Towards Actionable Learning Analytics Using Dispositions

Autor/inn/en	Tempelaar, Dirk T.; Rienties, Bart; Nguyen, Quan
Titel	Towards Actionable Learning Analytics Using Dispositions
Quelle	In: IEEE Transactions on Learning Technologies, 10 (2017) 1, S.6-16 (11 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1939-1382
DOI	10.1109/TLT.2017.2662679
Schlagwörter	Student Behavior; Integrated Learning Systems; Personality; Educational Research; Data Collection; Data Analysis; Foreign Countries; Introductory Courses; Blended Learning; College Freshmen; Mathematics Instruction; Multivariate Analysis; Prediction; Models; Student Attitudes; Student Motivation; Learner Engagement; Goal Orientation; Learning Processes; Learning Strategies; Psychological Patterns; Academic Achievement; Computer Uses in Education; Netherlands + Suchen Sie Ihr Suchwort? Student behaviour; Schülerverhalten; Personalität; Bildungsforschung; Pädagogische Forschung; Data capture; Datensammlung; Auswertung; Ausland; Einführungskurs; Studienanfänger; Mathematics lessons; Mathematikunterricht; Multivariate Analyse; Vorhersage; Analogiemodell; Schulische Motivation; Zielorientierung; Zielvorstellung; Learning process; Lernprozess; Learning methode; Learning techniques; Lernmethode; Lernstrategie; Schulleistung; Computernutzung; Niederlande
Abstract	Studies in the field of learning analytics (LA) have shown students' demographics and learning management system (LMS) data to be effective identifiers of "at risk" performance. However, insights generated by these predictive models may not be suitable for pedagogically informed interventions due to the inability to explain why students display these behavioral patterns. Therefore, this study aims at providing explanations of students' behaviors on LMS by incorporating dispositional dimensions (e.g., self-regulation and emotions) into conventional learning analytics models. Using a combination of demographic, trace, and self-reported data of eight contemporary social-cognitive theories of education from 1,069 students in a blended introductory quantitative course, we demonstrate the potential of dispositional characteristics of students, such as procrastination and boredom. Our results highlight the need to move beyond simple engagement metrics, whereby dispositional learning analytics provide an actionable bridge between learning analytics and educational intervention. (As Provided).
Anmerkungen	Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01