1. Anastassis Kozanitis École Polytechnique Montreal Canada Jean-François Desbiens & Sèverine Lanoue University of Sherbrooke Canada Conference on Higher Education Pedagogy Virginia Tech February 2012

2.  Context  Conceptual framework  Methodology  Results  Discussion

3.  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

4.  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?

5.  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).

6.  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).

7.  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)

8.  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.

9.  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.

10. Socio- demographics Instructor’s reaction and openness Task and learning activities Task-value Avoidance goal Performance goal Mastery goal Control beliefs Self-efficacy

11.  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.

12.  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).

13.  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.

14. Mastery GoalPerformanceAvoidanceTask ValueControl Beliefs Self-efficacy ModelR2R2 FR2R2 FR2R2 FR2R2 FR2R2 FR2R2 F 10.105.37**0.021.910.063.84**0.147.50**0.031.350.063.71* 20.269.54**0.052.38*0.217.66**0.259.03**0.207.14**0.238.19** 30.255.23**0.061.760.234.68**0.296.13**0.023.74**0.255.08** 40.284.17**0.091.82*0.233.51**0.304.48**0.053.10**0.233.44** 50.273.20**0.111.74*0.252.92**0.354.16**0.022.31**0.263.09**

15. Mastery GoalPerformanceAvoidanceTask ValueControl Beliefs Self-efficacy VariablesBetat t t t t t Age0.162.02* GPA0.232.44* Reaction0.463.75**0.413.04**0.353.03**0.272.26*0.373.05** Openness-0.56-4.62** Questioning0.212.12*0.212.29* Autonomous0.272.19*0.252.14*0.352.84* Critical-0.27-2.05* Synthesize0.232.03* Evaluate0.201.96* Job related0.292.63* Adapt-0.27-1.98*

16.  Instructor and context-related variables are significantly related to student motivational components.  They tend to overhaul most socio- demographic 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.

17.  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.

18.  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.

19.  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.

20.  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)

21.  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 (12 e 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 Self- Regulated Learning: Theory, Research, and Applications. Journal of Higher Education, 80 (4), 476-479.