Investigating SMART Models of Self-Regulation and Their Impact on Learning [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (14th, Online, Jun 29-Jul 2, 2021).

Autor/inn/en	Hutt, Stephen; Ocumpaugh, Jaclyn; Ma, Juliana; Andres, Alexandra L.; Bosch, Nigel; Paquette, Luc; Biswas, Gautam; Baker, Ryan S.
Titel	Investigating SMART Models of Self-Regulation and Their Impact on Learning [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (14th, Online, Jun 29-Jul 2, 2021).
Quelle	(2021), (8 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Models; Self Control; Learning Strategies; Student Behavior; Educational Environment; Simulated Environment; Artificial Intelligence; Middle School Students; Grade 6; Prediction + Suchen Sie Ihr Suchwort? Analogiemodell; Selbstbeherrschung; Learning methode; Learning techniques; Lernmethode; Lernstrategie; Student behaviour; Schülerverhalten; Lernumgebung; Pädagogische Umwelt; Schulumwelt; Künstliche Umwelt; Künstliche Intelligenz; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; School year 06; 6. Schuljahr; Schuljahr 06; Vorhersage
Abstract	Self-regulated learning (SRL) is a critical 21st -century skill. In this paper, we examine SRL through the lens of the searching, monitoring, assessing, rehearsing, and translating (SMART) schema for learning operations. We use microanalysis to measure SRL behaviors as students interact with a computer-based learning environment, Betty's Brain. We leverage interaction data, survey data, "in situ" student interviews, and supervised machine learning techniques to predict the proportion of time spent on each of the SMART schema facets, developing models with prediction accuracy ranging from "rho" = 0.19 for translating to "rho" = 0.66 for assembling. We examine key interactions between variables in our models and discuss the implications for future SRL research. Finally, we show that both ground truth and predicted values can be used to predict future learning in the system. In fact, the inferred models of SRL outperform the ground truth versions, demonstrating both their generalizability and their potential for using these models to improve adaptive scaffolding for students who are still developing SRL skills. [For the full proceedings, see ED615472.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01