PERC 2016 Abstract Detail Page
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| Abstract Title: | Finding groups in student-level data: Utilizing the Profile Approach |
|---|---|
| Abstract: | Individual students bring their own motivations and self-regulated study strategies to the classroom. When interested in characterizing students, one can turn to the Profile Approach. At the core of this approach is a theoretical framework that considers the interactions of different motivational and strategic self-regulatory constructs and describes the various, distinct patterns that exist across these constructs as Learning Profiles. The Profile Approach relies on individuals' characteristics; thus, accompanying research designs must incorporate collecting student-level data on various motivational and strategic self-regulatory constructs. Also, analyses must consider patterns across these constructs. Cluster analysis is such a technique, allowing identification of coherent groups (Learning Profiles) based on patterns in the data. Resulting Profiles are useful for exploring hypotheses about student characteristics, guiding instructors to better understand their students, and follow-up statistical analyses. I present--from start to finish--applying the Profile Approach to identify Learning Profiles among algebra-based, studio-mode physics students. |
| Abstract Type: | Symposium Poster |
| Parallel Session: | Expanding Research Questions by Expanding Quantitative Methodologies |
Author/Organizer Information | |
| Primary Contact: |
Jarrad W. T. Pond University of Central Florida |
| Co-Author(s) and Co-Presenter(s) |
Jacquelyn J. Chini |
