offered to students in 21 schools 

that did not have the resources to provide face-to-face Spanish instruction. The students in the 

face-to-face group came from seven schools that matched the virtual schools with respect to 

average language arts achievement and school size. The study involved a total of 463 students. 

Information needed to compute effect sizes was reported for two of the student learning 

measures used in the study. For the first of these, a multiple-choice test including subtests on oral 

and written comprehension of Spanish, the mean estimated effect was –0.15, and the difference 

between the two conditions was not statistically significant. The other measure was a test of 

students’ writing ability, and the effect size for this skill was –0.24, with students receiving faceto-face instruction doing significantly better than those receiving the online blended version of 

the course. 

Contrasting results were obtained in the other large-scale K–12 study, conducted by O’Dwyer, 

Carey and Kleiman (2007). These investigators used a quasi-experimental design to compare the 

learning of students participating in the Louisiana Algebra I Online initiative with the learning of 

students in comparison classrooms that were “similar with regard to mathematics ability, 

environment, and size, but where teachers used traditional ‘business as usual’ approaches to 

teaching algebra” (p. 293). Like the West Virginia Virtual School program, this initiative used a 

blended model of instruction that combined face-to-face and Web-based activities with two 

teachers: one in class and the other online. Matched pre- and posttest scores on researcherdeveloped multiple-choice tests were collected from a total of 463 students (231 from the 

treatment group, 232 from the comparison group) from multiple schools and school districts. An 

effect size of +0.37 was obtained, with online students performing better than their peers in 

conventional classrooms. 
33

Exhibit 7. Studies of Online Learning Involving K-12 Students (continued) 

Long and Jennings (2005) examined whether the performance of eighth-grade students whose 

teachers integrated the use of the Pathways to Freedom Electronic Field Trips—an online 

collection of interactive activities designed by Maryland Public Television—improved compared 

with performance of students whose teachers taught the same content without the online 

materials. The study provided two sets of analyses from two waves of data collection, yielding 

two independent effect sizes. The first set of analyses involved the data from nine schools in two 

Maryland districts. Schools were assigned randomly to conditions. Teachers in both conditions 

covered the same learning objectives related to slavery and the Underground Railroad, with the 

treatment teachers using the Pathways to Freedom Electronic Field Trips materials. A small 

effect size of +0.03 favoring the online condition was computed from change scores on 

researcher-developed multiple-choice tests administered to 971 students. 

Long and Jennings’ (2005, wave 2) second study involved a subset of teachers from one of the 

two participating districts, which was on a semester schedule. The teachers from this district 

covered the same curriculum twice during the year for two different sets of students. The gain 

scores of 846 students of six teachers (three treatment teachers and three control teachers) from 

both semesters were collected. Regression analysis indicated an effect size of +0.55 favoring the 

online conditions. This study also looked into the maturation effects of teachers’ using the online 

materials for the second time. As hypothesized, the results showed that the online materials were 

used more effectively in the second semester. 

Sun, Lin and Yu (2008) conducted a quasi-experimental study to examine the effectiveness of a 

virtual Web-based science lab with 113 fifth-grade students in Taiwan. Although both treatment 

and control groups received an equal number of class hours and although both groups conducted 

manual experiments, students in the treatment condition used the virtual Web-based science lab 

for part of their lab time. The Web-based lab enabled students to conduct virtual experiments 

while teachers observed student work and corrected errors online. The control group students 

conducted equivalent experiments using conventional lab equipment. Matched pre- and posttest 

scores on researcher-developed assessments were collected for a total of 113 students (56 from 

the treatment group and 57 from the comparison group) in four classrooms from two randomly 

sampled schools. An effect size of +0.26 favoring the virtual lab condition was obtained from 

analysis of covariance results, controlling for pretest scores. 

A small-scale quasi-experiment was conducted by Englert et al. (2007). This study examined the 

effectiveness of a Web-based writing support program with 35 elementary-age students from six 

special education classrooms across five urban schools. Students in the treatment group used a 

Web-based program that supported writing performance by prompting attention to the topical 

organization and structure of ideas during the planning and composing phases of writing. Control 

students used similar writing tools provided in traditional paper-and-pencil formats. Pre- and 

posttests of student writing, scored on a researcher-developed rubric, were used as outcome 

measures. An effect size of +0.74 favoring the online condition was obtained from an analysis of 

covariance controlling for writing pretest scores. 
34

 Exhibit 8. Tests of Study Features as Moderator Variables 

Variable Contrast

Number 

of 

Studies

Weighted 

Effect 

Size

Standard 

Error

Lower 

Limit

Upper 

Limit Q-Statistic

Sample size

Fewer than 35 11 0.203 0.139 -0.069 0.476

From 35 to 100 20 0.209* 0.086 0.039 0.378 0.01

More than 100 19 0.199** 0.072 0.058 0.339

Type of 

knowledge 

testeda

Declarative 12 0.180 0.097 -0.010 0.370

0.37

Procedural/ 

Procedural and 

declarative

30 0.239*** 0.068 0.106 0.373

Strategic 

knowledge 5 0.281 0.168 -0.047 0.610

Study design

Random 

assignment 

control

32 0.249*** 0.065 0.122 0.376

1.50

Quasiexperimental 

design with 

statistical control

13 0.108 0.095 -0.079 0.295

Crossover 

design 5 0.189 0.158 -0.120 0.499

Unit of 

assignment 

to conditionsa

Individual 32 0.169* 0.066 0.040 0. 298

Class section 7 0.475*** 0.139 0.202 0.748 4.73.

Course/School 9 0.120 0.103 -0.083 0.323 

Instructor 

equivalencea

Same instructor 20 0.176* 0.078 0.024 0.329

0.73

Different 

instructor 19 0.083 0.077 -0.067 0.233

Equivalence 

of curriculum/ 

instructiona

Identical/

Almost identical 29 0.130* 0.063 0.007 0.252

Different/ 6.85**

Somewhat 

different 

17 0.402*** 0.083 0.239 0.565

Exhibit reads: The average effect size was significantly positive for studies with a sample size of less 

than 35 as well as for those with 35 to 100 and those with a sample size larger than 100; the weighted 

average effect did not vary with size of the study sample. 

*p < .05, **p < .01, ***p < .001. a The moderator analysis excluded some studies because they did not report information about this 

feature. 
35

Effect sizes do not vary depending on whether or not the same instructor or instructors taught in 

the face-to-face and online conditions (Q = 0.73, p > .05). The average effect size for the 20 

contrasts in which instructors were the same across conditions was +0.18, p < .05. The average 

effect size for contrasts in which instructors varied across conditions was +0.08, p > .05. The 

only study method variable that proved to be a significant moderator of effect size was 

comparability of the instructional materials and approach for treatment and control students. 

The analysts coding study features examined the descriptions of the instructional materials and 

the instructional approach for each study and coded them as “identical,” “almost identical,” 

“different” or “somewhat different” across conditions. Adjacent coding categories were 

combined (creating the two study subsets Identical/Almost Identical and Different/Somewhat 

Different) to test Equivalence of Curriculum/Instruction as a moderator variable. Equivalence of 

Curriculum/Instruction was a significant moderator variable (Q = 6.85, p < .01). An examination 

of the study subgroups shows that the average effect for studies in which online learning and 

face-to-face instruction were described as identical or nearly so was +0.13, p < .05, compared 

with an average effect of +0.40 (p < .001) for studies in which curriculum materials and 

instructional approach varied across conditions. 

