n = 204 military members
Self-paced, online training
Task value, self-efficacy, and prior experience with online learning were positive predictors of satisfaction, perceived learning, and intentions to enroll in future online courses.
Artino & Stephens
n = 96;
Asynchronous college courses
Task value and self-efficacy for learning and performance were positive predictors of cognitive and metacognitive learning strategies use.
Bell & Akroyd
n = 201 undergraduates
Asynchronous college courses
Grade point average (GPA), expectancy, and an interaction term (GPA x expectancy) were positive predictors of academic performance.
n = 73 undergraduates
Asynchronous college courses
Online technologies self-efficacy and cognitive style were poor predictors of academic performance.
n = 169 undergraduates
Asynchronous college courses
Task value was positively related to metacognition, but only self-efficacy was positively related to academic performance.
Joo, Bong, & Choi
n = 152 junior high school students
Web-based search task
Self-efficacy for self-regulated learning positively related to academic self-efficacy, cognitive strategy use, and Internet self-efficacy. Only academic and Internet self-efficacy were positively related to academic performance.
n = 69 undergraduates
Asynchronous college courses
Task value was a significant predictor of satisfaction, whereas self-efficacy was the only significant predictor of academic performance.
n = 235 graduates and undergraduates
Asynchronous college courses
Computer self-efficacy was a significant predictor of satisfaction and intentions to enroll in future online courses.
Lynch & Dembo
n = 94 undergraduates
Courses with both face-to-face and asynchronous components
Self-efficacy for learning and performance and verbal ability significantly predicted academic performance.
n = 30 undergraduates
Asynchronous college courses
Online technologies self-efficacy negatively predicted academic performance. Task value and mastery goal orientation significantly predicted satisfaction.
Wang & Newlin
n = 122 undergraduates
Synchronous and asynchronous college courses
Self-efficacy for online learning was positively correlated with academic performance.
Moreover, the researchers found that an interaction term (college grade point average [GPA] X self-efficacy) was also a significant individual predictor of final course grade (β = -2.35, p < .001), indicating that self-efficacy beliefs had a greater effect on course grade for students with lower GPAs.
Along with end-of-course grades, several investigators have used student satisfaction with online education as the outcome of interest since satisfaction has been shown to predict course drop-out rates, as well as students’ intentions to enroll in future online courses (for a review, see Dabbagh & Bannan-Ritland, 2005; Moore & Kearsley, 2005; Simonson et al., 2003). For instance, in a study of student satisfaction and choice behaviors, Lim (2001) surveyed 235 adult learners at five American universities and found that computer self-efficacy, along with a linear combination of experiential variables, explained 15% of the variance in students’ overall satisfaction and 12% of the variance in their intentions to enroll in future online courses. And while the effect sizes found in Lim’s (2001) study are considered moderate (Cohen, 1988), Artino (in press) found much larger effects when attempting to predict military students’ satisfaction (model R2 = .65) and choice behaviors (model R2 = .40) using a linear combination of students’ prior experience, task value, and self-efficacy within the context of self-paced, online training (i.e., computer-based courses accessed through the Internet and completed without an instructor). In this case, however, self-efficacy for learning with self-paced, online training emerged as a significant individual predictor of satisfaction but not choice behaviors. On the other hand, task value—defined as the extent to which students find a task interesting, important, and/or valuable (Eccles & Wigfield, 1995)—was the strongest individual predictor of both satisfaction and choice behaviors. With respect to choice behaviors, these results are consistent with research conducted in traditional classrooms by Eccles and Wigfield (1995), who have shown that value beliefs tend to be better predictors of intentions to take future courses, as well as actual enrollment in those courses, than expectancy beliefs (e.g., self-efficacy).
Other researchers have attempted to use task value as a predictor of adaptive academic outcomes in online settings. In general, results have shown task value to be positively related to students’ metacognition and use of learning strategies (Artino & Stephens, 2006; Hsu, 1997); overall satisfaction (Artino, in press; Lee, 2002); and future enrollment choices (Artino, in press). Unfortunately, only three of the studies reviewed (Artino, in press; Hsu, 1997; Lee, 2002) examined the extent to which task value related to academic performance and learning. In two of these studies (Hsu, 1997; Lee, 2002), the researchers failed to find a significant relationship between task value and course performance when self-efficacy was also included as a predictor. On the other hand, Artino (in press) found that task value was a strong individual predictor of students’ perceived learning (partial r2 = .28) when task value was included with self-efficacy and prior experience in a regression model (model R2 = .50). In this case, however, the use of a self-report measure of learning was a significant limitation, as this type of subjective measure may bear little resemblance to more direct, performance-oriented outcomes (Mabe & West, 1982; Pace, 1990).
