TY - JOUR
T1 - Network analytics to unveil links of learning strategies, time management, and academic performance in a flipped classroom
AU - Raković, Mladen
AU - Uzir, Nora’Ayu Ahmad
AU - Matcha, Wannisa
AU - Eagan, Brendan
AU - Jovanović, Jelena
AU - Shaffer, David Williamson
AU - Pardo, Abelardo
AU - Gašević, Dragan
N1 - Funding Information:
This work is in part supported by funding from Deutsche Forschungsgemeinschaft, Nederlandse Organisatie voor Wetenschap-pelijk Onderzoek, the Economic and Social Research Council of the United Kingdom (BA 2044/10–1, GA 2739/1-1, MO 2698/1-1) through Open Research Area (Call 5), the Australian Research Council (DP220101209), and the Jacobs Foundation (CELLA 2 CERES).
Publisher Copyright:
© 2023, Society for Learning Analytics Research (SOLAR). All rights reserved.
PY - 2023/11/1
Y1 - 2023/11/1
N2 - Preparatory learning tasks are considered critical for student success in flipped classroom courses. However, less is known regarding which learning strategies students use and when they use those strategies in a flipped classroom course. In this study, we aimed to address this research gap. In particular, we investigated mutual connections between learning strategies and time management, and their combined effects on students’ performance in flipped classrooms. To this end, we harnessed a network analytic approach based on epistemic network analysis (ENA) to analyze student trace data collected in an undergraduate engineering course (N = 290) with a flipped classroom design. Our findings suggest that high-performing students effectively used their study time and enacted learning strategies mainly linked to formative and summative assessment tasks. The students in the low-performing group enacted less diverse learning strategies and typically focused on video watching. We discuss several implications for research and instructional practice.
AB - Preparatory learning tasks are considered critical for student success in flipped classroom courses. However, less is known regarding which learning strategies students use and when they use those strategies in a flipped classroom course. In this study, we aimed to address this research gap. In particular, we investigated mutual connections between learning strategies and time management, and their combined effects on students’ performance in flipped classrooms. To this end, we harnessed a network analytic approach based on epistemic network analysis (ENA) to analyze student trace data collected in an undergraduate engineering course (N = 290) with a flipped classroom design. Our findings suggest that high-performing students effectively used their study time and enacted learning strategies mainly linked to formative and summative assessment tasks. The students in the low-performing group enacted less diverse learning strategies and typically focused on video watching. We discuss several implications for research and instructional practice.
KW - Learning analytics
KW - learning strategies
KW - self-regulated learning
KW - time management
UR - http://www.scopus.com/inward/record.url?scp=85180697277&partnerID=8YFLogxK
U2 - 10.18608/jla.2023.7843
DO - 10.18608/jla.2023.7843
M3 - Article
AN - SCOPUS:85180697277
SN - 1929-7750
VL - 10
SP - 64
EP - 86
JO - Journal of Learning Analytics
JF - Journal of Learning Analytics
IS - 3
ER -