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
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
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
(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).
Self-Regulated Learning Strategies
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?
45 women (47%)
51 men (53%)
Mean Age: 30.7 years
SD: 9.3 years
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
Means, Standard Deviations, Cronbach’s Alphas, and Pearson Correlations Between the Motivation and Learning Strategies Variables.
Note. N = 96. *p < .01.
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.
Paper can be downloaded at