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What Makes the Difference? A Practical Analysis of Research on the Effectiveness of Distance Education

by Yong Zhao, Jing Lei, Bo Yan, Chun Lai & Sophia Tan - 2005

This article reports findings of a meta-analytical study of research on distance education. The purpose of this study was to identify factors that affect the effectiveness of distance education. The results show that although the aggregated data of available studies show no significant difference in outcomes between distance education and face-to-face education as previous research reviews suggest, there is remarkable difference across the studies. Further examination of the difference reveals that distance education programs, just like traditional education programs, vary a great deal in their outcomes, and the outcome of distance education is associated with a number of pedagogical and technological factors. This study led to some important data-driven suggestions for and about distance education.

History is a mirror of the past and a lesson for the present.

— Persian proverb

Research in distance education1 has traditionally been dominated by what is often referred to as “comparison studies” or “media comparison studies” (R. E. Clark, 1983; R. E. Clark & Salomon, 1986; Gunawardena & McIsaac, 2004; Lockee, Burton, & Cross, 1999; McIsaac & Gunawardena, 1996). Typ­ically these studies compared the effectiveness of distance education with that of face-to-face education or the effectiveness of one technology over another (Gunawardena & McIsaac, 2004; Joy & Garcia, 2000; Lockee et al., 1999; McIsaac & Gunawardena, 1996). Most of these studies found no significant differences in learning outcomes (Gunawardena & McIsaac, 2004; McIsaac & Gunawardena, 1996; Russell, 1999). Although some studies found positive effects, their effects are then quickly balanced out by other studies that found negative effects (Gunawardena & McIsaac, 2004; McIsaac & Gunawardena, 1996). Thus as a whole, distance education has been said to be as effective as its face-to-face counterpart (Institute for Higher Education Policy, 1999).

These comparison studies have been seriously criticized for many rea­sons: focusing on the wrong factor, methodologically flawed, biased sam­pling, using improper measures of outcomes, even being pseudoscientific (R. E. Clark, 1983; R. E. Clark & Salomon, 1986; Gunawardena & McIsaac, 2004; Joy & Garcia, 2000; Lockee et al., 1999; McIsaac & Gunawardena, 1996). But the most serious charge is that these studies are futile and use­less. “Whatever methods have been used to report the results of media comparison studies and their instructional impact, these studies have yield­ed very little useful guidance for distance education practice” (Gunawar­dena & McIsaac, 2004, p. 378). On these grounds, researchers have been called to discontinue this line of research (R. E. Clark, 1994; Gunawardena & McIsaac, 2004; Lockee et al., 1999; McIsaac & Gunawardena, 1996). Instead, they are advised to move “beyond the no-significant difference” (Twigg, 2001, p. 7), to use different designs, and to focus on different factors (R. E. Clark, 1994; Gunawardena & McIsaac, 2004; Koumi, 1994; Lockee et al., 1999; McIsaac & Gunawardena, 1996).

Although the call for a new paradigm of research in distance education has not stopped researchers from conducting comparison studies in dis­tance education, as publications of this nature continue to appear in schol­arly journals and professional conferences (Lockee et al., 1999), the characterization of previous comparison studies as flawed and useless has the potential, if it has not already done so, to lead to a complete dismissal of a large body of literature. And that could be a mistake, because these studies may have what we are looking for useful guidance for future practices and research when examined with a different lens.

Traditionally, the lens used to synthesize media comparison studies has often in essence been the same as the one used by comparison studies: the difference finder. A number of synthesis studies have been conducted in the field of distance education (Allen, Bourhis, Burrell, & Mabry, 2002; Cavanaugh, 2001; Dubin & Hedley, 1969; Machtmes & Asher, 2000; M. G. Moore & Thompson, 1996; Schlosser & Anderson, 1994; Schramm, 1962). These studies, not unlike the studies they synthesize, attempted to assess whether distance education or its delivery media, taken as a whole, pro­duced better, worse, or the same learning outcomes relative to face-to-face instruction. While some of these review studies, especially earlier ones, did qualitative analysis of the literature, a few more recent ones employed the meta-analysis method when more experimental or quasi-experimental studies became available. Regardless of the approach, these studies resulted in complaining either that the literature has too many methodological problems to lead to any conclusion (Schlosser & Anderson, 1994; Stickell, 1963) or that there is no significant difference between distance and face-to-face instruction (Cavanaugh, 2001; Machtmes & Asher, 2000; M. G. Moore & Thompson, 1996; Russell, 1999).

These studies have undoubtedly helped to promote distance instruction as a viable form of education with the same quality as its face-to-face coun­terpart; they have also led to the belief that previous distance education research is of low quality and has little to offer in terms of practical guidance for improving practice. Consequently, it is suggested that we should just discard it and move forward. However, upon closer examination, these studies reveal that individual studies indeed found significant difference between distance and face-to-face instruction. It is likely that a systematic analysis of what may account for the different findings across studies could provide us with practical guidance for improving practice.

The no-significant-difference conclusion was primarily drawn from two types of analyses: summary of studies that found no significant difference (Russell, 1999) and meta-analysis (Cavanaugh, 2001; Machtmes & Asher, 2000). The most influential and representative of the first type is Russell’s inventory of studies that found no significant difference. In 1999, Russell published an annotated bibliography of studies that found no significant difference between face-to-face and distance education. These studies span almost a century, beginning in 1928 and ending in 1998. This impressive collection of 355 articles forcefully supported the no-significant-difference phenomenon, a term used by Russell to refer to effectiveness studies of dis­tance education (including instruction using technology). However, most of the studies on this widely circulated list were not experimental studies. Instead, they were “surveys with small sample sizes (less than forty), no mention of the return rate of the surveys, and no mention of the learner demographics” (Machtmes & Asher, 2000, p. 31). Furthermore, these stud­ies were not identified using any systematic approach. According to Russell: “I did not use any scientific sampling method but instead listed every study found that showed no significant difference. . . . The point remains that such studies are practically nonexistent and the very few that do exist are offset by a like number which show negative results for the technology-based instruction” (p. xiii). For these reasons, the validity and reliability of Russell’s claim should be questioned.