The moderator variable analysis for aspects of study method also found additional patterns that 

did not attain statistical significance but that should be re-tested once the set of available rigorous 

studies of online learning has expanded. The type of learning outcome tested, for example, may 

influence the magnitude of effect sizes. Twelve studies measured declarative knowledge 

outcomes only, typically through multiple-choice tests. A larger group of studies (30) looked at 

students’ ability to perform a procedure, or they combined procedural and declarative knowledge 

outcomes in their learning measure. Five studies used an outcome measure that focused on 

strategic knowledge. (Three studies did not describe their outcome measures in enough detail to 

support categorization.) Among the subsets of studies, the average effect for studies that included 

procedural knowledge in their learning outcome measure (effect size of +0.24) and that for 

studies that measured strategic knowledge (effect size of +0.28) appeared larger than the mean 

effect size for studies that used a measure of declarative knowledge only (+0.18). Even so, the 

Type of Knowledge Tested was not a significant moderator variable (Q = 0.37, p > .05). 
 
 37

4. Narrative Synthesis of Studies 

Comparing Variants of Online Learning 

This chapter presents a narrative summary of Category 3 studies—those that examined the 

learning effects of variations in online practices such as different versions of blended instruction 

or online learning with and without immediate feedback to the learner. The literature search and 

screening (described in chapter 2) identified 84 Category 3 studies reported in 79 articles.20

Within the set of Category 3 studies, five used K–12 students as subjects and 10 involved K–12 

teacher education or professional development. College undergraduates constituted the most 

common learner type (see Exhibit 9). All Category 3 studies involved formal education. Course 

content for Category 3 studies covered a broad range of subjects, including observation skills, 

understanding Internet search engines, HIV/AIDS knowledge and statistics. 

When possible, the treatment manipulations in Category 3 studies were coded using the practice 

variable categories that were used in the meta-analysis to facilitate comparisons of findings 

between the meta-analysis and the narrative synthesis. No attempt was made to statistically 

combine Category 3 study results, however, because of the wide range of conditions compared in 

the different studies. 

Exhibit 9. Learner Types for Category 3 Studies 

Educational Level Number of Studies

K–12 5

Undergraduate 37

Graduate 4

Medicala 18

Teacher professional developmentb 10

Adult training 4

Otherc 4

Not available 2

Total 84

Exhibit reads: K–12 students were the learners in 5 of the 84 studies of 

alternative online practices. 

a The medical category spans undergraduate and graduate educational levels 

and includes nursing and related training. 

b Teacher professional development includes preservice and inservice training. 

c The Other category includes populations consisting of a combination of 

learner types such as student and adult learners or undergraduate and 

graduate learners.

 

20 Some articles contained not only contrasts that fit the criteria for Category 1 or 2 but also contrasts that fit 

Category 3. The appropriate contrasts between online and face-to-face conditions were used in the meta-analysis; 

the other contrasts were reviewed as part of the Category 3 narrative synthesis presented here. 
 38

Blended Compared With Pure Online Learning 

The meta-analysis of Category 1 and 2 studies described in chapter 3 found that effect sizes were 

larger for studies that compared blended learning conditions with face-to-face instruction than 

for studies that compared purely online learning with face-to-face instruction. Another way to 

investigate the same issue is by conducting studies that incorporate both blended and purely 

online conditions to permit direct comparisons of their effectiveness. 

The majority of the 10 Category 3 studies that directly compared purely online and blended 

learning conditions found no significant differences in student learning. Seven studies found no 

significant difference between the two, two found statistically significant advantages for purely 

online instruction, and one found an advantage for blended instruction. The descriptions of some 

of these studies, provided below, make it clear that although conditions were labeled as 

“blended” or “purely online” on the basis of their inclusion or exclusion of face-to-face 

interactions, conditions differed in terms of content and quality of instruction. Across studies, 

these differences in the nature of purely online and blended conditions very likely contributed to 

the variation in outcomes. 

Keefe (2003), for example, contrasted a section of an organizational behavior course that 

received lectures face-to-face with another section that watched narrated PowerPoint slides 

shown online or by means of a CD-ROM. Both groups had access to e-mail, online chat rooms, 

and threaded discussion forums. All course materials were delivered electronically to all students 

at the same time. On the course examination, students in the purely online section scored almost 

8 percent lower than those receiving face-to-face lectures in addition to the online learning 

activities. Keefe’s was the only study in the review that found a significant decrement in 

performance for the condition without face-to-face instructional elements. 

Poirier and Feldman (2004) compared a course that was predominantly face-to-face but also used 

an online discussion board with a course taught entirely online. Students in the predominantly 

face-to-face version of the course were required to participate in three online discussions during 

the course and to post at least two comments per discussion to an online site; the site included 

content, communication and assessment tools. In the purely online version of the course, students 

and the instructor participated in two online discussions each week. Poirier and Feldman found a 

significant main effect favoring the purely online course format for examination grades but no 

effect on student performance on writing assignments. 

Campbell et al. (2008) compared a blended course (in which students accessed instruction online 

but attended face-to-face discussions) with a purely online course (in which students accessed 

instruction and participated in discussions online). Tutors were present in both discussion 

formats. Students were able to select the type of instruction they wanted, blended or online. 

Mean scores for online discussion students were significantly higher than those for the face-toface discussion group. 

As a group, these three studies suggest that the relative efficacy of blended and purely online 

learning approaches depends on the instructional elements of the two conditions. For the most 

part, these studies did not control instructional content within the two delivery conditions (blend 
 39

of online and face-to-face versus online only). For example, the lecturer in the Keefe (2003) 

study may have covered material not available to the students reviewing the lecture’s PowerPoint 

slides online. Alternately, in the Poirier and Feldman (2004) study, students interacting with the 

instructor in two online discussions a week may have received more content than did those 

receiving face-to-face lectures. 

Davis et al. (1999) attempted to equate the content delivered in their three class sections (online, 

traditional face-to-face, and a blended condition in which students and instructor met face-toface but used the online modules). Students in an educational technology course were randomly 

assigned to one of the three sections. No significant differences among the three conditions were 

found in posttest scores on a multiple-choice test. 

An additional six studies contrasting purely online conditions and blended conditions (without 

necessarily equating learning content across conditions) also failed to find significant differences 

in student learning. Ruchti and Odell (2002) compared test scores from two groups of students 

taking a course on elementary science teaching methods. One group took online modules; the 

other group received instruction in a regular class, supplemented with an online discussion board 

and journal (also used in the online course condition). No significant difference between the 

groups was found. 

Beile and Boote (2002) compared three groups: one with face-to-face instruction alone, another 

with face-to-face instruction and a Web-based tutorial, and a third with Web-based instruction 

and the same Web-based tutorial. The final quiz on library skills indicated no significant 

differences among conditions. 

