Higher education institutions are facing growing pressure to maintain and strengthen the well-being of students. It is crucial to ensure that students' mental health is not compromised in the pursuit of knowledge and skills. Unfortunately, the reality is that student well-being in higher education is currently at risk, and the COVID-19 pandemic has only worsened the situation.
In response to these challenges, this Ph.D. project - conducted by Ran Zhang under supervision of Chris Snijders, Uwe Matzat, and Rianne Conijn (all TU/e Human-Technology Interaction Group) - strives for two helpful measures:
- The development of Learning Analytics-based tools that enable the unobtrusive and (potentially) continuous assessment of students’ well-being in ongoing courses through predictive models
- The design of personalized scalable (semi-automatic) web-based interventions that empower students to improve their well-being and academic resilience and that can be tailored to specific fields of study.
The ultimate goal is to guide preventive measures, implement effective interventions, and inform the design of educational practices to support student well-being.
Insights so far: Phase 1 of the project aims to determine the feasibility of gaining insights into students' well-being through their online learning behavior, specifically using Canvas clickstream data. The pilot study conducted as part of this phase indicates that it is indeed possible to predict several specific well-being constructs that indicate students’ academic resilience through their clickstream data. The first preliminary findings provide promising evidence for the potential of using Learning Analytics for the unobtrusive assessment of student well-being.
About the PhD student: Ran was born and raised in China. She received her master degree in clinical psychology in 2022 and cares deeply about improving the mental well-being of those in need.
For more details please visit the project website.