A more valid and reliable way to synthesize the literature is meta-analysis (Glass, 1976; Glass, McGaw, & Smith, 1981) because of its systematic proce­dure and criteria for identifying and selecting previous studies and verified statistical procedures for analyzing results. Interestingly, the two meta-analyses available (Cavanaugh, 2001; Machtmes & Asher, 2000) came to essen­tially the same conclusion as Russell: There is no significant difference between the outcomes of distance and face-to-face education. However, these two meta-analyses also found considerable differences among studies in terms of effect size, a measure of difference in learning outcomes between the two comparison groups, in this case, distance education and face-to-face educa­tion. For example, Cavanaugh (2001), after calculating the effect sizes of 19 studies that compared K-12 students learning with interactive distance ed­ucation technology with students learning with traditional classroom instruc­tion, found the weighted mean effect size across all studies to be 0.147, but the standard deviation was 0.69, indicating a significant variation among the studies. In other words, these studies were very different from each other in terms of their findings. Machtmes and Asher found the same phenomenon: There exists tremendous variation in the outcomes of distance education and face-to-face education. The overall effect size of the 19 studies comparing distance and traditional classroom instruction for adults they analyzed was -0.0093, with a range of -0.005 to +1.50. They found that “considerable heterogeneity was indicated (H = 47.927, df = 18, p = 0.0002)” (p. 36).

The considerable heterogeneity in the studies clearly indicates that there is indeed significant difference in learning outcomes of distance and face-to-face education. Individually many studies found significant differences between distance and face-to-face education, some favoring distance education and others face-to-face education. In fact, contrary to Russell’s claim, it is rarely the case that the individual studies included in the meta-analyses conducted by Cavanaugh (2001) and Machtmes and Asher (2000), which are ostensibly of higher quality than Russell’s, reported no significant difference between distance and face-to-face instruction. However, the difference disappears when the studies are considered as a whole.

The significant heterogeneity of achievement begs the question: Why did some studies find distance education students had better achievement than their counterparts in traditional classrooms and some find the opposite? To answer this question, we need to further examine the characteristics of each individual study. We know that distance education programs vary a great deal in content, learner characteristics, instructor characteristics, and delivery method. By examining these variables and the degree to which they influ­ence learning outcomes, we may be able to arrive at what distance education research is encouraged to do: find useful guidance for practice and research.

Machtmes and Asher (2000) made an attempt to do so as part of their meta-analysis of the effectiveness of telecourses in distance education. They coded 23 contextual variables to describe the qualities of each study. These variables were grouped into two categories of features of the studies: in­structional features (e.g., course type, type of delivery equipment, instruc­tor’s experience with delivery method) and methodological features (e.g., decade of study, research design type, and methods of assessing achieve­ment). Each of these features was examined to see whether and to what extent it could explain the heterogeneity in the effect sizes. Among other things, Machtmes and Asher found that studies that employed two-way in­teraction technology were “the only type [of delivery] that had a positive effect size” (p. 38) and that “learner achievement was influenced both by the type of course offered and by the type of learning environment” (p. 40). Another interesting finding is that the time when the study was conducted has a large impact on learner achievement. Similarly, although Cavanaugh (2001) did not attempt deliberately to explain the heterogeneity found in her meta-analysis, her grouping the studies into different content areas did sug­gest that some groups of studies seem to have larger effect sizes than others, suggesting that some content may be more suitable for distance instruction.

Machtmes and Asher’s study suggests a promising way to make use of the distance education literature, but it has a number of significant limitations. First, it has an extremely small sample. With only 19 studies from 13 pub­lications that span 30 years (from 1963 to 1993), the power of the study is extremely limited. Second, the study is limited to video-based/televised distance programs, whereas in recent years distance education has em­ployed many other technologies, including computer conferencing, the World Wide Web, and CD-ROMs. “In order to identify which features impact student learning,” they suggest, “researchers need to systematically identify and evaluate the technological and instructional features of all de­livery systems” (p. 42). Third, the coding of the variables in the study is all categorical, whereas we know that some of the features vary on a contin­uum. For example, the availability of the instructor is not a simple yes or no, because some distance education programs may have instructors available more frequently than others. Thus treating it as a continuous variable more accurately reflects the reality. The last but perhaps most significant limita­tion is the lack of a well-developed framework for identifying possible fea­tures that may contribute to learner achievement.

The purpose of the present study is in some way a continuation of Mach­tmes and Asher’s, but with more emphasis on systematically examining how different features of previous studies of distance education affect learning outcomes so as to inform future practice and research. To avoid the limi­tations of previous synthesis studies, we examined a much larger body of research, applied more sophisticated statistical procedures, and developed a more systematic analytical framework. In the remainder of this article, we describe the methods, findings, and implications of the present study.



Studies included in the research synthesis were identified through a three-step process. First, we conducted a thorough search for all studies included in the Education Resources Information Center (1966-2002) through FirstSearch with the following keywords: distan* and education, distan* and learning, distan* and teaching, distan* and instruction, online and education, online and learning, online and teaching, online and instruction, online and education, online and learning, online and teaching, online and instruction, web-based and education, web-based and learning, web-based and teaching, web-based and instruction, virtual and education, vir­tual and learning, virtual and teaching, virtual and instruction. The search identified 8,840 potentially relevant articles. Citation information for all 8,840 articles was then transferred into EndNote (version 5.0; ISI ResearchSoft, 2001) to build the first database.

At the second stage, the database was further examined based on the following criteria:

1. The article had to be published in a journal. The decision to include only journal articles was based on the concern of study quality. Previous research reviews on distance education had pointed out the low-quality problem of most studies. We believed that journal articles were of higher quality because of peer review procedures. Only including journal articles may result in publication bias, but we believed that the risk was minimal, as there had not been a dominant paradigm for distance education over the years to cause a certain bias against or for positive, negative, or nonsignificant findings.

2. The article must have had complete reference information (author, date, source, etc.).

3. The article had to include at least one evaluation study of distance education. The specific outcome measured was not limited.

4. The article must have had at least one comparison study on distance education and face-to-face education. Studies in which students’ own pretreatment scores served as controls for their posttreatment scores and those in which one distance course was compared with another distance course were excluded.

5. The article must have had some empirical data about the learning outcomes. Articles were not included if they merely describe a distance education course.

6. The article had to include enough statistical information for computing an effect size. The specific information we were looking for was mean, standard deviation, and sample size for both the distance education group and the face-to-face group, or t value, F value and degree of freedom (df).

