Learning analytics for academic paths: student evaluations of two dashboards for study planning and monitoring

Main Article Content

Anni Silvola
Amanda Sjöblom
Piia Näykki
Egle Gedrimiene
Hanni Muukkonen

Abstract

An in-depth understanding of student experiences and evaluations of learning analytics dashboards (LADs) is needed to develop supportive learning analytics tools. This study investigates how students (N = 140) evaluated two student-facing LADs as a support for academic path-level self-regulated learning (SRL) through the concrete processes of planning and monitoring studies. Aim of the study was to gain new understanding about student perspectives for LAD use on academic path-level context. The study specifically focused on the student evaluations of the dashboard support and challenges, and the differences of student evaluations based on their self-efficacy beliefs and resource management strategies. The findings revealed that students evaluated dashboard use helpful for their study planning and monitoring, while the challenge aspects mostly included further information needs and development ideas. Students with higher self-efficacy evaluated the dashboards as more helpful for study planning than those with lower self-efficacy, and students with lower help seeking skills evaluated the dashboards as more helpful for study monitoring than those with higher help seeking skills. The results indicate that the design of LAD can help students to focus on different aspects of study planning and monitoring and that students with different beliefs and capabilities might benefit from different LAD designs and use practices. The study provides theory-informed approach for investigating LAD use in academic path-level context and extends current understanding of students as users of LADs.

Article Details

How to Cite
Silvola, A., Sjöblom, A., Näykki, P., Gedrimiene, E., & Muukkonen, H. (2023). Learning analytics for academic paths: student evaluations of two dashboards for study planning and monitoring. Frontline Learning Research, 11(2), 78–98. https://doi.org/10.14786/flr.v11i2.1277
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