Gaddis et al. (2000) compared composition students’ audience awareness between a blended 

course and a course taught entirely online. The same instructor taught both groups, which also 

had the same writing assignments. Both groups used networked computers in instruction, in 

writing and for communication. However, the “on campus” group met face-to-face, giving 

students the opportunity to communicate in person, whereas the “off campus” group met only 

online. The study found no significant difference in learner outcomes between the two groups. 

Similarly, Caldwell (2006) found no significant differences in performance on a multiple-choice 

test between undergraduate computer science majors enrolled in a blended course and those 

enrolled in an online course. Both groups used a Web-based platform for instruction, which was 

supplemented by a face-to-face lab component for the blended group. 

Scoville and Buskirk (2007) examined whether the use of traditional or virtual microscopy 

would affect learning outcomes in a medical histology course. Students were assigned to one of 

four sections: (a) a control section where learning and testing took place face-to-face, (b) a 

blended condition where learning took place virtually and the practical examination took place 

face-to-face, (c) a second blended condition where learning took place face-to-face and testing 

took place virtually, and (d) a fully online condition. Scoville and Buskirk found no significant 

differences in unit test scores by learning groups. 

Finally, McNamara et al. (2008) studied the effectiveness of different approaches to teaching a 

weight-training course. They divided students into three groups: a control group that received 
 40

face-to-face instruction, a blended group that received a blend of online and face-to-face 

instruction, and a fully online group. The authors did not find a significant main effect for group 

type.21

Thus, as a group, these studies do not provide a basis for choosing online versus blended 

instructional conditions

Media Elements 

Eight studies in the Category 3 corpus compared online environments using different media 

elements such as one-way video (Maag 2004; McKethan et al. 2003; Schmeeckle 2003; 

Schnitman 2007; Schroeder 2006; Schutt 2007; Tantrarungroj 2008; Zhang et al. 2006). Seven of 

the eight studies found no significant differences among media combinations. In the study that 

found a positive effect from enhanced media features, Tantrarungroj (2008) compared two 

instructional approaches for teaching a neuroscience lesson to undergraduate students enrolled in 

computer science classes. The author contrasted an experimental condition in which students 

were exposed to online text with static graphics and embedded video with a control condition in 

which students did not have access to the streaming video. Tantrarungroj found no significant 

difference in grades for students in the two conditions on a posttest administered immediately 

after the course; however, the treatment group scored significantly higher on a knowledge 

retention test that was administered 4 weeks after the intervention. 

The other seven studies found no effect on learning from adding additional media to online 

instruction. For example, Schnitman (2007) sought to determine whether enhancing text with 

graphics, navigation options, and color would affect learning outcomes. The author randomly 

assigned students to one of two conditions in a Web-based learning interface; the control group 

accessed a plain, text-based interface, and the treatment group accessed an enhanced interface 

that featured additional graphics, navigational options, and an enhanced color scheme. 

Schnitman found no significant differences in learning outcomes between the treatment and 

control groups. 

The fact that the majority of studies found no significant difference across media types is 

consistent with the theoretical position that the medium is simply a carrier of content and is 

unlikely to affect learning per se (Clark 1983, 1994). A study by Zhang et al. (2006) suggests 

that the way in which a medium is used is more important than merely having access to it. Zhang 

et al. found that the effect of video on learning hinged on the learner’s ability to control the video 

(“interactive video”). The authors used four conditions: traditional face-to-face and three online 

environments—interactive video, noninteractive video, and nonvideo. Students were randomly 

assigned to one of the four groups. Students in the interactive video group performed 

significantly better than the other three groups. There was no statistical difference between the 

online group that had noninteractive video and the online group that had no video. 

 

21 However, in tests of cognitive knowledge and strength, both the control and blended sections showed significant 

improvements, whereas the fully online section showed no significant pre- to posttest growth for either outcome. 
 41

In summary, many researchers have hypothesized that the addition of images, graphics, audio, 

video or some combination would enhance student learning and positively affect achievement. 

However, the majority of studies to date have found that these media features do not affect 

learning outcomes significantly. 

Learning Experience Type 

Other Category 3 studies manipulated different features of the online learning environment to 

investigate the effects of learner control or type of learning experience. The learning experience 

studies provide some evidence that suggests an advantage for giving learners an element of 

control over the online resources with which they engage; however, the studies’ findings are 

mixed with respect to the relative effectiveness of the three learning experience types in the 

conceptual framework presented in chapter 2.

Four studies (Cavus et al. 2007; Dinov, Sanchez and Christou 2008; Gao and Lehman 2003; 

Zhang 2005) provide preliminary evidence supporting the hypothesis that conditions in which 

learners have more control of their learning (either active or interactive learning experiences in 

our conceptual framework) produce larger learning gains than do instructor-directed conditions 

(expository learning experiences). Three other studies failed to find such an effect (Cook et al. 

2007; Evans 2007; Smith 2006). 

Zhang (2005) reports on two studies comparing expository learning with active learning, both of 

which found statistically positive results in favor of active learning. Zhang manipulated the 

functionality of a Web course to create two conditions. For the control group, video and other 

instruction received over the Web had to be viewed in a specified order, videos had to be viewed 

in their entirety (e.g., a student could not fast forward) and rewinding was not allowed. The 

treatment group could randomly access materials, watching videos in any sequence, rewinding 

them and fast forwarding through their content. Zhang found a statistically significant positive 

effect in favor of learner control over Web functionality (see also the Zhang et al. 2006 study 

described above). Gao and Lehman (2003) found that students who were required to complete a 

“generative activity” in addition to viewing a static Web page performed better on a test about 

copyright law than did students who viewed only the static Web page. Cavus, Uzonboylu and 

Ibrahim (2007) compared the success rates of students learning the Java programming language 

who used a standard collaborative tool with the success rate of those who used an advanced 

collaborative tool that allowed compiling, saving and running programs inside the tool. The 

course grades for students using the advanced collaborative tool were higher than those of 

students using the more standard tool. Similarly, Dinov, Sanchez and Christou (2008) integrated 

tools from the Statistics Online Computational Resource in three courses in probability and 

statistics. For each course, two groups were compared: one group of students received a “lowintensity” experience that provided them with access to a few online statistical tools; the other 

students received a “high-intensity” condition with access to many online tools for acting on 

data. Across the three classes, pooling all sections, students in the more active, high-intensity 

online tool condition demonstrated better understanding of the material on mid-term and final 

examinations than did the other students. 

These studies that found positive effects for learner control and nondidactic forms of instruction 

are counterbalanced by studies that found mixed or null effects from efforts to provide a more 
 42

active online learning experience. Using randomly assigned groups of nurses who learned about 

pain management online, Smith (2006) altered the instructional design to compare a text-based, 

expository linear design with an instructional design involving participant problem solving and 

inquiry. No significant difference was found between the two groups in terms of learning 

outcomes. Cook et al. (2007) found no differences in student learning between a condition with 

end-of-module review questions that required active responses and a condition with expository 

end-of-module activities. Evans (2007) explored the effects of more and less expository online 

instruction for students learning chemistry lab procedures. After asking students to complete an 

online unit that was either text-based or dynamic and interactive, Evans found that SAT score 

and gender were stronger predictors of student performance on a posttest with conceptual and 

procedural items than was the type of online unit to which students were exposed. 