Although investigators have given some attention to the relationships between several motivational components of self-regulation and various academic outcomes (e.g., satisfaction, academic performance, and choice behaviors), very little research has been conducted on how these motivational components relate to students’ academic behaviors, such as their use of cognitive and metacognitive learning strategies. Two exceptions are the studies conducted by Artino and Stephens (2006) and Joo et al. (2000). For example, using path analytic techniques, Joo et al. (2000) found that academic self-efficacy and self-efficacy for SRL both significantly and positively predicted students’ self-reported use of cognitive and metacognitive learning strategies. However, contrary to expectations, neither cognitive nor metacognitive strategies use was related significantly to performance outcomes. Thus, the researchers failed to confirm their hypothesis that learning strategies use mediates the relationship between self-efficacy and student performance. Based on these results, the authors questioned the usefulness of self-reports of strategy use. Furthermore, they recommended that future studies employ more direct, behavioral indicators of learning strategies use to help clarify how students’ motivational characteristics relate to their capacity to apply learning strategies in online environments.
In summary, findings from non-experimental, correlational studies seem to support results from research in traditional classrooms indicating that students’ motivational beliefs about a learning task are related to positive academic outcomes. The existing research in this area, however, suffers from several limitations. First and foremost, results are strictly correlational in nature; therefore, one cannot infer causality from the observed relationships. Although, overall, the results suggest moderate to strong relations between motivational components and adaptive outcomes, the direction of influence between the variables is sometimes ambiguous. For example, although many of the study designs imply that academic performance results, in part, from students’ motivational beliefs, these causal relations could be reversed. Hence, additional research is needed before the exact direction of operation of these social cognitive components can be fully understood.
Second, many of the studies reviewed have suggested that the performance outcomes employed suffered from range restriction, a significant issue in college courses where, often times, the majority of students receive a grade of either A or B. Range restriction has the effect of downwardly biasing the effect size (Cohen, Cohen, West, & Aiken, 2003). Therefore, the failure of several studies to find a significant relationship between motivational components of self-regulation and overall academic performance may have been exacerbated by the restricted range of the criterion measure. Considering this limitation, it is important that future studies utilize other measures of academic success, such as assessments of critical thinking skills and online engagement; outcomes than can be measured through content and discourse analysis of online discussion boards (Hara, Bonk, & Angeli, 2000; Jeong, 2003).
A third limitation of the extant research on self-regulation in online education is a failure to control for prior knowledge when attempting to understand the relations between task value and academic performance. As Tobias (1994) warned in his review of the literature on interest, “research is needed in which both interest and prior knowledge about the same topic are assessed so that the percentages of independent variance attributable to these two constructs may be determined” (p. 50). Because task value includes an interest component, this recommendation is particularly relevant to studies of online education that use task value as an independent variable.
Finally, the online learning literature is rather limited with respect to the student characteristics investigated. Although self-efficacy and task value have received some emphasis, none of the research reviewed considered the effects of other personal factors, such as different types of affect (mood and emotions); factors that social cognitive theoreticians consider critical to an understanding of individual performance in academic settings (Bandura, 1997; Linnenbrink & Pintrich, 2002; Pekrun, Goetz, Titz, & Perry, 2002).
Some researchers have examined how student differences and characteristics of the online environment interact with each other to influence learning. In many ways, these investigations mirror the classic Aptitude-Treatment Interaction (ATI) studies conducted by Cronbach and Snow (1977) that were designed to determine which instructional strategies are more or less effective for particular individuals with specific abilities. As a theoretical framework, ATI posits that optimal learning results when the instruction is closely matched to the aptitudes of the learner.
Using an ATI framework, Eom and Reiser (2000) examined the effects of Self Regulated Learner (SRL) strategies use on achievement and motivation in 37 sixth and seventh graders taking a computer-based course. Essentially, the authors were trying to determine how varying the amount of learner control within the computer-based course might effect the achievement and motivation of students who rated themselves as either high or low in SRL skills. Using a self-report instrument, students were classified as being either high or low self-regulated learners and then were randomly assigned to either a learner-controlled or program-controlled version of a computer-based course. Results revealed that, regardless of how students rated their SRL skills, learners in the program-controlled condition (i.e., learners who had very little control over their progression through the course), “scored significantly higher on a posttest than did learners in the learner-controlled condition” (Eom & Reiser, 2000, p. 247). Additionally, the researchers found that poorer performance in the learner-controlled condition was particularly evident in the students who rated themselves as low self-regulated learners. In fact, students who rated themselves as low in SRL skills scored higher on the posttest (approximately 76.4% higher) when taking the program-controlled condition as compared to the learner-controlled condition. Although this interaction was not statistically significant (perhaps due to inadequate power), the trend supported the researchers’ hypothesis that students with low SRL skills are not as able to learn from computer-based courses that provide high quantities of learner control as students with high self-regulating skills.
