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 Background Social Cognitive Self-Regulation Person EnvironmentBehavior Environmental Self-Regulation Behavioral Self-Regulation Covert Self- Regulation “Personal, behavioral, and environmental factors are constantly changing during the course of learning and performance, and must be observed or monitored using three self-oriented feedback loops” (Zimmerman, 2000, p. 14). (Adapted from Bandura, 1997)

4 Environment (Online Education) Background Motivational Influences on Learning Strategies Use Person Behavior Motivational Characteristics Task Value Self-Efficacy Use of Learning Strategies Elaboration Critical Thinking Metacognitive Self-Regulation

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 students learning in the context of online education (WebCT courses)

6 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? Task ValueSelf-Efficacy + Elaboration Metacognitive Self-Regulation Critical Thinking (+) Motivational Components Self-Regulated Learning Strategies Hypothesis

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 Methods Predictor 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 Methods Outcome 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 Results Student Characteristics Gender: 45 women (47%) 51 men (53%) Age: Mean Age: 30.7 years SD: 9.3 years Range: 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%)

11 Results Pearson Correlations VariableMSD α Task Value *.67*.48*.60* 2. Self-Efficacy *.56* 3. Elaboration *.75* 4. Critical Thinking * 5. Metacognitive Self-Regulation Means, Standard Deviations, Cronbach’s Alphas, and Pearson Correlations Between the Motivation and Learning Strategies Variables. Note. N = 96. *p <.01.

12 Results Multiple Linear Regressions Variable ElaborationCritical Thinking Metacognitive Self-Regulation BSE BβB βB β Task Value ** ** Self-Efficacy ** ** * Model SummaryR 2 =.55, p <.001R 2 =.44, p <.001R 2 =.42, p <.001 Note. N = 96. *p <.01. **p <.001. 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

13 Discussion General 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 Discussion Task 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 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?

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