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 & InstructionDepartment of Educational Psychology

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Overview

• Background

• Research Question

• Methods

• Results

• Discussion

• Limitations & Future Directions

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BackgroundSocial Cognitive Self-Regulation

Person

Environment Behavior

EnvironmentalSelf-Regulation

BehavioralSelf-Regulation

Covert Self-Regulation

“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).

(Adapted from Bandura, 1997)

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Environment(Online Education)

BackgroundMotivational Influences on Learning Strategies Use

Person

Behavior

Motivational Characteristics

• Task Value

• Self-Efficacy

Use of Learning Strategies

• Elaboration

• Critical Thinking

• Metacognitive Self-Regulation

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

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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 Value Self-Efficacy+

Elaboration

MetacognitiveSelf-Regulation

CriticalThinking

(+)

Motivational Components

Self-Regulated Learning Strategies

Hypothesis

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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

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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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ResultsStudent Characteristics

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%)

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ResultsPearson Correlations

Variable M SD α 1 2 3 4 5

1. Task Value 5.78 1.23 .94 - .58* .67* .48* .60*

2. Self-Efficacy 5.79 1.03 .93 - .65* .65* .56*

3. Elaboration 5.58 1.23 .87 - .85* .75*

4. Critical Thinking 5.00 1.36 .88 - .68*

5. Metacognitive Self-Regulation 4.76 1.67 .89 -

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

Variable

Elaboration Critical ThinkingMetacognitive Self-Regulation

B SE B β B SE B β B SE B β

Task Value .43 .08 .44** .18 .11 .16 .39 .09 .41**

Self-Efficacy .48 .10 .40** .73 .13 .55** .37 .11 .32*

Model Summary R2 = .55, p < .001 R2 = .44, p < .001 R2 = .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

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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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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?

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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

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

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The End

Questions?

Paper can be downloaded at

http://www.tne.uconn.edu/presentations.htm