A total of 1,100 articles that either were not journal articles or didn’t have complete reference information were removed from the database. Then the research team read the abstracts of the remaining 7,740 articles, 6,365 of which were removed because they didn’t meet criterion 3 or 4. Articles on which a clear decision could not be reached at this stage were kept in the database to be dealt with at a later stage. A total of 1,375 references were left after this selection.

At the third stage, the research team collected and read the 1,375 articles and excluded those that didn’t have empirical data. As a result, 421 articles were left after this elimination. The research team then read and coded these 421 articles and found that only 49 articles contained sufficient in­formation for calculating the effect size. For fear of missing some articles that might actually have had the information because of the large number of articles and complexity of the database, the research team examined the 421 articles once more and identified 2 more articles that had complete information for calculating the effect size. Ancestry search was conducted, but no extra articles that met all the criteria were found. Thus, 51 journal articles were included for the analysis.


To identify those methodological and substantive characteristics that may be responsible for significant variations in the findings, a detailed analytical framework was developed through an iterative process. The framework was developed based on our understanding of possible sources of variation in the studies. Heterogeneity in outcomes across studies can come from three sources: the publication, the study, and the instruction. Figure 1 depicts the logic model underlying the analytical framework.

For publication and study features, we started with Stock’s (1994) seven categories for describing research reports: report identification, the setting of the study, participants, methodology, treatment characteristics, statistical outcomes or effect sizes, and coding process. Instructional features refer to the characteristics of the distance education program under study. It has been argued that distance education should be considered as education at a distance (Shale, 1990):

In sum, distance education ought to be regarded as education at a distance. All of what constitutes the process of education when teacher and student are able to meet face-to-face also constitutes the process of education when teacher and student are physically separated. (p. 334)

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In other words, the quality of distance education programs is influenced by the same set of factors that affect the quality of face-to-face education. Schwab (1983) characterizes education in terms of four common places of education: teacher, student, what is taught, and milieux of teaching-learn­ing. This characterization is also applicable to the study of distance edu­cation. Whereas teacher, student, and what is taught remain pretty much the same as in face-to-face education, the milieux of teaching-learning are different between distance and face-to-face education in that the milieux of teaching-learning of distance education are mostly mediated through some kind of technology. Hence we describe the milieux of teaching-learning in terms of the format and method of delivery. Finally, we used the grounded theory (Glaser, 1992; Glaser & Strauss, 1967) to guide the development of the framework. Following a typical constant-comparison process, we started coding the studies with the initial framework and then modified the frame­work when we came across new features during the coding process. Table 1 summarizes the variables included in the final framework. In the following paragraphs, we describe these variables and reasons for their inclusion in the framework.

Evidence of Effectiveness

There are different ways to measure the effectiveness of distance education programs. Studies in distance education thus differ in what they used as evidence of effectiveness and the reliability and validity of the evidence used. The variation in what was measured and the quality of the measure­ment may explain the heterogeneity of outcomes.

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Outcome measures

Information about what has been used to assess the effectiveness of distance programs was collected for each study. A study could use one or more of the following measures: grades, quizzes, independent/standardized tests, student satisfaction, instructor satisfaction, dropout rate, student evaluation of learning, student evaluation of course, and external evaluation. Grades usually are the final scores students re­ceived for the class. Student evaluation of learning is students’ perception of how much they learned from the course, which can be significantly different from the grades they received.

Source of instrument

The source of instruments used to measure effec­tiveness can affect the final outcomes in that instruments from different sources may have different levels of reliability and validity. We identified four sources of the most frequently used measures: commercial testing agencies, the researcher (the author of the article), the instructor of the course, and publishers of textbooks that include assessment items.

Study design

The design of a study is a good indication of its quality and thus the quality of the results. We were curious about whether a certain type of design is associated with the study results; thus we coded the studies into two categories: true experimental or quasi-experimental. The differentiat­ing characteristic between the two designs is whether random sampling method was used.

Study results

To calculate effect sizes, the results of each study were recorded in the database. The study results include means, standard deviations, t values, F values, and r values, depending on what is reported in the primary study.


Factors that may affect the effectiveness of a distance education program can be categorized into two groups: publication features and instructional features. Two publication features were identified as possible factors affect­ing effectiveness: publication year and instructor as author.

Publication year

Previous research found that the time when a study was conducted had a significant correlation with the reported effectiveness (Machtmes & Asher, 2000). The assumption was that technology used to deliver distance education had changed dramatically over the years and that newer technologies seemed to have more capacity to deliver richer and more powerful learning experiences. To verify this hypothesis, we coded the year when a study was published for each article.

Instructor as author

All studies are based on advocacy (Begg, 1994). We hypothesize that if the instructor of the distance learning course was also the author of the publication, the result could be more likely to favor distance learning. To verify this hypothesis, we coded whether the author of an article was also the instructor of the distance education program under study.

As mentioned before we used Schwab’s four common places of education to guide the identification of instructional features that could potentially affect the effectiveness of distance education programs: the teacher, the student, the curriculum, and the milieux. In each of the four common places were a number of potential factors that could contribute to the out­comes of learning.

Teacher: Instructor involvement

The extent to which the instructor of a distance education course is involved in the actual delivery of the content and available for interactions with students during and outside the class sessions is termed “instructor involvement.” The level of instructor in­volvement is perhaps one of the most defining differences between tradi­tional face-to-face education and distance education. In face-to-face education, the instructor generally delivers the content live and interacts with students both in and outside class meetings, whereas in distance ed­ucation programs the level of instructor involvement varies a great deal, from one extreme where the content is preprogrammed and delivered through some technology means without the actual involvement of an in­structor to another where the instructor actually delivers the content live and is available for interactions with students in very much the same fashion as face-to-face education. Interactions between the teacher and students have been found to affect the quality of student experiences and learning outcomes in distance education (Institute for Higher Education Policy, 2000). How content is delivered should also have an effect on student learning experiences and outcomes. This is also a hot topic for distance education programs because one of the appeals of distance education is the potential to increase efficiency by reducing the demand of actual involve­ment of faculty. Having one faculty member teaching thousands of students (or even more) with the help of broadcasting, recording, and computing technologies has been a dream for many advocates of distance education. However, if a higher level of instructor involvement becomes a requirement for effective programs, the efficiency dream may never be realized. On the other hand, if the level of instructor involvement is found to be irrelevant to student outcomes, it would be unnecessary to assign an instructor to only a small group of students or have him or her be actually involved in the teaching. A preprogrammed video or computer program can accomplish as much. To assess the value of instructor involvement, we hence included this factor. We coded instructor involvement on a scale from 1 (no human in­volvement; e.g., computer-based training) to 10 (full involvement of a human instructor; e.g., two-way interactive TV courses).

