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Neag School of Education. Using Social Cognitive Theory to Predict Students’ Use of Self-Regulated Learning Strategies in Online Courses. Anthony R. Artino, Jr. and Jason M. Stephens. Program in Cognition & Instruction Department of Educational Psychology. Overview. Background

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Neag School of Education

Using Social Cognitive Theory to Predict Students’ Use of Self-Regulated Learning Strategies in Online Courses

Anthony R. Artino, Jr. and Jason M. Stephens

Program in Cognition & Instruction

Department of Educational Psychology

Overview l.jpg

  • Background

  • Research Question

  • Methods

  • Results

  • Discussion

  • Limitations & Future Directions

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BackgroundSocial Cognitive Self-Regulation




Covert Self-Regulation





(Adapted from Bandura, 1997)

“Personal, behavioral, and environmental factors are constantlychanging during the course of learning and performance, andmust be observed or monitored using three self-oriented feedbackloops” (Zimmerman, 2000, p. 14).

Background motivational influences on learning strategies use l.jpg

Environment(Online Education)

  • Use of Learning Strategies

    • Elaboration

    • Critical Thinking

    • Metacognitive Self-Regulation

BackgroundMotivational Influences on Learning Strategies Use



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Purpose of the Study

  • To determine if the linkages between task value, self-efficacy, and students’ use of cognitive and metacognitive learning strategies extend to university studentslearning in thecontext ofonline education (WebCT courses)

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Self-Regulated Learning Strategies

Motivational Components



Task Value





Research Question

RQ: How do two motivational components of social cognitive theory – task value and self-efficacy – relate to students’ use of self-regulated learning strategies in online courses?

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  • University students (n = 96) in WebCT versions of graduate and undergraduate courses in Departments of Educational Psychology and Information Sciences

  • Completed 60-question online survey during last four weeks of the semester

  • Survey adapted from:

    • Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich, et al., 1993)

    • Where necessary, items were re-worded to reflect online nature of courses

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MethodsPredictor Variables

  • Task Value (6 items; α = .94)

    • It is important for me to learn the course material in this class

    • I am very interested in the content area of this course

    • I think the course material in this class is useful for me to know

  • Self-Efficacy for Learning and Performance (7 items; α = .93)

    • I believe I will receive an excellent grade in this class

    • I’m confident I can do an excellent job on the assignments in this course

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MethodsOutcome Variables

  • Cognitive Strategies

    • Elaboration (5 items; α = .87)

      • I try to understand the material in this class by making connections between the readings and the concepts from the online activities

      • When reading for this class, I try to relate the material to what I already know

    • Critical Thinking (5 items; α = .88)

      • I treat the course material as a starting point and try to develop my own ideas about it

      • I often find myself questioning things I hear or read in this course to decide if I find them convincing

  • Metacognitive Self-Regulation (10 items; α = .89)

    • I ask myself questions to make sure I understand the material I have been studying in this class

    • When I study for this class, I set goals for myself in order to direct my activities in each study period

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45 women (47%)

51 men (53%)


Mean Age: 30.7 years

SD: 9.3 years

Range: 19-56

Educational Experience:

High School/GED (n = 3, 3.1%)

Some College (n = 29, 30.2%)

2-Year College (n = 22, 22.9%)

4-Year College (B.S./B.A.) (n = 13, 13.5%)

Master’s Degree (n = 28, 29.2%)

Professional Degree (M.D./J.D.) (n = 1, 1.0%)

ResultsStudent Characteristics

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ResultsPearson Correlations

Means, Standard Deviations, Cronbach’s Alphas, and Pearson Correlations Between the Motivation and Learning Strategies Variables.

Note. N = 96. *p < .01.

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ResultsMultiple Linear Regressions

Summary of Multiple Linear Regression Analyses Predicting Students’ Reported Use of Self-Regulated Learning Strategies


Regression (Stevens, 2002):

Wilks’ Λ = .37, F = 19.62, p < .001

Note. N = 96. *p < .01. **p < .001.

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DiscussionGeneral Findings

  • Findings generally support prior research that students’ motivational beliefs about a learning task are related to their use of SRL strategies in academic settings

  • Results provide some evidence that these views extend to online education

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DiscussionTask Value

  • Task value was a significant individual predictor of elaboration and metacognitive self-regulation

  • Students who valued the learning task were more cognitively and metacognitively engaged in trying to learn the material

  • Findings are consistent with prior research

    • Task value → cognitive and metacognitive strategies use (Pintrich & De Groot, 1990)

    • Task value did not have a significant direct relation to student performance when cognitive and metacognitive strategy use were considered (TV effect mediated by SRL strategies)

  • Task value links to SRL strategies use has not been well studied in online learning environments

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  • Self-efficacy was a significant individual predictor of elaboration, critical thinking, and metacognitive self-regulation

  • Students who believed they were capable were more likely to report using cognitive and metacognitive strategies

  • Results are consistent with prior research

    • Self-efficacy → SRL strategies in traditional classrooms (Pintrich & De Groot, 1990; Zimmerman & Bandura, 1994)

  • Self-efficacy links to SRL strategies have not been well studied in online learning environments

    • How do online learners’ efficacy beliefs influence their use of SRL strategies and, ultimately, their online academic performance?

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Educational Implications

Diagnostic Tool

  • Instructors do not have access to traditional student cues (e.g., facial expressions, non-attendance, etc.)

  • Administer modified MSLQ early in course to assess which students might require more “other-regulation”

    Instructional Elements

  • Enhancing value may lead to greater engagement

    • For example, use PBL learning cycles rooted in controversial, “real world” issues (Bransford, Brown, & Cocking, 2000)

  • Enhancing efficacy may lead to greater engagement

    • Set challenging, proximal goals (Schunk, 1991)

    • Scaffold students’ self-regulation by providing timely, honest, and explicit feedback (Pintrich & Schunk, 2002)

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Limitations & Future Directions


  • Data are correlational; cannot make causal conclusions

  • Use of self-reports only

    • Social desirability bias

    • Mono-method bias; method itself may influence results

  • Limited generalizability based on particular sample used

    Future Directions

  • Measure more outcome variables

    • Choice, effort, persistence, and procrastination

    • Academic achievement and online “engagement”

  • Is there an interaction between students’ level of SRL and course characteristics?

    • For example, level of SRL and amount of instructor guidance in online discussions

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The End


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