Transcript Using Social-Cognitive Theory to Predict Students’ Use of

Neag School of Education
Task Value, Self-Efficacy, and Experience:
Predicting Military Students’ Attitudes Toward
Self-Paced, Online Learning
Anthony R. Artino, Jr.
Program in Cognition and Instruction
Department of Educational Psychology
Overview
• Background
• Research Questions
• Methods
• Results
• Discussion
• Limitations & Future Directions
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Background
Interest in Self-Regulated Learning
• Interest in academic self-regulation has grown
• How do students become masters of their own
learning processes?
• Self-regulated learners efficiently control their own
learning experiences by…
– Establishing a productive work environment and using
resources effectively
– Organizing and rehearsing information to be learned
– Holding positive beliefs about their capabilities, the value of
learning, and the factors that influence learning (Schunk &
Zimmerman, 1998)
3
Background
Growth of Online Learning
• Online education has emerged as a viable
alternative to traditional classroom instruction
(Moore, 2003; Tallent-Runnels et al., 2006)
• Survey of 1000 U.S. colleges and universities:
– 63% of schools offering undergraduate face-to-face
courses also offer undergraduate courses online (Sloan
Consortium, 2005)
• Department of Defense committed to transforming
majority of face-to-face training to online learning
(United States General Accounting Office, 2003)
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Background
A Learner-Centered Focus
• A shift from an instructor-centered to a
learner-centered focus
• Without an ever-present instructor, students
do not received as much guidance/structure
• Students must take greater responsibility for
the management/control of their own learning
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Background
Linking Self-Regulation and Online Learning
• Ultimately, online students may need…
– well-developed self-regulated learning skills
to guide their cognition and behavior in
these highly independent environments
(Bandura, 1997; Schunk & Zimmerman,
1998)
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Background
Social Cognitive Self-Regulation
Person
Behavioral
Self-Regulation
Covert SelfRegulation
Environment
Behavior
Environmental
Self-Regulation
(Adapted from Bandura, 1997)
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Background
Important Personal Variables
• Prior research in traditional classrooms, and
limited research with online learning, has
revealed the importance of…
– Task Value
– Self-Efficacy
– Prior Experience
• Positively related to students use of SRL
strategies, academic achievement,
satisfaction, and choice behaviors
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Purpose of the Study
• To determine if the linkages between
task value, self-efficacy, prior
experience, and adaptive learning
outcomes extend to military students
learning in the context of self-paced,
online training
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Research Questions
RQ1: How do task value, self-efficacy, and prior
experience with online learning relate to
students’ overall satisfaction, perceived
learning, and intentions to enroll in future
online courses?
RQ2: Are there significant differences in the predictor
and outcome variables when comparing
students reporting on required courses versus
students reporting on courses they chose to
complete?
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Methods
• Convenience sample of military personnel
(n = 204) from the Naval Operational
Medicine Institute
• Completed an online survey regarding…
– “the most effective self-paced, online course they
had completed within the last two years”
• Participants indicated if the course was one
they chose to take or were required to
complete
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Methods
Survey Components
Section 1
– 25 items; Likert-type response scale
• 1-completely disagree to 7-completely agree
– Principle axis factor analysis with oblique rotation (Oblimin; delta = 0)
• 3 interpretable factors accounting for 61.6% of the total variance in items
Task Value (14 items; α = .95)
– I liked the subject matter of this course.
– I will be able to use what I learned in this course in my job.
Self-Efficacy for Learning with Self-Paced, Online Training (7 items; α = .89)
– I can perform well in a self-paced, online course.
– I am confident I can learn without the presence of an instructor to assist me.
Satisfaction (4 items; α = .91)
– Overall, I was satisfied with my online learning experience.
– This online course met my needs as a learner.
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Methods
Survey Components
Section 2
– Background and demographics items
– Three individual items used as variables
Experience
– In your estimation, how experienced are you with self-paced, online learning?
– 1-extremely inexperienced to 7-extremely experienced
Perceived Learning
– In your estimation, how well did you learn the material presented in this course?
– 1-not well at all to 7-extremely well
Choice
– What is the likelihood that you will enroll in another self-paced, online Navy
course if you are not required to do so?
– 1-definitely will not enroll to 7-definitely will enroll
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Results
Participant Characteristics
Gender:
53 women (26%)
150 men (74%)
Age:
Mean Age: 39.0 years
Educational Experience:
High School/GED
(n = 21, 10%)
Some College
(n = 51, 25%)
SD: 9.3 years
2-Year College
(n = 24, 12%)
Range: 22-69
4-Year College (B.S./B.A.)
(n = 25, 12%)
Master’s Degree
(n = 48, 24%)
Doctoral Degree
(n = 15, 7%)
Professional Degree (M.D./J.D.)
(n = 16, 8%)
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Results
RQ1: Pearson Correlations
Means, Standard Deviations, Cronbach’s Alphas, and Pearson Correlations Between the Measured
Variables.
M
SD
α
1
2
3
4
5
6
1. Task Value
4.47
1.16
.95