Teacher: Status of the instructor

Another way distance education programs have sought to increase efficiency is to employ nonregular faculty, who nor­mally cost less than regular faculty. Thus we were interested in whether instructor status influenced student learning in distance education programs.

Teacher: Training for teaching distance courses

It has been argued that in­structors of distance education programs should be trained first, because distance education is a different teaching environment from face-to-face classrooms. Again, the training has cost implications for programs. To test whether training affects student learning, we collected information about teacher training from each study, if that information was available.

Student: Education level

We collected information about students’ educa­tional attainment level before attending the distance course to examine whether, and if so at which level, certain types of students were more pre­pared to take distance education courses.

What is being taught: Content area

Some content may be more suited for distance education, whereas other content may be better taught in a face-to-face course. Interested in whether this assumption was true and if so, what content area was better suited for distance education, we collected infor­mation about the content area of each study. A course could be categorized as teaching one of the following subject areas: social science, mathematics, science, medical science, literacy, humanities, business, law, engineering, computer science, teacher education, and skills. (Skills here represented any professional training that didn’t fall into other categories.) We coded medical science, business education, and teacher education separately be­cause they had been among the most commonly taught content areas in distance education.

The milieux: Instructional level

Distance education programs have been traditionally intended for adults, but recently distance education has ex­panded to include younger audiences. As a related factor to student char­acteristics and content, the instructional level of distance education may be associated with its effectiveness. We grouped the distance education pro­grams in each study into nine levels: Grades K-2 (lower elementary), Grades 3-5 (upper elementary), Grades 6-9 (middle school), Grades 10-12 (high school), associate’s degree (community college), undergraduate level (4-year college), graduate level, professional development, and military training. We also collected data about whether the course was for credit or not and whether it was for a degree-granting program or not.

The milieux: Interaction type

Interaction type characterizes how instruc­tors and students interact in the distance learning process. There are four types of interaction: asynchronous, in which a time lag exists between the interactions of the instructor and students in that students may ask a ques­tion via e-mail, to which the instructor may respond, for example, 2 days later; synchronous, where the potential exists for instructors and students to interact at the same time; noninteractive, where there is no interaction between instructors and students at all; and both synchronous and asyn­chronous, where the instructor can interact with students both synchro­nously and asynchronously.

The milieux: Media involvement

Distance education programs also vary in the level of technology used. Some programs employ a mixed model, in which part of the instruction is conducted face-to-face whereas some others are delivered via technology. Proponents of the mixed model suggest that some face-to-face contact is necessary or desirable to maintain student mo­tivation and thus a higher quality of education. We were interested in test­ing this hypothesis. Thus we coded each study’s level of media involvement, which was defined as the extent to which a certain instructional delivery system has been mediated by technologies, that is, how frequently technol­ogy is used in a program. Media involvement is coded on a scale from 1 (no technology was used) to 10 (instruction was delivered completely with technology).


Information from complete articles selected for inclusion was coded by the two researchers who were most involved in the development of the frame­work and rubric. One coded 25 articles and the other coded 26 articles independently. When there was any uncertainty, a third researcher was in­volved, and an agreement would be reached through discussion. After both researchers completed the coding, 10 articles were randomly selected to test interrater reliability. Both coders coded these 10 articles, and they reached an agreement of 98.3%. Disagreements were solved through discussion.


Effect Size Computation

Effect size is a measure of standardized mean difference between two groups. In this study, effect size was computed to estimate the extent of the difference between online learning and face-to-face learning. Depending on the information available, we used different strategies to compute the effect size for each study. When information on mean and standard deviation for both control and experimental groups was available, effect size was com­puted by subtracting the control group mean (face-to-face education) from the experimental group (distance education) mean and dividing the differ­ence by their pooled standard deviation.2 When information on means and standard deviations was not available and only t values were reported, the effect size was computed based on t value and degrees of freedom.3 When only the F values and sample sizes were reported, and there were only two groups, the effect size was computed based on the F value and sample sizes (see Rosenthal, 1991).4 Positive effect sizes indicate that distance education has a better outcome than face-to-face education and vice versa.

Correcting Sample Size Bias

One statistical principle is that studies with larger within-study sample sizes will give more accurate estimates of population parameters than studies with smaller sample sizes (Shadish & Haddock, 1994). In meta-analysis, effect sizes are biased in studies with smaller sample sizes. Hence, large-sample studies should be weighted more than studies with smaller sample sizes. In this study, all effect sizes were corrected for potential bias intro­duced by different sample sizes.5

Multiple Outcomes

In some cases, one primary study has more than one outcome. Cooper (1998) pointed out that multiple results could happen for two reasons. First, more than one measure of the same construct might have been employed and each measure analyzed separately. Second, different samples of the same population might be used in the same study and their data analyzed separately. Additionally, different times of measurement can also result in multiple outcomes (Lipsey, 1994, p. 112). These multiple results from the same study can be problematic for meta-analysis because the separate estimates in the same study are not completely independent: They share historical and situational influences, and some of them even share influ­ences contributed by having been collected from the same people (Cooper, 1998). Becker (2000) proposed several ways to address the problem of multiple outcomes. Based on her suggestion, we addressed the multiple-outcomes problem in a number of different ways, depending on the specifics of the study. First, an average effect size was derived when the same construct with the same sample was measured with more than one instru­ment, the same construct was measured by more than one subconstructs, or the same data were analyzed using different statistical methods. Second,

one “best” effect size was chosen in the following situations: (a) If the pri­mary study provided comparisons of control groups and experimental groups and comparisons of subsamples, such as at different grade levels, then only the comparison of the whole-sample groups was used to compute the effect size, and (b) If the same construct with the same sample was measured at different time points, one effect size was calculated based on data from the final time point (the end of the course), since we were com­paring the effectiveness of the whole course. However, when different con­structs were measured, or the same construct was measured on different samples, the effect size for each construct or sample was calculated and included in this study.

