TY - JOUR
T1 - Intense, turbulent, or wallowing in the mire
T2 - a longitudinal study of cross-course online tactics, strategies, and trajectories
AU - Saqr, Mohammed
AU - López-Pernas, Sonsoles
AU - Jovanović, Jelena
AU - Gašević, Dragan
N1 - Funding Information:
The paper is co-funded by the Academy of Finland (Suomen Akatemia) Research Council for Natural Sciences and Engineering for the project Towards precision education: Idiographic learning analytics (TOPEILA), Decision Number 350560 which was received by the first author. The First and second authors would also like to thank January Collective for their generous support.
Publisher Copyright:
© 2022 The Authors
PY - 2023/4
Y1 - 2023/4
N2 - Research has repeatedly demonstrated that students with effective learning strategies are more likely to have better academic achievement. Existing research has mostly focused on a single course or two, while longitudinal studies remain scarce. The present study examines the longitudinal sequence of students' strategies, their succession, consistency, temporal unfolding, and whether students tend to retain or adapt strategies between courses. We use a large dataset of online traces from 135 students who completed 10 successive courses (i.e., 1350 course enrollments) in a higher education program. The methods used in this study have shown the feasibility of using trace data recorded by learning management systems to unobtrusively trace and model the longitudinal learning strategies across a program. We identified three program-level strategy trajectories: a stable and intense trajectory related to deep learning where students used diverse strategies and scored the highest grades; a fluctuating interactive trajectory, where students focused on course requirements, scored average grades, and were relatively fluctuating; and a light trajectory related to surface learning where students invested the least effort, scored the lowest grades, and had a relatively stable pathway. Students who were intensely active were more likely to transfer the intense strategies and therefore, they were expected to require less support or guidance. Students focusing on course requirements were not as effective self-regulators as they seemed and possibly required early guidance and support from teachers. Students with consistent light strategies or low effort needed proactive guidance and support.
AB - Research has repeatedly demonstrated that students with effective learning strategies are more likely to have better academic achievement. Existing research has mostly focused on a single course or two, while longitudinal studies remain scarce. The present study examines the longitudinal sequence of students' strategies, their succession, consistency, temporal unfolding, and whether students tend to retain or adapt strategies between courses. We use a large dataset of online traces from 135 students who completed 10 successive courses (i.e., 1350 course enrollments) in a higher education program. The methods used in this study have shown the feasibility of using trace data recorded by learning management systems to unobtrusively trace and model the longitudinal learning strategies across a program. We identified three program-level strategy trajectories: a stable and intense trajectory related to deep learning where students used diverse strategies and scored the highest grades; a fluctuating interactive trajectory, where students focused on course requirements, scored average grades, and were relatively fluctuating; and a light trajectory related to surface learning where students invested the least effort, scored the lowest grades, and had a relatively stable pathway. Students who were intensely active were more likely to transfer the intense strategies and therefore, they were expected to require less support or guidance. Students focusing on course requirements were not as effective self-regulators as they seemed and possibly required early guidance and support from teachers. Students with consistent light strategies or low effort needed proactive guidance and support.
KW - Learning analytics
KW - Learning strategies
KW - Longitudinal studies
KW - Sequence analysis
UR - http://www.scopus.com/inward/record.url?scp=85145972569&partnerID=8YFLogxK
U2 - 10.1016/j.iheduc.2022.100902
DO - 10.1016/j.iheduc.2022.100902
M3 - Article
AN - SCOPUS:85145972569
SN - 1096-7516
VL - 57
JO - Internet and Higher Education
JF - Internet and Higher Education
M1 - 100902
ER -