.36**
.17*
.73**
.58**
.50**
2. Self-Efficacy
5.36
1.07
.89

.43**
.58**
.57**
.41**
3. Experience
5.19
1.37
.91

.20**
.36**
.46**
4. Satisfaction
4.56
1.42
-

.70**
.59**
5. Perceived Learning
4.53
1.45
-

.54**
6. Choice (Intentions to Enroll)
4.32
1.88
-
Variable

Note. N = 204. *p < .05. **p < .01.
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Results
RQ1: Multiple Linear Regressions
Summary of Multiple Linear Regression Analyses Predicting Satisfaction, Perceived
Learning, and Intentions to Enroll in Future Online Courses
Satisfaction
Perceived Learning
Choice
(Intentions to Enroll)
Variable
B
SE B
β
B
SE B
β
B
SE B
β
Multivariate
Regression
(Stevens, 2002):
Task Value
.73
.06
.60**
.54
.07
.43**
.64
.10
.40**
Wilks’ Λ = .25,
F = 40.47, p < .001
Self-Efficacy
.52
.07
.39**
.49
.08
.36**
.22
.11
.12
Experience
-.07
.05
-.07
.13
.06
.12*
.46
.08
.33**
Model Summary
R2 = .65, p < .001
R2 = .50, p < .001
R2 = .40, p < .001
Note. N = 204. *p < .05. **p < .001.
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Results
RQ2: Group Comparisons
1-Way MANOVA; Wilks’ Λ = .86, F(6, 191) = 5.15, p < .001
Results of t-Tests Comparing Students Reporting on an Elective and Students Reporting on a Required Course
Variable
Elective Course
(n = 35)
Required Course
(n = 166)
M
SD
M
SD
t
df
Cohen’s d
Task Value
5.21
.86
4.32
1.14
4.29***
62.38
.81
Self-Efficacy
5.56
1.03
5.34
1.06
1.15
50.64
-
Experience
5.49
1.25
5.14
1.39
1.35
53.30
-
Satisfaction
5.24
1.38
4.43
1.38
3.16**
49.36
.59
Perceived
Learning
5.00
1.39
4.44
1.45
2.01*
48.89
.39
Choice
5.66
1.45
4.05
1.85
4.83***
59.91
.90
Note. *p < .05. **p < .01. ***p < .001.
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Discussion
General Findings
• Findings generally support prior research that
students’ motivational beliefs and prior experience
are related to positive academic outcomes
• Results provide some evidence that these
relationships extend to self-paced, online learning in
the context of military training
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Discussion
Task Value
• Task value was a significant positive predictor of
satisfaction, perceived learning, and choice behaviors
• Findings are consistent with prior research
– Task value → cognitive engagement and academic
performance (Pintrich & De Groot, 1990)
– Task value → overall satisfaction (Lee, 2002)
Educational Implications
• Instructional elements designed to enhance value
may improve overall satisfaction, learning, and choice
behaviors
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Discussion
Self-Efficacy
• Self-efficacy was a significant positive predictor of
satisfaction and perceived learning, but not choice
• Findings are consistent with prior research
– Online education; self-efficacy → satisfaction and academic
achievement (Lynch, 2002; Wang & Newlin, 2002)
– Value beliefs tend to be better predictors of choice
behaviors than expectancy beliefs (Eccles & Wigfield, 1995)
Educational Implications
• Instructional elements designed to enhance efficacy
may improve students’ overall satisfaction and
learning
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Discussion
Group Differences
• Participants reporting on a course they chose to take
conveyed significantly more positive attitudes than
those reporting on required courses
• Findings consistent with motivation literature (Dai &
Sternberg, 2004; Pintrich & Schunk, 2002)
Educational Implications
• Organizational leaders may want to provide
personnel with opportunities to exercise choice and
control over their online learning activities
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Limitations & Future Directions
Limitations
– Data are correlational; cannot make causal conclusions
– Some participants reporting on recent courses, some distant
courses
– Use of self-reports only
• Social desirability bias
• Mono-method bias; method itself may influence results
• Perceived learning variable is particularly problematic
Future Directions
– Use more direct measures of student performance
(i.e., course grades)
– Control for prior knowledge when studying interest/value
(Tobias, 1994)
– Assess whether online interventions designed to enhance task
value and self-efficacy also improve academic performance
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The End
Questions?
Paper can be downloaded at
http://www.tne.uconn.edu/presentations.htm
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