The analysis resulted in 99 effect sizes. To examine the impact of extreme values on the data set, outlier analyses were conducted using standard re­sidual procedure and three outliers with standardized residuals larger than ± 2.0 were identified. Further examination of the outliers found that two of them were due to computational error, and these were then corrected. There was no computational error for the other outlier, which was larger than 3 (3.08). This effect size was deleted from the data set. Consequently, 98 effect sizes from 51 studies on 11,477 participants (8,660 independent participants) were left. The articles and their study features are listed in Appendix A. Strictly speaking, since there were still multiple outcomes from the same studies, the overall Q—the amount of total variance—of the effect sizes could be problematic. However, in later analyses, these effect sizes were categorized into different groups and analyzed separately, thus in effect, the multiple-outcomes problem should not have affected the final results.


We examined the representativeness of our data set by checking its distri­bution and publication bias. The stem-and-leaf plot (Figure 2) demonstrates the distribution of our data set. Statistically, if there is no publication bias, the plot should be symmetric and normally distributed. As shown in Figure 2, the typical values in the stems corresponding to values of d in a range falling between 0.01 and 0.09. The distribution was symmetric and seems to have more negative values. There was no gap in this distribution, indicating no atyptical observations. These features of this stem-and-leaf plot suggest that the effect sizes in this data set were normally distributed.

Publication bias is the tendency on the parts of investigators, reviewers, and editors to submit or accept manuscripts for publication based on the direction or strength of the study findings (Dickersin, 1990). Publication bias has been one of the major concerns in meta-analysis, because a data set with publication bias will lead to biased conclusions (Begg, 1994). Since publication bias is inversely related to sample size (Begg, 1994), to examine publication bias of the data set in this study, a funnel plot (Figure 3) was generated. A funnel plot is a graph of sample size versus effect size. If there is no publication bias, the distribution should resemble an inverted funnel. This plot is symmetric and looks like a funnel, so we assumed that there was no publication bias in our data set.

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The stem-and-leaf plot and publication bias plot showed that the effect sizes in our data set were normally distributed and there was no publication bias. Hence the primary studies included in this study can be said to be representative of the literature in distance education.


We tested the homogeneity of the effect sizes with the homogeneity analysis (Cooper & Hedges, 1994). The homogeneity analysis compares the amount of variance in an observed set of effect sizes with the amount of variance that would be expected from sampling error alone (Cooper, Valentine, Charlton, & Melson, 2003). If the total variance (Qt) is greater than the critical value, considerable variation among the effect sizes exists.

The significance of each factor was tested using two homogeneity tests: a between-group homogeneity test and a within-group homogeneity test. The between-group homogeneity test analyzes the homogeneity of effect sizes across groups. If the between-group variance (Qb) is greater than the critical value, it indicates that there is significant difference among the groups of effect sizes. In other words, the factor is a significant predictor of the effect size difference among different groups. The within-group ho­mogeneity test examines the homogeneity of effect sizes within the groups. If the within-group variance (Qw) is greater than the critical value, it in­dicates that the effect sizes within each group are heterogeneous. If the between-group homogeneity test showed the factor was a significant pre­dictor, further analysis was conducted to calculate average effect size and the significant level of each group. The average effect sizes were calculated through univariate analysis of variance using weighted procedures. The effect sizes were weighted by multiplying each independent effect size by the inverse of its variance. The significance level p was calculated through the Z score of the average effect size in each group. We defined a mean effect size as significant when the p value was smaller than .05. The 95% confidence interval of the average effect size also indicated whether it was significant: If the 95% confidence interval included zero, the difference was not significant, and if the 95% confidence interval didn’t include zero, the difference was considered significantly positive or negative, depending on the sign of the mean value.

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We analyzed the effect sizes using both the fixed-effect model6 and the random-effect model.7 But since most of the results from both models are similar, we only present the results from the fixed-effects model in the text and include the results from the random-effects model in Appendix B. The analysis was conducted mainly with Statistical Analysis System (SAS Institute Inc., 1997), and a few descriptive analyses and graphs were generated with the Statistical Package for the Social Sciences (SPSS Inc., 2003).


In this section we first present our overall finding, followed by results about each of the factors that showed a significant effect on the difference between distance education and face-to-face education.


The overall weighted mean effect size8 between distance education and face-to-face education was 10.10, with a 95% confidence interval of [ — 0.01 0.22] (z = 1.76, p>.05, SD = .06). This finding suggests that when considered as a whole, the studies suggest that there is no significant difference between distance education and face-to-face education, confirming the “no significant difference” claim of previous researchers.

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However, a closer look at the data revealed considerable variation among the effect sizes: There is a wide range of effect sizes (from - 1.43 to 1.48); about two thirds of the studies show that distance education produced bet­ter student outcomes than face-to-face education, whereas the remaining third showed just the opposite. The heterogeneous nature of the effect sizes is clearly shown in the confidence interval plot (Figure 4).9 A homogeneity test was conducted and the result, Q(97) = 484.58, p<.001, further con­firms the heterogeneous nature of the data set, which means that the effect sizes varied greatly across the studies.


The homogeneity analysis of the data set shows that there is a big variation among the effect sizes. In other words, these studies are very different in terms of their findings. As mentioned previously, the primary goal of this study was to identify factors that may explain why some studies found distance education programs yielded more positive outcomes than their face-to-face counterparts and vice versa. To accomplish this goal, in the following section, we examine how different factors identified in the liter­ature influence the effectiveness of distance education. Table 2 shows all the factors we have identified and the variations they could explain.

In the following section, we present detailed results of the moderator analyses. In the tables included in this section, k is the number of independent samples. The d statistic is an effect size indicator that represents the standardized mean difference between distance education and face-to-face education. The 95% confidence interval of d is also presented together with d. The significance level p is reported when d is significant.

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Publication Year

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As Table 3 suggests, when the study was published has been found to be related to the outcomes of distance and face-to-face education (Machtmes & Asher, 2000). Our analyses confirmed this finding. The year of publication is indeed a significant moderator (Qb = 24.40, p<.0001). The year 1998 seems to be an especially important dividing time. Studies published before 1998 did not seem to find a significant difference between distance edu­cation and face-to-face education, whereas studies published in and after 1998 found distance education to be significantly more effective than face-to-face education (d = 0.20, p<.001).

