Leveraging Complexity Frameworks to Refine Theories of Engagement: Advancing Self-Regulated Learning in the Age of Artificial Intelligence

Autor/inn/en	Hilpert, Jonathan C.; Greene, Jeffrey A.; Bernacki, Matthew
Titel	Leveraging Complexity Frameworks to Refine Theories of Engagement: Advancing Self-Regulated Learning in the Age of Artificial Intelligence
Quelle	In: British Journal of Educational Technology, 54 (2023) 5, S.1204-1221 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Greene, Jeffrey A.) ORCID (Bernacki, Matthew)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0007-1013
DOI	10.1111/bjet.13340
Schlagwörter	Learning Theories; Independent Study; Artificial Intelligence; Biology; Science Achievement; Learning Strategies; Predictor Variables; Grades (Scholastic); Intervention; Technology Uses in Education; Electronic Learning; Markov Processes; Learning Analytics; Learning Management Systems + Suchen Sie Ihr Suchwort? Learning theory; Lerntheorie; Selbststudium; Künstliche Intelligenz; Biologie; Learning methode; Learning techniques; Lernmethode; Lernstrategie; Prädiktor; Notenspiegel; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Markowscher Prozess
Abstract	Capturing evidence for dynamic changes in self-regulated learning (SRL) behaviours resulting from interventions is challenging for researchers. In the current study, we identified students who were likely to do poorly in a biology course and those who were likely to do well. Then, we randomly assigned a portion of the students predicted to perform poorly to a science of learning to learn intervention where they were taught SRL study strategies. Learning outcome and log data (257 K events) were collected from n = 226 students. We used a complex systems framework to model the differences in SRL including the amount, interrelatedness, density and regularity of engagement captured in digital trace data (ie, logs). Differences were compared between students who were predicted to: (1) perform poorly (control, n = 48); (2) perform poorly and received intervention (treatment, n = 95); and (3) perform well (not flagged, n = 83). Results indicated that the regularity of students' engagement was predictive of course grade, and that the intervention group exhibited increased regularity in engagement over the control group immediately after the intervention and maintained that increase over the course of the semester. We discuss the implications of these findings in relation to the future of artificial intelligence and potential uses for monitoring student learning in online environments. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01