Predicting Deep Learning Strategies: Making a Case to Consider Need Satisfaction and Motivation
Conference Tracks
Assessment and SoTL - Research
Abstract
The purpose of this study was to examine predictors of surface and deep metacognitive strategies in Exercise Science students. Two hundred and sixty-six students completed questionnaires measuring basic need satisfaction, motivation, and metacognitive strategies. Two regressions were run to determine predictors of surface and deep metacognitive strategies. The first regression revealed intrinsic motivation, identified regulation, external regulation and internal perceived locus of control were predictors of surface strategies. The second regression showed that intrinsic motivation, identified regulation, competence, relatedness, and class predicted deep strategies. Students who were intrinsically motivated and had their basic needs met used more deep learning strategies.
Session Format
Research Brief and Reflection Panels
1
Publication Type and Release Option
Image (Open Access)
Recommended Citation
Langdon, Jody L.; Botnaru, Diana; and Van Arkel, Johanna, "Predicting Deep Learning Strategies: Making a Case to Consider Need Satisfaction and Motivation" (2022). SoTL Commons Conference. 53.
https://digitalcommons.georgiasouthern.edu/sotlcommons/SoTL/2022/53
Predicting Deep Learning Strategies: Making a Case to Consider Need Satisfaction and Motivation
The purpose of this study was to examine predictors of surface and deep metacognitive strategies in Exercise Science students. Two hundred and sixty-six students completed questionnaires measuring basic need satisfaction, motivation, and metacognitive strategies. Two regressions were run to determine predictors of surface and deep metacognitive strategies. The first regression revealed intrinsic motivation, identified regulation, external regulation and internal perceived locus of control were predictors of surface strategies. The second regression showed that intrinsic motivation, identified regulation, competence, relatedness, and class predicted deep strategies. Students who were intrinsically motivated and had their basic needs met used more deep learning strategies.