Whether the Instructor Is the Author

As shown in Table 4, whether the author is the instructor of the distance learning course under study makes a significant difference in the effective­ness of distance education compared to face-to-face education (Qb = 17.12, p<.001). When the author of the article is also the instructor of the distance education course studied, the outcome seems to significantly favor distance education (d = 0.33, p<.001); when the author is not the instructor, no significant difference between distance education and face-to-face education is reported; and when the status of author is unknown, distance education is found to be more effective than face-to-face education, but the overall effect size is smaller than in studies whose author is clearly the instructor (d = 0.18, p<.001).

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Results from the random-effect model suggest a similar situation. The only difference is that when the researchers’ status is unknown, studies demonstrate no significant difference between distance education and face-to-face education. The results from both models indicate a possible bias favoring distance education when the author is the instructor of the distance education course.

Outcome Measures

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What is measured or what are used as outcome measures can also have a significant effect on the difference between distance education and face-to-face education (see Table 5). When grades (including quizzes), student attitude and beliefs, student satisfaction, and student participation are measured, or when the outcome is based on researchers’ observation, distance learning shows a significantly better outcome than face-to-face learning. When outcome is measured as student evaluation of learning or metacognition, face-to-face education is slightly better than distance learning, but the difference is not significant.

Instructor Involvement

Instructor involvement was the most significant moderator among all the identified factors (Qb = 118.726, p<.0001). There were significant differ­ences among studies with different instructor involvement levels. For ex­ample, the mean effect sizes of studies at levels 2, 5, 8, and 9 were - 0.07, 0.43 (p<.001), 0.47 (p<.001), and 0.37 (p<.001), respectively. These differences suggest a general trend that when instructor involvement is low, the outcomes of distance education are not as positive as those of face-to-face education; when instructor involvement increases, distance education programs yield more positive outcomes than face-to-face education. How­ever, when instructor involvement reaches the highest level, the difference tends to decrease. To further examine this trend, we recoded the variable Instructor Involvement into three levels: low (2, 3, 4), medium (5, 6, 7), and high (8, 9, 10). As shown in Table 6, when instructor involvement is low, face-to-face education is significantly more effective than online education (d = -0.24, p<.001); when instructor involvement is at the medium level, distance education seems to fare better, and the difference is the largest among these three groups (d = 0.29, p<.001); and when instructor in­volvement is high, distance learning still shows a significantly better effect than face-to-face education, but the difference is not as large as at the me­dium level (d = 0.21, p<.001).

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Student Education Level

Student prior education level is another significant predictor (Qb = 26.81, p<.001) of the difference between distance education and face-to-face ed­ucation. As shown in Table 7, distance education shows significantly better outcomes than face-to-face education with students who have a high school diploma (d = 0.25, p<.001). But for students with a college degree, the difference between distance education and face-to-face education seems insignificant (d = 0.06, p>.05).

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Content Area

What is being taught is also closely related to whether distance education performs better than its face-to-face counterpart. Our analyses suggest that content area is a significant predictor of the difference between distance education and face-to-face education (Qb = 79.08, p<.0001). As shown in Table 8, studies of distance education programs in business, computer sci­ence, and medical science found distance learning to be more effective than face-to-face education. In social science and science areas, there is no sig­nificant difference between distance learning and face-to-face learning, al­though face-to-face learning shows a slightly better effect than distance learning. In military, mathematics and specific skills, distance education has a slightly better effect than face-to-face education. Given the small numbers (k) of studies in these three areas, it is difficult to draw any firm conclusion. Under the random-effect model, the results demonstrate a similar trend, but the differences in effectiveness between these two learning environ­ments are not significant in almost all of the content areas except in com­puter science, which shows a significant difference between distance learning and face-to-face learning (d = 0.50, p<.01).

Level of Instruction

The level of instruction was found to be a significant predictor of the dif­ference between distance learning and traditional learning (Qb = 73.12, p<.0001). As shown in Table 9, in multiple settings, face-to-face learning is more effective than distance learning (d = -0.16, p<.01). However, dis­tance education programs at the undergraduate level and in military set­tings were more effective than face-to-face learning (d = 0.36, p<.001; d = 0.23, p<.01, respectively). There was no significant difference in other settings. Under the random-effect model, distance learning showed signif­icantly better outcomes than face-to-face learning at undergraduate level (d = 0.35, p<.001). In all the other settings, there was no significant dif­ference between face-to-face and distance education. The finding that dis­tance programs at the undergraduate level yield more positive learning outcomes than face-to-face programs is closely related to the previous find­ing that students with high school diplomas seem to benefit more from distance education than students with other prior education levels.

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Type of Interaction

The type of interactions, that is, how interactions between the instructor and the students and among students were realized, is a significant pre­dictor of the difference between distance learning and traditional learning (Qb = 14.21, p<.0001). As Table 10 shows, studies of distance programs that employed both synchronous and asynchronous means of interaction found distance education to be significantly better than face-to-face education (d = .22, p<.001). Although there are two studies that allowed no interac­tion that found distance education to be more effective than face-to-face education, the small sample size makes the finding questionable. The mean effect size of these two studies is significant under the fixed-effect model (d = .49, p<.01), but not significant under the random-effect model (d = .55±.83, p>.05).

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Media Involvement

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The level of media involvement is another significant factor that seems to distinguish the studies in terms of the learning outcomes, Qb(96) = 55.07, p<.0001). As shown in Table 11, studies with a coded media involvement of 60-80% reported distance education to be significantly more effective than face-to-face education (d = 0.50, p<.001). Studies with a 90-100% media involvement also found results favoring distance education, but the differ­ence is much smaller (d = .07, p<.001). In essence, this finding suggests that distance education mixed with a certain amount of face-to-face in­struction seems most effective.

Insignificant Factors

We also examined the effect of other factors but did not find significant effects. These factors include whether the instructor was trained for offer­ing distance education, whether the students took the course for credit, and whether it was a degree course. The results (see Table 2) suggest that these factors did not differentiate studies with results favoring distance education from those favoring face-to-face education.