Golanics and Nussbaum (2008) examined the effect of “elaborated questions” and “maximizing 

reasons” prompts on students’ ability to construct and critique arguments. Students were 

randomly divided into groups of three; each group engaged in asynchronous discussions. Half of 

the groups received “elaborated questions,” which explicitly instructed them to think of 

arguments and counterarguments, whereas the other half of the groups viewed unelaborated 

questions. In addition, half of the groups randomly received prompts to provide justifications and 

evidence for their arguments (called the “maximizing reasons” condition); half of the groups did 

not receive those prompts. Elaborated questions stimulated better-developed arguments, but 

maximizing reasons instructions did not. 

Chen (2007) randomly assigned students in a health-care ethics class to one of three Web-based 

conditions: (a) a control group that received online instruction without access to an advanced 

organizer; (b) a treatment group that studied a text-based advanced organizer before online 

instruction; and (c) a second treatment group that reviewed an advanced, Flash-based concept 

map organizer before engaging in online learning.22 The authors hypothesized that both the 

advanced organizer and the concept map would help students access relevant prior knowledge 

and increase their active engagement with the new content. Contrary to expectations, Chen found 

no significant differences in learning achievement across the three groups. 

Suh (2006) examined the effect of guiding questions on students’ ability to produce a good 

educational Web site as required in an online educational technology course. Students in the 

guiding-question condition received questions through an electronic discussion board and were 

required to read the questions before posting their responses. E-mails and online postings 

reminded them to think about the guiding questions as they worked through the problem 

scenario. Guiding questions were found to enhance the performance of students working alone, 

but they did not produce benefits for students working in groups. One possible explanation 

offered by the author is that students working in groups may scaffold each other’s work, hence 

reducing the benefit derived from externally provided questions. 

 

22 Flash animations are created using Flash software from Adobe; a concept map is a graphic depiction of a set of 

ideas and the linkages among them. 
 43

Computer-Based Instruction 

The advantage of incorporating elements that are generally found in stand-alone computer-based 

instruction into online learning seems to depend on the nature of the contrasting conditions. 

Quizzes, simulations and individualized instruction, all common to stand-alone computer-based 

instruction, appear to vary in their effectiveness when added to an online learning environment. 

Online Quizzes 

Research on incorporating quizzes into online learning does not provide evidence that the 

practice is effective. The four studies that examined the effectiveness of online quizzes (Lewis 

2002; Maag 2004; Stanley 2006; Tselios et al. 2001) had mixed findings. Maag (2004) and 

Stanley (2006) found no advantage for the inclusion of online quizzes. Maag included online 

quizzes in a treatment condition that also provided students with online images, text and some 

animation; the treatment group was compared with other groups, which differed both in the 

absence of online quizzes and in terms of the media used (one had the same text and images 

delivered online, one had printed text only, and one had printed text plus images). Maag found 

no significant difference between the online group that had the online quizzes and the online 

group that did not. Stanley (2006) found that outcomes for students taking weekly online quizzes 

did not differ statistically from those for students who completed homework instead. 

Two other studies suggested that whether or not quizzes positively affect learning may depend 

on the presence of other variables. Lewis (2002) grouped students into two cohorts. For six 

modules, Group 1 took online quizzes and Group 2 participated in online discussions. For six 

other modules, the groups switched so that those who had been taking the online quizzes 

participated in online discussions and vice versa. When Group 1 students took the online quizzes, 

they did significantly better than those participating in discussions, but no difference was found 

between the groups when Group 2 took the online quizzes in the other six modules. The 

researchers interpreted this interaction between student group and condition in terms of the 

degree of interactivity in the online discussion groups. Group 1 was more active in the online 

discussions, and the authors suggested that this activity mitigated any loss in learning otherwise 

associated with not taking quizzes. 

Tselios et al. (2001) suggest that the software platform used to deliver an online quiz may affect 

test performance. In their study, students completing an online quiz in WebCT performed 

significantly better than students taking the online quiz on a platform called IDLE. The 

educational content in the two platforms was identical and their functionality was similar; 

however, they varied in the details of their user interfaces. 

Simulations 

The results of three studies exploring the effects of including different types of online simulations 

were modestly positive. Two of the studies indicated a positive effect from including an online 

simulation; however, one study found no significant difference. In an online module on 

information technology for undergraduate psychology students, Castaneda (2008) contrasted two 

simulation conditions (one provided a simulation that students could explore as they chose, and 

the other guided the students’ interaction with the simulation, providing some feedback and 
 44

expository material) with a condition that included no simulation. Castaneda also manipulated 

the sequencing of instructional activities, with the interaction with the simulation coming either 

before or after completion of the expository portion of the instructional module. Knowledge 

gains from pre- to posttest were greater for students with either type of simulation, provided they 

were exposed to it after, rather than before, the expository instruction. 

Hibelink (2007) explored the effectiveness of using two-dimensional versus three-dimensional 

images of human anatomy in an online undergraduate human anatomy lab. The group of students 

that used three-dimensional images had a small, but significant advantage in identifying 

anatomical parts and spatial relationships. Contrasting results were obtained by Loar (2007) in an 

examination of the effects of computer-based case study simulations on students’ diagnostic 

reasoning skills in nurse practitioner programs. All groups received identical online lectures, 

followed by an online text-based case study for one group and by completion of a computersimulated case study for the other. No difference was found between the group receiving the case 

simulation versus that receiving the text-based version of the same case. 

Individualized Instruction 

The online learning literature has also explored the effects of using computer-based instruction 

elements to individualize instruction so that the online learning module or platform responds 

dynamically to the participant’s questions, needs or performance. There were only two online 

learning studies of the effects of individualizing instruction, but both found a positive effect. 

Nguyen (2007) compared the experiences of people learning to complete tax preparation 

procedures, contrasting those who used more basic online training with those who used an 

enhanced interface that incorporated a context-sensitive set of features, including integrated 

tutorials, expert systems, and content delivered in visual, aural and textual forms. Nguyen found 

that this combination of enhancements had a positive effect. 

Grant and Courtoreille (2007) studied the use of post-unit quizzes presented either as (a) fixed 

items that provided feedback only about whether or not the student’s response was correct or (b) 

post-unit quizzes that gave the student the opportunity for additional practice on item types that 

had been answered incorrectly. The response-sensitive version of the tutorial was found to be 

more effective than the fixed-item version, resulting in greater changes between pre- and posttest 

scores. 

Supports for Learner Reflection 

Nine studies (Bixler 2008; Chang 2007; Chung, Chung and Severance 1999; Cook et al. 2005; 

Crippen and Earl 2007; Nelson 2007; Saito and Miwa 2007; Shen, Lee and Tsai 2007; Wang et 

al. 2006) examined the degree to which promoting aspects of learner reflection in a Web-based 

environment improved learning outcomes. These studies found that a tool or feature prompting 

students to reflect on their learning was effective in improving outcomes. 

