JMIR Publications

ABSTRACT

Background:

Poor mental health among higher education students is a global public health concern. Learning analytics, which involves collecting and analysing big data to support learning, could detect changes in behaviour, learning patterns, as well as mental health and well-being. This could help inform mental health interventions in university settings. However, research has yet to explore students' perspectives on using learning analytics for mental health and well-being purposes.

Objective:

To explore students’ perspectives on using learning analytics to support students’ mental health and well-being at university.

Methods:

Semi-structured interviews were conducted online using Microsoft Teams between June and July 2023. Participants were identified through university student unions and social media and were purposefully sampled for maximum variation. Three university students aged 20-26 joined our team and formed our student advisory group (SAP). They informed the design, analysis and dissemination stages of the research cycle. Braun and Clarke’s approach guided our thematic analysis. Data was triangulated by comparing codes from two transcripts across two independent researchers over a 2-hour virtual meeting. A coding framework was co-created with the SAP to code the remaining transcripts and ensure data saturation. Themes were finalised and presented in a thematic map during a 2-hour meeting with the SAP and two researchers.

Results:

Fifteen participants were interviewed. We identified three main themes: 1) potential of learning analytics for mental health and well-being innovation, 2) student involvement in decision-making regarding learning analytics, and 3) integration of learning analytics with existing support. Despite being initially unaware, students recognised the potential of using learning analytics as a monitoring and early intervention tool to support university students’ mental health. However, students raised concerns regarding data reliability and identified several ethical issues, such as privacy and lack of transparency. They also expressed the need to be involved in decision-making regarding learning analytics design, practices and policies. Overall, students welcomed the possible integration of learning analytics with the existing university support.

Conclusions:

This is the first qualitative study to explore students’ perceptions of using learning analytics to support student mental health and well-being. Students’ generally positive attitudes towards learning analytics suggest this tool could be effectively integrated into the existing university support systems. Considering the ethical concerns raised by students, our findings suggest the need to bring the student voice into learning analytics development and implementation.