The purpose of this study was not to seek evidence of effectiveness of dis­tance education since, with or without scientific evidence, distance educa­tion has already evolved from a marginal form of education to a commonly accepted and increasingly popular alternative to traditional face-to-face ed­ucation (McIsaac & Gunawardena, 1996; National Center for Education Statistics, 2003; United States General Accounting Office, 2002). Instead, the study was motivated by the pressing need for practical guidance for improving distance education and the dismissive criticism of the immense body of literature in distance education. Rather than waiting for new and improved research and sound theoretical frameworks suggested by some scholars (Gunawardena & McIsaac, 2004; Lockee et al., 1999; McIsaac & Gunawardena, 1996; Institute for Higher Education Policy, 1999), which have not come out as quickly as expected and may also be deemed outdated and inadequate by some critics as soon as they become available as a result of the rapid changes in distance education technologies, we took a prag­matic approach. We went back to what we already have: the existing lit­erature. We wanted to find out what guidance, if any at all, this literature might have to offer besides the already well-known no-significant-difference conclusion. After searching through thousands of publications, we found a small set of studies that provided sufficient information for secondary anal­ysis. These studies and our analyses seem to support seven findings.


Although the aggregated data of all available studies show no significant difference in outcomes between distance education and face-to-face edu­cation, there is remarkable difference across the studies. Some studies found distance education to be significantly more effective than face-to-face, whereas others found the opposite to be true. This finding is consistent with previous research and supports the popular impression of distance edu­cation, in that distance education as a form of education is as good (or as bad) as face-to-face education. It highlights, however, an important and often neglected fact about distance education literature: Distance education programs, just like traditional education programs, vary a great deal in their outcomes. Thus it is advisable not to automatically apply the “no-significant-difference” label to all distance education programs just because the positive findings of some studies cancel out the negative findings of other studies.


Whether and how much students interact with peers and instructors seems to be a differentiating quality of distance programs in terms of learning outcomes. Our analysis found three interaction-related factors that tend to distinguish studies with outcomes favoring distance education from those favoring face-to-face education: instructor involvement, media involve­ment, and types of interactions. The extent to which the instructor is in­volved in the actual delivery of the content directly influences opportunities for students to interact with the instructor. The study found that studies with higher instructor involvement tend to favor distance education over its face-to-face counterpart. Whether a distance course included a face-to-face component, which presumably increased the opportunity for interaction, was also found to set studies favoring distance education apart from those favoring face-to-face education. Perhaps the most compelling and direct evidence is the distinguishing power of the types of interactions. The study clearly shows that studies of distance programs with both synchronous and asynchronous interactions reported more positive outcomes than those with only one type of interaction.

Interaction among students and between instructors and students is the hallmark of education. But distance education has traditionally been at a disadvantage in this aspect. Either because of the limitation of technology or because of cost, distance education programs, until recently, have not been able to offer the full range of communication channels to students and instructors. With the advent of more cost-effective and efficient communi­cation technologies such as the Internet, distance education programs have started to provide both synchronous and asynchronous communications that enable a broad range of interactions between students and instructors and among students. Recent distance education programs that took ad­vantage of these tools have reported positive outcomes (for example, see Institute for Higher Education Policy, 2000; Levin, Levin, & Chandler, 2001; Twigg, 2001).

There are, however, nontechnological costs associated with offering both synchronous and asynchronous interactions. The first is that someone needs to coordinate and manage the interactions. It is difficult to imagine that the provision of technological capacity for interaction alone automat­ically leads to meaningful interactions. The instructor needs to be present to answer student questions and facilitate discussions. Second, someone needs to maintain the infrastructure for both synchronous and asynchronous communications. Although there are plenty of communication tools avail­able, they need to be maintained and updated. Third, both students and instructors need training and help with these tools. In traditional distance education, delivery is the main concern. The instructor and students may not need to know how to actually operate the tools because there is a tech­nician to handle the transmission and reception of content. In interactive distance education, both instructors and students need to use the commu­nication tools, which are often computer software unfamiliar to instructors and students. Thus training and support become necessary.


Distance education comes in all forms and shapes. Some programs are Web-based, computerized instructional courses that do not have any involvement of a “live” instructor. Students simply interact with the computer. Some programs take the form of broadcasting prerecorded videos of instruction, and these do not have instructor involvement either. Still there are programs that take the shape of correspondence courses, but with limited e-mail communication with a “live” instructor, who may have to supervise hundreds of students. There are also programs that are just like traditional face-to-face education, in that there are scheduled class meetings and out-of-class office hours, except that these meetings take place in an online environment. Our findings suggest that the presence of a “live” instructor is important for effective distance education. We found that the degree of instructor involvement is a significant distinguishing quality of effective and ineffective distance education programs. Although this finding may be dis­appointing to those who think distance education may be more efficient, in terms of personnel cost, we want to emphasize that, based on previous research, “live” human instructors are still needed to ensure quality dis­tance education.


Today’s distance education inevitably involves the use of some kind of technology, be it interactive television or the Internet. In fact one of the defining characteristics of distance education is its use of technology to remove the distance between the provider and recipient of instruction. Our analysis suggests that those studies that used a combination of technology and face-to-face education resulted in the most positive outcomes.

Recent research supports a hybrid model of distance education that combines both a face-to-face component and a technology-mediated dis­tance component. For instance, in reviewing and projecting the use and influence of information technology on learning and teaching, Lant (2002) expressed the necessity of treating online technologies and traditional learning as complementary to each other. Although it may not be possible for all distance programs to include a face-to-face component, there are tools (e.g., video conferencing) that can effectively remove the distance and create effective social organizations (Levin et al., 2001).


The nature of what is being taught, that is, the content area or the cur­riculum, seems to affect the effectiveness of distance education as well. Studies of distance courses in computer science, for example, reported more positive results than other content areas. It was also found that studies of college-level courses had results that favor distance education over face-to-face, while studies of graduate level programs found less positive results. Typically college level courses differ from graduate level courses in terms of content and desirable outcomes. This suggests, although more studies are needed to confirm, that distance education may be more effective in teach­ing some content than others.

Another related finding is that studies of programs whose students had high school diplomas found distance education to be significantly more effective than face-to-face education, while studies of other students did not find such strong effect. One possible explanation of this finding has to do the content of the courses. We can assume that most of the courses that the students with a high school diploma take are college level courses, and those taken by students with a college degree are graduate level courses. Relatively speaking, college level courses could have more of a focus on knowledge and skill acquisition, while graduate level courses focus more on idea or research interest development. It is possible that knowledge and skills can be taught more effectively in distance education, but the devel­opment of an idea or research interest may need more discussion and interactions with the instructor and other students. In other words, the advantage of distance education in delivering learning content in college level courses may not hold for graduate level courses where more complex ideas are explored.


