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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

  • Background
  • Research Question
  • Methods
  • Results
  • Discussion
  • Limitations & Future Directions
background social cognitive self regulation
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

Motivational Characteristics

    • Task Value
    • Self-Efficacy

Environment(Online Education)

  • Use of Learning Strategies
    • Elaboration
    • Critical Thinking
    • Metacognitive Self-Regulation
BackgroundMotivational Influences on Learning Strategies Use



purpose of the study
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)
research question


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?

  • 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
methods predictor variables
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
methods outcome variables
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
results student characteristics

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
results pearson correlations
ResultsPearson Correlations

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

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

results multiple linear regressions
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.

discussion general findings
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
discussion task value
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
discussion self efficacy
  • 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?
educational implications
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)
limitations future directions
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
the end
The End


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