Transcript Better predictors of student motivation: Pedagogical vs

Anastassis Kozanitis
École Polytechnique Montreal
Canada
Jean-François Desbiens
&
Sèverine Lanoue
University of Sherbrooke
Canada
Conference on Higher Education Pedagogy
Virginia Tech
February 2012
 Context
 Conceptual
framework
 Methodology
 Results
 Discussion
3
year government-funded research
 Partial results of a larger research
 4 French speaking universities in Canada
 6 undergraduate programs
 Multi-method scheme





Short and long instructor’s questionnaire
Short and long student’s questionnaire
Classroom filming, video analyses
Initial interviews
Follow-up interviews
 How
do instructor related variables influence
their pedagogical decisions?
 Do instructors’ pedagogical decisions have an
impact on students’ approach to learning?
 In turn, does this have an impact on their
actual learning?
 If so, to what extent? In which situations?
Under what conditions?
 Motivation
and engagement are strongly
related to student learning, academic
achievement, and persistence (NSSE, Kuh, & al.
2001, McKeachie & Svinicki, 2006).
 According
to the socio-cognitive paradigm
cognitions and students’ perceptions of their
abilities, their school work and the learning
environment act as mediators of their behavior
and explain much of the achievement-related
behaviors, such as effort (Bandura, 1997).
 Bradley
and Graham, (2000) found a positive
relationship between instructor-student
interactions and student academic
engagement.
 Others
have found that instructional practices
are related to student adoption of mastery and
performance goals (Anderman, Patrick,
Hruda, & Linnenbrink, 2002; Patrick,
Anderman, Ryan, Edelin, & Midgley, 2001).
 Theoretical
models explaining motivation
have integrated myriad of variables, such as:



Precollege and socio-demographic characteristics
(gender, age, family values, ability);
Social and cognitive characteristics (student
perceptions of self and others, school related
value, goals);
Contextual characteristics (class size, learning
activities)
 The
Expectancy-Value theory is used as a
conceptual framework in a number of studies
on student motivation.
 Relevant
because of its consideration of how
course-specific factors are thought to
influence students’ motivation.
 For



example:
perceived nature of the tasks used;
the way in which students are recognized;
the perceived teachers’ instructional practices.
A
broad adaptation of a model proposed by
Pintrich & Schunk (2002) was used to explore
the relation between motivation to learn,
students’ socio-demographic characteristics,
their perception of tasks and learning
activities, and their perception of
instructor’s openness and reaction towards
students.
Sociodemographics
Mastery goal
Performance
goal
Instructor’s
reaction and
openness
Avoidance goal
Task and
learning
activities
Control beliefs
Task-value
Self-efficacy
 The
purpose of this study is to examine if
instructor and course characteristics
contribute to student motivation above and
beyond socio-demographic variables;
 It
addresses the underling practical problem
on how to motivate students.
Sample:


French speaking engineering school in Quebec,
Canada;
215 students (79% male, with a mean age of 22.7,
SD=4.1)
Instrument:



Condensed version of the Motivated Strategies for
Learning Questionnaire (MSLQ) (Pintrich et
al.1993);
Student Engagement Survey (Ahlfeldt et al. 2005);
Perceived Teacher Support of Questioning (PTSQ;
Karbenick & Sharma, 1994).
 Multiple
linear regression analysis was used
to predict the set of motivational
components for this study.
 Independent
variables were introduced with
the enter-remove method, in the following
order:



socio-demographic variables,
instructor attitude and behavior,
student perception of tasks and learning
activities.
Mastery Goal
Performance
Avoidance
Task Value
Control
Beliefs
2
R
F
Model
R2
F
R2
F
R2
F
R2
F
1
0.10
5.37**
0.02
1.91
0.06
3.84**
0.14
7.50**
0.03
2
0.26
9.54**
0.05
2.38*
0.21
7.66**
0.25
9.03**
3
0.25
5.23**
0.06
1.76
0.23
4.68**
0.29
4
0.28
4.17**
0.09
1.82*
0.23
3.51**
5
0.27
3.20**
0.11
1.74*
0.25
2.92**
Self-efficacy
R2
F
1.35
0.06
3.71*
0.20
7.14**
0.23
8.19**
6.13**
0.02
3.74**
0.25
5.08**
0.30
4.48**
0.05
3.10**
0.23
3.44**
0.35
4.16**
0.02
2.31**
0.26
3.09**
Mastery Goal
Variables
Beta
t
Performance
Beta
t
Avoidance
Beta
t
Age
GPA
0.23
2.44*
Reaction
0.46
3.75**
0.41
3.04**
Openness
-0.56
Questioning
Autonomous
0.21
0.27
Task Value
Beta
t
0.16
2.02*
0.35
3.03**
0.25
2.14*
Critical
Synthesize
0.23
2.03*
Evaluate
0.20
1.96*
Job related
0.29
2.63*
Adapt
-0.27
-1.98*
Beta
t
0.27
0.37
3.05**
0.21
2.29*
0.35
2.84*
-0.27
-2.05*
2.26*
Self-efficacy
-4.62**
2.12*
2.19*
Control
Beliefs
Beta
t
 Instructor
and context-related variables are
significantly related to student motivational
components.
 They tend to overhaul most sociodemographic variables when considered
concurrently.
 Instructor reaction to student questioning is
positively related to all components except
for Avoidance goals, which is, not
surprisingly, inversely related to instructor
openness.
 Students
tend to have lower performance
goals when they are asked to participate in
learning activities that require adapting to
new or unforeseen situations.
 Although
older students show higher task
value, results indicate that various task
related variables can also positively
influence task value. Namely autonomous
learning, evaluating information, and job
related knowledge.
 Critical
thinking activities seem to be
negatively related to self-efficacy beliefs.
 One
possible explanation to this surprising
result might ensue by the fact that
undergraduates are rarely exposed to
activities of this nature, and therefore feel
insufficiently prepared to do well.
 This
study bears evidence that instructors’
classroom attitude and pedagogical decisions
can have a direct influence on student
motivation.
 Carefully
designing learning activities can
promote effective motivational components.
 Compare
between programs
 Compare parametric and non-parametric
analyses (regressions, HLM, PCA)
 Triangulate with qualitative data
 Verify relations with actual learning outcomes
and academic success (final grades or GPA)





Bandura, A. (1997). Attention and retrieval from long-term
memory. Journal of Experimental Psychology: General, 13,
518-540.
McKeachie, W.J. et Svinicki, M. (2006). McKeachie’s
teaching tips (12e éd.). New York: Houghton Mifflin.
Pintrich, P., & Schunk, D. (2002). Motivation in education:
Theory, research, and applications (2nd ed.), Upper Saddle
River, NJ: Merrill.
Pintrich, P.R., & Zusho, A. (2002). The development of
academic self-regulation: The role of cognitive and
motivational factors. In A. Wigfield & J.S. Eccles (Eds.),
Development of achievement motivation (pp.249-284). San
Diego: Academic Press.
Schunk, D., & Zimmerman, B. (2009). Motivation and SelfRegulated Learning: Theory, Research, and Applications.
Journal of Higher Education, 80 (4), 476-479.