The finding that some learners may be more able to take advantage of distance education than others is not new. A number of studies (e.g., In­stitute for Higher Education Policy, 1999) have suggested that students with certain qualities seem to benefit more from distance education. In the studies we analyzed, there was not sufficient information about learner characteristics to examine how individual characteristics affect learning outcomes of distance education. We were only able to conduct analysis of prior education level, which may interact with many other learner char­acteristics, such as gender, study habits, learning styles, learning environ­ment, access to resources, experiences with distance learning, and technology proficiency. Thus we are not able to draw any conclusion. However, the finding that a high school diploma seems to distinguish ef­fective distance education programs from ineffective ones leads to the sug­gestion that more attention should be paid to learners in future studies.


The finding that studies prior to 1998 found distance education to be less effective than face-to-face education, whereas those post-1998 found the opposite, could be an indication that distance programs are getting better with more powerful delivery media and more sophisticated support systems. There were some significant changes in technology employed in distance education in the mid-1990s. The 1997-98 report produced by National Center for Education Statistics (1999) reported that among all higher education institutions offering any distance educa­tion, the percentages of institutions using two-way interactive video and one-way prerecorded video were essentially the same in 1997-98 as in 1995. However, with the dramatic growth of the World Wide Web, tech­nologies based on the Internet, such as e-mail, Web page, and Web board have increased rapidly since 1998. The change on technology employed in distance learning is likely to influence many aspects of distance education such as how learning materials are presented and how the teacher and students communicate and interact and hence to influence the effectiveness of distance education.

The difference could also be a result of the maturation of distance education programs. Over the years, distance education programs have become more mature, with an increased amount and variety of support for both instructors and students. For example, distance education programs are providing technical help through a variety of means, including toll-free phones, e-mail, real-time chat rooms, and online tutorials for technical as­sistance (Institute for Higher Education Policy, 2000; Levin et al., 2001). Meanwhile, the instructors are receiving more training and becoming more experienced with teaching online courses. Moreover, students are becom­ing more comfortable working with computers and learning online. All these factors can contribute to the increased effectiveness on distance education in recent years.

Another possible, but less likely, explanation is that there was a paradigm shift: Distance education has been accepted as an effective form of education, and thus only studies that report positive findings have been published.


There are frequent calls for new conceptual and theoretical framework for distance education research and practice (Gunawardena & McIsaac, 2004; Head, Lockee, & Oliver, 2002; Lock, 2002; McIsaac & Gunawardena, 1996). Although such exercises may be useful, they may be unnecessary. Distance education is in essence still education (Shale, 1990). Results from this study further support this argument. The factors found to have an impact on the effectiveness of distance education are also factors that would affect the effectiveness of face-to-face education. Additionally, the one factor that often sets distance education apart from face-to-face education—distance or the technology that is used to remove the effect of distance—is quickly disappearing as face-to-face distance education increasingly uses technology to support teaching and learning. In other words, the line be­tween distance education and face-to-face education is quickly being erased (McIsaac & Gunawardena, 1996).

When distance education is considered the same as face-to-face educa­tion, we are encouraged to consider the abundance of theoretical, analytical, and conceptual frameworks for understanding education. Schwab’s four common places—teacher, student, what is taught, and milieux of teaching-learning—can serve as a very useful overarching framework for studying and thinking about distance education, as suggested by the present study. There are other more detailed and specific theoretical frameworks that can help us understand the relationships and interactions among the variables located in the four common places.

Although it is desirable to have well-designed true random experimental and longitudinal studies that ask the right questions in distance education research, as expected by some scholars, such high-quality studies are hard to come by for a number of practical reasons (Cook & Campbell, 1979). This is the reality of research in social sciences in general and distance education research in particular. Thus we have to accept this reality and find novel approaches to understand existing research. The study reported here is an example of how to take a pragmatic approach to research syn­thesis in order to answer pressing and practical questions.

Finally, this study is by no measure conclusive because of a number of limitations. For example, the exclusion of publications from other sources, such as dissertation and conference proceedings, may have biased the findings. One of the primary purposes of this study is to test a new way of working with previous research. Although we believe the findings suggest that examining factors that moderate the effects of distance education can lead to the identification of practical guidance for practice and research, we also encourage future studies to include a more complete examination of all studies and integrate analysis of qualitative studies from the literature.

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The study was conducted as a project of the Center for Teaching and Technology in the College of Education at Michigan State University. The views expressed in the article do not necessarily represent those of the college or the university. We would like to thank Tom Bird, Richard Banghart, Mark Urban-Lurain, and Gary Cziko for sharing their insights on the issues of distance education and educational technology during formal and informal discussions. Ou (Lydia) Liu, Yun Fan, and Eduardo Junqueira Rodrigues assisted with data collection and coding. We would particularly like to thank Meng-jia Wu and Ken Frank for their help with the meta-analysis.


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Cite This Article as: Teachers College Record Volume 107 Number 8, 2005, p. 1836-1884
https://www.tcrecord.org ID Number: 12098, Date Accessed: 5/20/2022 12:13:35 AM

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About the Author
  • Yong Zhao
    Michigan State University
    E-mail Author
    YONG ZHAO is a professor in the Learning, Technology, and Culture Program in the College of Education at Michigan State University. His research interests include educational uses of technology, online/distance education, and innovation diffusions. His recent publications include “Technology Uses in Schools: An Ecological Perspective” (American Educational Research Journal, 2003, with K. Frank) and an edited volume, What Should Teachers Know about Technology: Perspectives and Practices (Information Age, 2003).
  • Jing Lei
    Michigan State University
    JING LEI is a doctoral candidate in the Learning, Technology, and Culture Program in the College of Education at Michigan State University. Her dissertation concerns conditions for effective technology use by students.
  • Bo Yan
    Michigan State University
    BO YAN is a doctoral student in the Learning, Technology, and Culture Program in the College of Education at Michigan State University.
  • Chun Lai
    Michigan State University
    CHUN LAI is a doctoral student in the Learning, Technology, and Culture Program in the College of Education at Michigan State University.
  • Sophia Tan
    Coastal Carolina University
    SOPHIA TAN is an assistant professor in the Professional Program in Teacher Education at Coastal Carolina University.
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