In another ATI-type investigation, McManus (2000) attempted to determine what combinations of online course non-linearity (i.e., the extent to which learners were given the opportunity to proceed through the course in a non-linear fashion) and the use of advance organizers (i.e., the presence or absence of short overviews of new material at the beginning of each lesson) would work best for 119 undergraduates reporting different levels of self-regulation. Students’ declarative knowledge was measured by a 12-item, multiple-choice test, and their procedural knowledge was measured by a 20-item, performance assessment. Although the researcher found no significant main effects or interactions, results revealed a near significant interaction between non-linearity and self-regulation (p = .054). According to McManus, these results “suggest that highly self-regulating learners learn poorly in mostly linear Web-based hypermedia learning environments, where they have very few choices, while medium self-regulating learners learn poorly in highly non-linear environments where they are given too many choices” (p. 219). Despite the non-significance of this interaction, the results are promising in that they suggest the ATI framework may be a useful approach that allows researchers to study how individual learner differences and features of the online environment interact with each other to influence learning and performance.
Taken together, the research on self-regulation and learner control in computer-based environments has failed to find statistically significant results. It is worth noting, however, that these studies, like many others, suffer from serious limitations. For example, in McManus’s (2000) work, scores from the SRL sub-scales possessed marginal internal reliabilities (Cronbach’s alphas ranged from .35 to .67), thereby compromising the ability of the study to uncover noteworthy effects (Thompson, 2003). Additionally, both studies reviewed here attempted to study online instruction by utilizing instruments developed for traditional classrooms. Although some measurement instruments may work equally well in classroom and online settings, considering the differences between the two learning environments, an instrument that works well in the classroom may not be valid in online and/or computer-based learning situations (Tallent-Runnels et al., 2006). It is important, then, that future studies employ appropriate survey instruments that, at the very least, have been pilot tested in the learning environment of interest. Certainly, if the reliability and validity of the measurement instruments used in online education research are not assessed, findings based on those instruments are, at best, questionable (DeVellis, 2003; Tallent-Runnels et al., 2006; Thompson, 2003).
Many experts believe that online learning environments require the learner to assume greater responsibility for the learning process (Dabbagh & Kitsantas, 2004; King et al., 2001; Schunk & Zimmerman, 1998). Furthermore, many of these same experts argue that self-regulatory skills are essential for success in these highly autonomous learning situations and that the development of these skills can be supported by Web-based pedagogical tools (WBPT; Azevedo, 2005; Dabbagh & Kitsantas, 2004; Zimmerman & Tsikalas, 2005). Accordingly, several researchers have attempted to determine the characteristics of effective WBPT, as well as the extent to which various self-regulatory skills might be supported and/or enhanced by these tools (see Table 2 for a summary of these studies). For example, Kramarski and Gutman (2006) randomly assigned 65 ninth graders to one of two online learning environments designed to teach mathematics: one with self-regulatory support (SRS) in the form of metacognitive questioning and the other without explicit support for self-regulation. Results showed that when pre- and post-test scores were compared, students in the SRS group significantly outperformed their counterparts in the non-supported group on all outcome measures, including performance on mathematical explanations, procedural and transfer tasks, and use of SRL strategies. In terms of effect sizes, post-test improvements in the SRS group were moderate on SRL strategies use (d = .45) to large on mathematical explanations (d = 2.24; Cohen, 1988).
Using a similar conceptual framework, Azevedo, Cromley, and Seibert (2004) confirmed the positive benefits of online self-regulatory support. In this case, however, the researchers randomly assigned 51 undergraduates to one of three computer-based scaffolding conditions: adaptive scaffolding (AS; i.e., a teacher or tutor who continuously diagnoses students’ understanding), fixed scaffolding (FS), and no scaffolding (NS). Using a mixed-methods approach, the authors found that AS facilitated positive shifts in students’ mental models (as assessed through the coding of student diagrams) significantly more than FS and NS. Furthermore, the researchers analyzed verbalizations of students’ learning activities and found that a significantly larger number of participants in the AS condition planned their learning, monitored their progress, and used learning strategies. Results from this study were particularly noteworthy because the authors used both qualitative and quantitative methods to analyze both performance and process data.
In summary, results from this line of research have been promising and suggest the following practical and theoretical implications: (1) Web-Based Pedagogical Tools (WBPT) can be an effective way to support and/or enhance students’ self-regulatory skills (Azevedo et al., 2004; Dabbagh & Kitsantas, 2005; Kauffman, 2004; Kramarski & Gutman, 2006; Niemi, Nevgi, & Virtanen, 2003); (2) adaptive scaffolding appears to be more effective in supporting students self-regulatory processes and academic performance than fixed or no scaffolding (Azevedo et al., 2004); (3) different types of WBPTs support different self-regulatory processes (Dabbagh & Kitsantas, 2005; Kauffman, 2004; Kramarski & Gutman, 2006); and (4) WBPTs may be more effective for novice learners with under-developed self-regulatory skills than for veteran learners with more advanced SRL skills (Niemi et al., 2003).