For example, Chung, Chung and Severance (1999) examined how computer prompts designed to 

encourage students to use self-explanation and self-monitoring strategies affected learning, as 

measured by students’ ability to integrate ideas from a lecture into writing assignments. Chung et 
 45

al. found that students in the group receiving the computer prompts integrated and elaborated a 

significantly higher number of the concepts in their writing than did those in the control group. 

In a quasi-experimental study of Taiwan middle school students taking a Web-based biology 

course, Wang et al. (2006) found that students in the condition using a formative online selfassessment strategy performed better than those in conditions using traditional tests, whether the 

traditional tests were online or administered in paper-and-pencil format. In the formative online 

assessment condition, when students answered an item incorrectly, they were told that their 

response was not correct, and they were given additional resources to explore to find the correct 

answer. (They were not given the right answer.) This finding is similar to that of Grant and 

Courtoreille (2007) described above. 

Cook et al. (2005) investigated whether the inclusion of “self-assessment” questions at the end of 

modules improved student learning. The study used a randomized, controlled, crossover trial, in 

which each student took four modules, two with the self-assessment questions and two without. 

The order of modules was randomly assigned. Student performance was statistically higher on 

tests taken immediately after completion of modules that included self-assessment questions than 

after completion of those without such questions—an effect that the authors attributed to the 

stimulation of reflection. This effect, however, did not persist on an end-of-course test, on which 

all students performed similarly. 

Shen, Lee and Tsai (2007) found a combination of effects for self-regulation and opportunities to 

learn through realistic problems. They compared the performance of students who did and did 

not receive instruction in self-regulation learning strategies such as managing study time, goalsetting and self-evaluation. The group that received instruction in self-regulated learning 

performed better in their online learning. 

Bixler (2008) examined the effects of question prompts asking students to reflect on their 

problem-solving activities. Crippen and Earl (2007) investigated the effects of providing students 

with examples of chemistry problem solutions and prompts for students to provide explanations 

regarding their work. Chang (2007) added a self-monitoring form for students to record their 

study time and environment, note their learning process, predict their test scores and create a 

self-evaluation. Saito and Miwa (2007) investigated the effects of student reflection exercises 

during and after online learning activities. Nelson (2007) added a learning guidance system 

designed to support a student’s hypothesis generation and testing processes without offering 

direct answers or making judgments about the student’s actions. In all of these studies, the 

additional reflective elements improved students’ online learning. 

Overall, the available research evidence suggests that promoting self-reflection, self-regulation 

and self-monitoring leads to more positive online learning outcomes. Features such as prompts 

for reflection, self-explanation and self-monitoring strategies have shown promise for improving 

online learning outcomes. 
 46

Moderating Online Groups 

Organizations providing or promoting online learning generally recommend the use of 

instructors or other adults as online moderators, but research support for the effects of this 

practice on student learning is mixed. A study by Bernard and Lundgren-Cayrol (2001) suggests 

that instructor moderation may not improve learning outcomes in all contexts. The study was 

conducted in a teacher education course on educational technology in which the primary 

pedagogical approach was collaborative, project-based learning. Students in the course were 

randomly assigned to groups receiving either low or high intervention on the part of a moderator 

and composed of either random or self-selected partners. The study did not find a main effect for 

moderator intervention. In fact, the mean examination scores of the low-moderation, randomselection groups were significantly higher than those of the other groups. A study by De Wever, 

Van Winckel and Valcke (2008) also found mixed effects resulting from instructor moderation. 

This study was conducted during a clinical rotation in pediatrics in which knowledge of patient 

management was developed through case-based asynchronous discussion groups. Researchers 

used a crossover design to create four conditions based on two variables: the type of moderator 

(instructor moderator versus student moderator) and the presence of a developer of alternatives 

for patient management (assigned developer versus no assigned developer). The presence of a 

course instructor as moderator was found not to improve learning outcomes significantly. When 

no assigned developer of alternatives was assigned, the two moderator conditions performed 

equivalently. When a developer of alternatives was specified, the student-moderated groups 

performed significantly better than the instructor-moderated groups. 

Alternately, Zhang (2004) found that an externally moderated group scored significantly higher 

on problems calling for use of statistical knowledge and problem-solving skills than a peercontrolled group on both well- and ill-structured problems. Zhang’s study compared the 

effectiveness of peer versus instructor moderation of online asynchronous collaboration. 

Students were randomly assigned to one of two groups. One group had a “private” online space 

where students entirely controlled discussion. The other group’s discussion was moderated by 

the instructor, who also engaged with students through personal e-mails and other media. 

Scripts for Online Interaction 

Four Category 3 studies investigated alternatives to human moderation of online discussion in 

the form of “scaffolding” or “scripts” designed to produce more productive online interaction. 

The majority of these studies indicated that the presence of scripts to guide interactions among 

groups learning together online did not appear to improve learning outcomes. 

The one study that found positive student outcomes for learners who had been provided scripts 

was conducted by Weinberger et al. (2005). These researchers created two types of scripts: 

“epistemic scripts,” which specified how learners were to approach an assigned task and guided 

learners to particular concepts or aspects of an activity, and “social scripts,” which structured 

how students should interact with each other through methods such as gathering information 

from each other by asking critical questions. They found that social scripts improved 

performance on tests of individual knowledge compared with a control group that participated in 

online discussions without either script (whether or not the epistemic script was provided). 
 47

The remaining three studies that examined the effect of providing scripts or scaffolds for online 

interaction found no significant effect on learning (Choi, Land and Turgeon 2005; Hron et al. 

2000; Ryan 2007). Hron et al. (2000) used an experimental design to compare three groups: (a) a 

control that received no instructions regarding a 1-hour online discussion, (b) a group receiving 

organizing questions to help structure their online communication and (c) a group receiving both 

the organizing questions and rules for discussion. The discussion rules stated that group members 

should discuss only the organizing questions; that discussion of one question had to be 

completed before the next discussion was begun; that the discussion needed to be structured as 

an argument, with claims justified and alternative viewpoints considered; and that all participants 

should take turns moderating the discussion and making sure that the discussion adhered to the 

rules. Hron et al. found statistically significant differences across conditions in the content and 

coherence of student postings, but no difference across the three groups in terms of knowledge 

acquisition as measured by a multiple-choice test. 

Ryan’s study (2007) reached conclusions similar to those of Hron et al. Ryan hypothesized that 

exposure to collaborative tools would affect student performance. He compared two groups of 

middle school students: a treatment group, which engaged in online learning that included 

interaction with instructors and peers using online collaboration tools, and a control group, which 

did not have access to or instruction in the use of collaboration tools. Like Hron et al., Ryan 

found no significant difference in academic performance between the two groups of online 

students. 

Choi, Land and Turgeon (2005) used a time-series control-group design to investigate the effects 

of providing online scaffolding for generating questions to peers during online group 

discussions. Although scaffolds were found to increase the number of questions asked, they did 

not affect question quality or learner outcomes. 

In summary, mechanisms such as scaffolds or scripts for student group interaction online have 

been found to influence the way students engage with each other and with the online material, 

but have not been found to improve learning. 

