Tracking Patterns in Self-Regulated Learning Using Students’ Self-Reports and Online Trace Data

Nicolette van Halem*, Chris van Klaveren, H.J. Drachsler, Marcel Schmitz, Ilja Cornelisz

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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.
Original languageEnglish
Pages (from-to)140-163
Number of pages24
JournalFrontline Learning Research
Issue number3
Publication statusPublished - 30 Mar 2020


  • Event-Based Measures
  • Online Trace Data
  • Self-Regulated Learning
  • Self-Report Measures


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