Using Temporal Analytics to Detect Inconsistencies between Learning Design and Student Behaviours

Nguyen, Quan; Huptych, Michal and Rienties, Bart (2018). Using Temporal Analytics to Detect Inconsistencies between Learning Design and Student Behaviours. Journal of Learning Analytics, 5(3) pp. 120–135.

DOI: https://doi.org/10.18608/jla.2018.53.8

Abstract

Extensive research in learning science has established the importance of time management in online learning. Recently, learning analytics (LA) has shed further lights on the temporal characteristics of learning by allowing researchers to capture authentic digital footprints of student learning behaviours. Nonetheless, students’ timing of engagement and its relation to learning design (LD) and academic performance have received limited attention. This study investigates to what extent students’ timing of engagement aligned with instructor learning design, and how engagement varied across different levels of performance. Our findings revealed a mismatch between how instructors designed for learning and how students study. In most weeks, students spent less time studying the assigned materials on the virtual learning environment (VLE) compared to the number of hours recommended by instructors. The timing of engagement also varied, from in advance to catching up patterns. High-performing students spent more time studying in advance, while low-performing students spent a higher proportion of their time on catching-up activities. By incorporating the pedagogical context into learning analytics, not only we can understand what, why, and when students engage, but also how their behaviours are influenced by the way instructors design for learning.

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