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

• Main Authors: Nicolette van Halem (Author), Chris van Klaveren (Author), Hendrik Drachsler (Author), Marcel Schmitz (Author), Ilja Cornelisz (Author)
• Format: Book
• Published: EARLI, 2020-03-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.14786/flr.v8i3.497; 2295-3159
• Keywords: Education; L
• Source: Frontline Learning Research, Vol 8, Iss 3 (2020)

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.