Delivery Platform 

Several platform options are available for online learning—an exclusively Web-based 

environment or e-mail or mobile phone. The alternative platforms can be used as primary 

delivery channels or as supplements to Web-based instruction. Neither of the two studies that 

addressed this issue found significant differences across delivery platforms. Shih (2007) 

investigated whether student groups who accessed online materials by means of mobile phone 

demonstrated significantly different learning outcomes from groups who did so using a 

traditional computer; the author found no statistical difference between the two groups. 

Similarly, Kerfoot (2008) compared the effects of receiving course materials and information 

through a series of e-mails spaced out over time versus accessing the online materials all at once 

by means of a traditional Web-site and found no statistical difference. 

Overall, the controlled studies are too few to support even tentative conclusions concerning the 

learning effects of using alternative or multiple delivery platforms for online learning. 
 48

Summary 

This narrative review has illustrated the many variations in online, individual and group, and 

synchronous and asynchronous activities that can be combined in a course or instructional 

intervention. The number of Category 3 studies concerning any single practice was insufficient 

to warrant a quantitative meta-analysis, and the results varied to such an extent that only 

tentative, rather than firm, conclusions can be drawn about promising online learning practices. 

The direct comparison of blended and purely online conditions in 10 studies produced mostly 

null results, tempering what appeared to be an advantage of blended compared with purely 

online instruction in the moderator variable analysis that was conducted as part of the metaanalysis presented in chapter 3. Although a fair number of Category 3 studies contrasted these 

two versions of online learning, few equated instructional content or activities across conditions, 

making it difficult to draw conclusions. 

With respect to incorporation of multiple media, the evidence available in the Category 3 studies 

suggests that inclusion of more media in an online application does not enhance learning when 

content is controlled, but some evidence suggests that the learner’s ability to control the learning 

media is important (Zhang 2005; Zhang et al. 2006). Alternately, the set of studies using various 

manipulations to try to stimulate more active engagement on the part of online learners (such as 

use of advanced organizers, conceptual maps, or guiding questions) had mostly null results. 

The clearest recommendation for practice that can be made on the basis of the Category 3 

synthesis is to incorporate mechanisms that promote student reflection on their level of 

understanding. A dozen studies have investigated what effects manipulations that trigger learner 

reflection and self-monitoring of understanding have on individual students’ online learning 

outcomes. Ten of the studies found that the experimental manipulations offered advantages over 

online learning that did not provide the trigger for reflection. 

Another set of studies explored features usually associated with computer-based instruction, 

including the incorporation of quizzes, simulations, and techniques for individualizing 

instruction. The providing of simple multiple-choice quizzes did not appear to enhance online 

learning. The incorporation of simulations produced positive effects in two out of three studies 

(Castaneda 2008; Hibelink 2007). Individualizing online learning by dynamically generating 

learning content based on the student’s responses was found to be effective in the two studies 

investigating this topic (Grant and Courtoreille 2007; Nguyen 2007). 

Attempts to guide the online interactions of groups of learners were less successful than the use 

of mechanisms to prompt reflection and self-assessment on the part of individual learners. Some 

researchers have suggested that students who learn in online groups provide scaffolds for one 

another (Suh 2006). 
 49

Finally, readers should be cautioned that the literature on alternative online learning practices has 

been conducted for the most part by professors and other instructors who are conducting research 

using their own courses. Moreover, the combinations of technology, content and activities used 

in different experimental conditions have often been ad hoc rather than theory based. As a result, 

the field lacks a coherent body of linked studies that systematically test theory-based approaches 

in different contexts. 

51

5. Discussion and Implications 

The meta-analysis reported here differs from prior meta-analyses of distance learning in several 

important respects: 

! Only studies of Web-supported learning have been included. 

! All effects have been based on objective measures of learning. 

! Only studies with controlled designs that met minimum quality criteria have been 

included. 

The corpus of 50 effect sizes extracted from 45 studies meeting these criteria was sufficient to 

demonstrate that in recent applications, online learning has been modestly more effective, on 

average, than the traditional face-to-face instruction with which it has been compared. It should 

be noted, however, that this overall effect can be attributed to the advantage of blended learning 

approaches over instruction conducted entirely face-to-face. Of the 11 individual studies with 

significant effects favoring the online condition, 9 used a blended learning approach. 

The test for homogeneity of effects found significant variability in the effect sizes for the 

different online learning studies, justifying a search for moderator variables that could explain 

the differences in outcomes. The moderator variable analysis found only three moderators 

significant at p < .05. Effects were larger when a blended rather than a purely online condition 

was compared with face-to-face instruction; when students in the online condition were engaged 

in instructor-led or collaborative instruction rather than independent learning; and when the 

curricular materials and instruction varied between the online and face-to-face conditions. This 

pattern of significant moderator variables is consistent with the interpretation that the advantage 

of online conditions in these recent studies stems from aspects of the treatment conditions other 

than the use of the Internet for delivery per se. 

Clark (1983) has cautioned against interpreting studies of instruction in different media as 

demonstrating an effect for a given medium inasmuch as conditions may vary with respect to a 

whole set of instructor and content variables. That caution applies well to the findings of this 

meta-analysis, which should not be construed as demonstrating that online learning is superior as 

a medium. Rather, it is the combination of elements in the treatment conditions, which are likely 

to include additional learning time and materials as well as additional opportunities for 

collaboration, that has proven effective. The meta-analysis findings do not support simply 

putting an existing course online, but they do support redesigning instruction to incorporate 

additional learning opportunities online. 

Several practices and conditions associated with differential effectiveness in distance education 

meta-analyses (most of which included nonlearning outcomes such as satisfaction) were not 

found to be significant moderators of effects in this meta-analysis of Web-based online learning. 

Nor did tests for the incorporation of instructional elements of computer-based instruction (e.g., 

online practice opportunities and feedback to learners) find that these variables made a 

difference. Online learning conditions produced better outcomes than face-to-face learning alone, 

regardless of whether these instructional practices were used. 
52

The meta-analysis did not find differences in average effect size between studies published 

before 2004 (which might have used less sophisticated Web-based technologies than those 

available since) and studies published from 2004 on (possibly reflecting the more sophisticated 

graphics and animations or more complex instructional designs available). Nor were differences 

associated with the nature of the subject matter involved. 

Finally, the examination of the influence of study method variables found that effect sizes did not 

vary significantly with study sample size or with type of design. It is reassuring to note that, on 

average, online learning produced better student learning outcomes than face-to-face instruction 

in those studies with random-assignment experimental designs (p < .001) and in those studies 

with the largest sample sizes (p < . 01). 

The relatively small number of studies meeting criteria for inclusion in this meta-analysis limits 

the power of tests for moderator variables. A few contrasts that did not attain significance (e.g., 

time on task or type of knowledge tested) might have emerged as significant influences under a 

fixed-effects analysis and may prove significant when tested in future meta-analyses with a 

larger corpus of studies. 

The narrative synthesis of studies comparing variations of online learning provides some 

additional insights with respect to designing effective online learning experiences. The practice 

with the strongest evidence of effectiveness is inclusion of mechanisms to prompt students to 

reflect on their level of understanding as they are learning online. In a related vein, there is some 

evidence that online learning environments with the capacity to individualize instruction to a 

learner’s specific needs improves effectiveness. 