Azevedo, Cromley, & Seibert
n = 51 undergraduates
Hypermedia learning course
Adaptive scaffolding (AS) facilitated shifts in students’ mental models significantly more than fixed scaffolding (FS) and no scaffolding (NS). Students in the AS condition used more SRL strategies than students in the FS and NS conditions.
Dabbagh & Kitsantas
n = 65 graduates
Asynchronous college courses
Different types of Web-based pedagogical tools (WBPT) supported different SRL processes. Content creation and delivery tools supported the SRL processes of goal setting, help seeking, self-evaluations, and task strategies; collaborative and communication tools supported goal setting, time planning and management, and help seeking; and assessment tools supported task strategies, self-monitoring, and self-evaluation.
n = 119 undergraduates
The cognitive strategy prompting (i.e., note-taking) had the strongest influence on achievement. Self-efficacy building feedback and self-monitoring prompts had modest effects on achievement.
Kramarski & Gutman
n = 65 ninth graders
Online mathematics course
Compared to students in the course without self-regulatory support, students in the course supported with self-metacognitive questioning significantly outperformed their counterparts in math explanations, procedural tasks, transfer tasks, and use of self-monitoring strategies.
Niemi, Nevgi, & Virtanen
n = 108 undergraduates
Asynchronous college courses
The online virtual tutor was most useful for students who had difficulties in learning, were at an early stage in their university studies, and who had unstable SRL skills. Additionally, the tool worked best when the teacher gave guidance on how to properly employ it.
*Note. A WebQuest is an inquiry-based instructional tool designed to facilitate search and synthesis of information from multiple sources (Dodge, 1997 as cited in Kauffman, 2004).
In terms of research quality, these self-regulatory scaffolding studies tended to use superior research methods when compared to much of the empirical work on self-regulation in online education. For example, in three of five WBPT studies, researchers randomly assigned participants to treatment and control/comparison groups, thereby enhancing the internal validity of their experiments and improving their ability to establish causal relationships (Shadish, Cook, & Campbell, 2002). Additionally, all of the investigators used multiple outcome measures and employed both qualitative and quantitative methods to analyze their data. Taken together, these studies have taken a positive step toward improving the methodological quality of research in online distance education (Abrami & Bernard, 2006; Bernard et al., 2004a).
Clearly, more well-designed research is needed on self-regulation and its influence on student success in online learning environments. To date, most studies in this burgeoning field have been descriptive in nature and have suffered from numerous methodological limitations. Despite these limitations, however, the studies reviewed here seem to support the linkages between students’ motivational beliefs about a learning task, their use of learning strategies, and their performance in online settings. Furthermore, although the empirical support is thin, it appears that highly self-regulated learners may have more success in learner-controlled environments than their peers with poorer self-regulatory skills. Finally, some of the highest quality research in online education seems to indicate that providing students with self-regulatory scaffolding can be an effective instructional method—one that instructional designers might do well to consider including as integral to their online courses.
Ultimately, the existing empirical literature supports the trends established in research with more traditional classrooms; specifically, that self-regulation is an important, if not essential skill for effective learning and performance (Miltiadou & Savenye, 2003; Schunk & Zimmerman, 1994, 1998). Therefore, future research should continue to explore self-regulation in online education, with the intent of determining which instructional elements, as well as which existing personal characteristics, behaviors, and attitudes, contribute to achievement in and satisfaction with this emergent form of instruction (Bernard et al., 2004).
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Anthony R. Artino, Jr. is a doctoral candidate in the Cognition and Instruction Program, Department of Educational Psychology, Neag School of Education, University of Connecticut. Anthony is interested in online learning and its use within both higher education and military training environments. In his research, he uses social cognitive views of academic self-regulation to investigate how motivational beliefs and achievement emotions influence student success within highly autonomous, online learning situations. He can be reached at firstname.lastname@example.org.
Anthony R. Artino Jr.
Doctoral Candidate, Cognition and Instruction Program
Department of Educational Psychology
Neag School of Education, University of Connecticut
373 Squaw Hollow Road
Ashford, CT 06278
Phone: (cell) 860.942.9345,
Fax: (work) 860.486.3510
 Bell and Akroyd (2006) utilized a self-efficacy scale from the Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich, Smith, Garcia, & McKeachie, 1993). It is worth noting that the definition of self-efficacy used to develop the MSLQ’s self-efficacy scale is a bit broader than other measures of self-efficacy, which usually limit themselves to assessing confidence in one’s ability to attain designated types of performances and do not include expectancy for success (see Bandura, 1997). Accordingly, Bell and Akroyd (2006) referred to their self-efficacy scale as a measure of expectancy.