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Challenges
The challenges that the project looked to overcome included:
- Increasing mental health concerns among university students
- Need for early identification of students at risk
- Balancing data use with privacy and ethical considerations
Approach
The project took the following approach to identify students at risk of mental health issues, aiming to provide early support and interventions:
- Data analytics: Developed a system to analyse various data points related to student behaviour and engagement.
- Dashboard creation: Implemented a dashboard to visualise and interpret the data.
- Ethical framework: Established clear guidelines for data use and student privacy.
- Collaboration: Partnered with Jisc and Civica to leverage their expertise in data analytics and education technology.
- Pilot testing: Conducted a trial with a group of students to assess the system’s effectiveness.
Outcomes and benefits
The project demonstrated that predicting student wellbeing with significant accuracy is possible, and mental health analytics identifies more students needing support than educational analytics.
By collecting data, developing predictive models, and using a dashboard with validated wellbeing indicators, staff could centralise information and identify students who required support. Actions included sending tailored messages to guide students to available university resources.
While not fully automated, the process allowed staff to make informed decisions. The team noted a 50% increase in their caseload due to these interventions, showing that more students became aware of available help.
Lessons learnt
Key findings from the evaluation include the need for clean, accurate, centrally available data; analytics enhancing but not replacing expert decision-making; and a shift from project to deliverable service. The report notes that data issues arose during the project, highlighting the importance of ongoing data governance.