Neag School of Education

1. 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

2. Overview • Background • Research Question • Methods • Results • Discussion • Limitations & Future Directions

3. BackgroundSocial Cognitive Self-Regulation Person Behavioral Self-Regulation Covert Self-Regulation Environment Behavior Environmental 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).

4. 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 Person Behavior

5. 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)

6. Hypothesis Self-Regulated Learning Strategies Motivational Components Elaboration (+) Task Value Self-Efficacy CriticalThinking + MetacognitiveSelf-Regulation 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?

7. Methods • 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

8. 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

9. 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

10. Gender: 45 women (47%) 51 men (53%) Age: 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

11. ResultsPearson Correlations Means, Standard Deviations, Cronbach’s Alphas, and Pearson Correlations Between the Motivation and Learning Strategies Variables. Note. N = 96. *p < .01.

12. ResultsMultiple Linear Regressions Summary of Multiple Linear Regression Analyses Predicting Students’ Reported Use of Self-Regulated Learning Strategies Multivariate Regression (Stevens, 2002): Wilks’ Λ = .37, F = 19.62, p < .001 Note. N = 96. *p < .01. **p < .001.

13. 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

14. 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

15. DiscussionSelf-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?

16. 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)

17. Limitations & Future Directions Limitations • 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

18. The End Questions? Paper can be downloaded at http://www.tne.uconn.edu/presentations.htm