Modeling temporal self-regulatory processing in a higher education biology course. (April 2021)

• Record Type: Journal Article
• Authors: Greene, Jeffrey A.; Plumley, Robert D.; Urban, Christopher J.; Bernacki, Matthew L.; Gates, Kathleen M.; Hogan, Kelly A.; Demetriou, Cynthia; Panter, Abigail T.
• Is Part Of: Learning and instruction. Volume 72(2021)
• Publication Date: 2021-04
• Subjects: Self-regulated learning -- Active learning -- Biology -- Latent profile analysis -- Differential sequence mining; Learning -- Periodicals; Teaching -- Periodicals; Apprentissage -- Périodiques; Enseignement -- Périodiques; Learning; Teaching; Periodicals; Electronic journals; 370.1
• DOI: 10.1016/j.learninstruc.2019.04.002
• Languages: English
• ISSNs: 0959-4752

Abstract

Abstract: The scientific literacy and conceptual understanding demands of the 21st century have necessitated fundamental changes in science education, including changes from traditional lecture to more active learning pedagogies. The affordances of such pedagogies can benefit students, but only when they are able to enact effective and efficient self-regulated learning processing. More research is needed to understand how and when students should self-regulate during science learning, as well as how to help those students who struggle to do so. In this study, we leveraged multimodal online interaction trace data from 408 college students enrolled in an introductory biology class to investigate the temporal nature of self-regulation during science education. Using latent profile analyses, we found differences in self-regulatory processing predicted course performance, with implications for the development of systems for identifying and supporting students who are likely to struggle in active learning science education environments. Highlights: Active learning pedagogies in science education require self-regulated learning. We used multimodal trace data of SRL processing to predict biology performance. We found the frequency of SRL early in the course predicted later performance. Temporal and contingent SRL trace data can be used to identify struggling students.