As noted in chapter 4, the results of studies using purely online and blended conditions cast some 

doubt on the meta-analysis finding of larger effect sizes for studies blending online and face-toface elements. The inconsistency in the implications of the two sets of studies underscores the 

importance of recognizing the confounding of practice variables in most studies. Studies using 

blended learning also tend to involve more learning time, additional instructional resources, and 

course elements that encourage interactions among learners. This confounding leaves open the 

possibility that one or all of these other practice variables, rather than the blending of online and 

offline media per se, accounts for the particularly positive outcomes for blended learning in the 

studies included in the meta-analysis. 

Comparison With Meta-Analyses of Distance Learning 

Because online learning has much in common with distance learning, it is useful to compare the 

findings of the present meta-analysis with the most comprehensive recent meta-analyses in the 

distance-learning field. The two most pertinent earlier works are those by Bernard et al. (2004) 

and Zhao et al. (2005). As noted above, the corpus in this meta-analysis differed from the earlier 

quantitative syntheses, not only in including more recent studies but also in excluding studies 

that did not involve Web-based instruction and studies that did not examine an objective student 

learning outcome. 

Bernard et al. (2004) found advantages for asynchronous over synchronous distance education, a 

finding that on the surface appears incongruent with the results reported here. On closer 
53

inspection, however, it turns out that the synchronous distance-education studies in the Bernard 

et al. corpus were mostly cases of a satellite classroom yoked to the main classroom where the 

instructor taught. It is likely that the nature of the learning experience and extent of collaborative 

learning were quite different in the primary and distant classrooms in these studies. For 

asynchronous distance education, Bernard et al. also found that the distance-education condition 

tended to have more favorable outcomes when opportunities for computer-mediated 

communication were available. Online learners in all of the studies in this meta-analysis had 

access to computer-mediated communication and in every case there were mechanisms for 

asynchronous communication. 

Zhao et al. (2005) found advantages for blended learning (combining elements of online and 

face-to-face communication) over purely online learning experiences, a finding similar to that of 

this meta-analysis. Zhao et al. also found that instructor involvement was a strong mediating 

variable. Distance learning outcomes were less positive when instructor involvement was low (as 

in “canned” applications), with effects becoming more positive, up to a point, as instructor 

involvement increased. At the highest level of instructor involvement (which would suggest that 

the instructor became dominant and peer-to-peer learning was minimized), effect size started to 

decline in the corpus of studies Zhao et al. examined. Although a somewhat different construct 

was tested in the Learning Experience variable used here, the present results are consonant with 

those of Zhao et al. Studies in which the online learners worked with digital resources with little 

or no teacher guidance were coded here as “independent/active,” and this category was the one 

learner experience category for which the advantage of online learning failed to attain statistical 

significance at the p < .05 level or better. 

The relative disadvantage of independent online learning (called “active” in our conceptual 

model) should not be confused with automated mechanisms that encourage students to be more 

reflective or more actively engaged with the material they are learning on line. As noted above, a 

number of studies reviewed in chapter 4 found positive effects for techniques such as prompts 

that encourage students to assess their level of understanding or set goals for what they will learn 

whereas mechanisms such as guiding questions or advance organizers had mostly null results. 

Implications for K–12 Education 

The impetus for this meta-analysis of recent empirical studies of online learning was the need to 

develop research-based insights into online learning practices for K–12 students. The research 

team realized at the outset that a look at online learning studies in a broader set of fields would 

be necessary to assemble sufficient empirical research for meta-analysis. As it happened, the 

initial search of the literature published between 1996 and 2006 found no studies contrasting K–

12 online learning with face-to-face instruction that met methodological quality criteria.23 By 

 

23 The initial literature search identified several K–12 online studies comparing student achievement data collected 

from both virtual and regular schools (e.g., Cavanaugh et al. 2004; Schollie 2001), but these studies were neither 

experiments nor quasi-experiments with statistical control for preexisting differences between groups. Some of 

these K–12 studies used a pre-post, within-subject design without a comparison group; others were quasiexperiments without a statistical control for preexisting differences among study conditions (e.g., Karp and 

Woods 2003; Long and Stevens 2004; Stevens 1999). Several studies used experimental designs with K–12 

students but did not report the data needed to compute or estimate effect sizes. A few experiments compared a K–
54

performing a second literature search with an expanded time frame (through July 2008), the team 

was able to greatly expand the corpus of studies with controlled designs and to identify five 

controlled studies of K–12 online learning with seven contrasts between online and face-to-face 

conditions. This expanded corpus still comprises a very small number of studies, especially 

considering the extent to which secondary schools are using online courses and the rapid growth 

of online instruction in K–12 education as a whole. Educators making decisions about online 

learning need rigorous research examining the effectiveness of online learning for different types 

of students and subject matter as well as studies of the relative effectiveness of different online 

learning practices. 

 12 online intervention with a condition in which there was no instruction (e.g., Teague and Riley 2006). Many of 

the references (8 out of 14) used for the Cavanaugh et al. (2004) meta-analysis of K–12 online studies were 

databases of raw student performance data and did not describe learning conditions, technology use or 

learner/instructor characteristics. A recent large-scale study by the Florida TaxWatch (2007) failed to control for 

preexisting differences between the students taking courses online and those taking them in conventional 

classrooms. 
55

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c

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d

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A-1

Appendix 

Meta-Analysis Methodology 

Terms and Processes Used in the Database Searches 

In March 2007, researchers performed searches through the following four data sources: 

1. Electronic research databases. Using a common set of keywords (see Exhibit A-1), 

searches were performed in ERIC, PsycINFO, PubMed, ABI/INFORM, and UMI 

ProQuest Digital Dissertations. In addition, to make sure that studies of online 

learning in teacher professional development and career technical education were 

included, additional sets of keywords, shown in Exhibit A-2, were used in additional 

searches of ERIC and PsycINFO. 

2. Recent meta-analyses and narrative syntheses. Researchers reviewed the lists of 

studies included in Bernard et al. (2004), Cavanaugh et al. (2004), Childs (2001), 

Sitzmann et al. (2006), Tallent-Runnels et al. (2006), Wisher and Olson (2003), and 

Zhao et al. (2005) for possible inclusions. Additionally, for teacher professional 

development and career technical education, references from recent narrative research 

syntheses in those fields (Whitehouse et al. 2006; Zirkle 2003) were examined to 

identify potential studies for inclusion. 

3. Key journals. Abstracts were manually reviewed for articles published since 2005 in 

American Journal of Distance Education, Journal of Distance Education (Canada), 

Distance Education (Australia), International Review of Research in Distance and 

Open Education, and Journal of Asynchronous Learning Networks. In addition, the 

Journal of Technology and Teacher Education and Career and Technical Education 

Research (formerly known as Journal of Vocational Education Research) were 

manually searched. 

