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Titel
Tracking patterns in self-regulated learning using students' self-reports and online trace data
Autoren
OriginalveröffentlichungFrontline learning research 8 (2020) 3, S. 140-163 ZDB
Dokument Volltext (externe Quelle)
Lizenz des Dokumentes Lizenz-Logo 
Schlagwörter (Deutsch)
Teildisziplin
DokumentartAufsatz (Zeitschrift)
ISSN2295-3159; 22953159
SpracheEnglisch
Erscheinungsjahr
BegutachtungsstatusPeer-Review
Abstract (Englisch):For decades, self-report instruments - which rely heavily on students' perceptions and beliefs - have been the dominant way of measuring motivation and strategy use. An event-based measure based on online trace data arguably has the potential to remove analytical restrictions of self-report measures. The purpose of this study is therefore to triangulate constructs suggested in theory and measured using self-reported data with revealed online traces of learning behaviour. The results show that online trace data of learning behaviour are complementary to self-reports, as they explained a unique proportion of variance in student academic performance and reveal that self-reports explain more variance in online learning behaviour of prior weeks than variance in learning behaviour in succeeding weeks. Student motivation is, however, to a lesser extent captured with online trace data, likely because of its covert nature. In that respect, it is of importance to recognize the crucial role of self-reports in capturing student learning holistically. This manuscript is 'frontline' in the sense that event-based measurement methodologies using online trace data are relatively unexplored. The comparison with self-report data made in this manuscript sheds new light on the added value of innovative and traditional methods of measuring motivation and strategy use. (DIPF/Orig.)
Eintrag erfolgte am14.10.2020
QuellenangabeHalem, Nicolette van; Klaveren, Chris van; Drachsler, Hendrik; Schmitz, Marcel; Cornelisz, Ilja: Tracking patterns in self-regulated learning using students' self-reports and online trace data - In: Frontline learning research 8 (2020) 3, S. 140-163 - DOI: 10.14786/flr.v8i3.497
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