4. Google Scholar searches. To complement these targeted searches, researchers used 

limiting parameters and sets of keywords (available from the authors of this report) in 

the Google Scholar search engine. 
A-2

Exhibit A-1. Terms for Initial Research Database Search 

Technology and 

Education/

Training Terms Study Design Termsa

Distance education Control group

Distance learning Comparison group

E-learning Treatment group

Online education Experimental

Online learning

Online training

Online course

Virtual learning

Virtual training

Virtual & course

Internet & learning

Internet & training 

Internet & course

Web-based learning

Web-based instruction

Web-based course

Web-based training

“Distributed learning”

a All four terms were used in one query with “OR” if the 

database allowed. 

Exhibit A-2. Terms for Additional Database Searches for Online Career Technical Education and 

Teacher Professional Development 

Education Terms

Technology 

Terms Study Design Terms

Career education Distance Control group

Vocational education Distributed Comparison group

Teacher education E-learning Experimental

Teacher mentoring Internet Randomized

Teacher professional 

development

Online Treatment group

Teacher training Virtual

Technical education Web-based
A-3

Additional Sources of Articles 

Exhibit A-3 lists the sources for the resulting 502 articles that went through full-text screening. 

Exhibit A-3. Sources for Articles in the Full-Text Screening 

Number of Articles 

Identified and Passing 

Initial Screening

Total retained for full-text screen 502

Source of articles in full-text screen:

Electronic research database searches 316

Additional database searches for teacher 

professional development and career 

technical education 6

Recent meta-analyses 171

Manual review of key journals 19

Google Scholar searches 31

Recommendations from experts 3

Overlaps –36

Unretrievable –8

 
A-4

Effect Size Extraction 

Of the 176 studies passing the full-text screening, 99 were identified as having at least one 

contrast between online learning and face-to-face or offline learning (Category 1) or between 

blended learning and face-to-face/offline learning (Category 2). These studies were transferred to 

quantitative analysts for effect size extraction. 

Numerical and statistical data contained in the studies were extracted for analysis with 

Comprehensive Meta-Analysis software (Biostat Solutions 2006). Data provided in the form of ttests, F-tests, correlations, p-levels, and frequencies were used for this purpose. 

During the data extraction phase, it became apparent that one set of studies rarely provided 

sufficient data for Comprehensive Meta-Analysis calculation of an effect size. Quasiexperimental studies that used hierarchical linear modeling or analysis of covariance with 

adjustment for pretests and other learner characteristics through covariates typically did not 

report some of the data elements needed to compute an effect size. For studies using hierarchical 

linear modeling to analyze effects, typically the regression coefficient on the treatment status 

variable (treatment or control), its standard error, and a p-value and sample sizes for the two 

groups were reported. For analyses of covariance, typically the adjusted means and F-statistic 

were reported along with group sample sizes. In almost all cases, the unadjusted standard 

deviations for the two groups were not reported and could not be computed because the pretestposttest correlation was not provided. Following the advice of Robert Bernard, the chief metaanalysis expert on the project’s Technical Working Group, analysts decided to retain these 

studies and to use a conservative estimate of the pretest-posttest correlation (r = .70) in 

estimating an effect size for those studies where the pretest was the same measure as the posttest 

and using a pretest-posttest correlation of r = .50 when it was not. These effect sizes were 

flagged in the coding as “estimated effect sizes,” as were effect sizes computed from t tests, F

tests, and p levels. 

In extracting effect size data, the analysts followed a set of rules: 

! The unit of analysis was the independent contrast between online condition and face-toface condition (Category 1) or between blended condition and face-to-face condition 

(Category 2). Some studies reported more than one contrast, either by reporting more 

than one experiment or by having multiple treatment conditions (e.g., online vs. blended 

vs. face-to-face) in a single experiment. 

! When there were multiple treatment groups or multiple control groups and the nature of 

the instruction in the groups did not differ considerably (e.g., two treatment groups both 

fell into the “blended” instruction category), then the weighted mean of the groups and 

pooled standard deviation were used. 

! When there were multiple treatment groups or multiple control groups and the nature of 

the instruction in the groups did differ considerably (e.g., one treatment was purely online 

whereas the other treatment was blended instruction, both compared against the face-toface condition), then analysts treated them as independent contrasts. 
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! In general, one learning outcome finding was extracted from each study. When multiple 

learning outcome data were reported (e.g., assignments, midterm and final examinations, 

grade point averages, grade distributions), the outcome that could be expected to be more 

stable and more closely aligned to the instruction was extracted (e.g., final examination 

scores instead of quizzes). However, in some studies, no learning outcome had obvious 

superiority over the others. In such cases, analysts extracted multiple contrasts from the 

study and calculated the weighted average of the multiple outcome scores if the outcome 

measures were similar (e.g., two final tests, one testing procedural skills and the other 

testing declarative knowledge). For example, in one study, analysts retained two outcome 

findings because the outcome measures were quite different (Schilling et al. 2006). One 

measure was a multiple-choice test, examining basic knowledge, whereas the other was a 

performance-based assessment, testing students’ strategic and problem-solving skills in 

the context of ill-structured problems. 

! Learning outcome findings were extracted at the individual level. Analysts did not extract 

group-level learning outcomes (e.g., scores for a group product). Too few group products 

were included in the studies to support analyses of this variable. 

The review of the 99 studies for effect size calculation produced 50 independent effect sizes (27 

for Category 1 and 23 for Category 2) from 45 studies; 54 studies did not report sufficient data to 

support effect-size calculation. 

Coding of Study Features 

All studies that provided enough effect size data were coded for their study features and for study 

quality. The top-level coding structure, incorporating refinements made after pilot testing, is 

shown in Exhibit A-4. (The full coding structure is available from the authors of this report.) 

Twenty percent of the studies with sufficient data to compute effect size were coded by two 

researchers. The interrater reliability across these double-coded studies was 86.4 percent. As a 

result of analyzing coder disagreements, some definitions and decision rules for some codes were 

refined; other codes that required information missing in the vast majority of documents or that 

proved difficult to code reliably (e.g., indication of whether the instructor was certified or not) 

were eliminated. A single researcher coded the remaining studies. 

 
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Exhibit A-4. Top-level Coding Structure for the Meta-analysis 

Study Feature Coding Categories 

! Study type 

! Type of publication 

! Year of publication 

! Study author 

! Whether the instructor was trained in online training 

! Learner type 

! Learner age 

! Learner incentive for involvement in the study 

! Learning setting 

! Subject matter 

! Treatment duration 

! Dominant approach to learner control 

! Media features 

! Opportunity for face-to-face contact with the instructor 

! Opportunity for face-to-face contact with peers 

! Opportunity for asynchronous computer-mediated communication with the instructor 

! Opportunity for asynchronous computer-mediated communication with peers 

! Opportunity for synchronous computer-mediated communication with the instructor 

! Opportunity for synchronous computer-mediated communication with peers 

! Use of problem-based or project-based learning 

! Opportunity for practice 

! Opportunity for feedback 

! Type of media-supported pedagogy 

! Nature of outcome measure 

! Nature of knowledge assessed 

Study Design Codes 

! Unit of assignment to conditions 

! Sample size for unit of assignment 

! Student equivalence 

! Whether equivalence of groups at preintervention was described 

! Equivalence of prior knowledge/pretest scores 

! Instructor equivalence 

! Time-on-task equivalence 

! Curriculum material/instruction equivalence 

! Attrition equivalence 

! Contamination 
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