Part of the
4TU.
Centre for
Engineering Education
TU DelftTU EindhovenUniversity of TwenteWageningen University
4TU.
Centre for
Engineering Education
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4TU.Federation

+31(0)6 48 27 55 61

secretaris@4tu.nl

Website: 4TU.nl

Project introduction and background information

The problem? Student well-being has come under pressure in recent years and the pandemic situation, combined with the trend towards more blended and online learning, made this issue more salient. At TU/e and other Dutch universities, study stress and a high workload create the need to monitor well-being more efficiently and to empower students to manage their resilience.

The deliverables? First, we produce predictive models, data scripts, and insights based on students’ online behavior that facilitate the regular monitoring of student well-being. Second, we design personalized interventions that improve existing “one-size-fits-all” interventions and that can easily be scaled up.

How to generate them? In Phase I, we make use of process-mining techniques to create predictive models that allow unobtrusive assessment of students’ well-being. In Phase II, we use co-design approaches with students to iteratively design and test theory-guided personalized educational interventions and feedback, to strengthen students’ academic resilience.

Objective and expected outcomes

The project will produce intervention tools, predictive models, and insights that facilitate

● Unobtrusive assessments of students’ well-being (potentially in all TU/e Canvas courses) in an automatic and efficient way and

● Empowerment of students to maintain or improve their academic resilience and learning outcomes in a personalized way, given indicators of poor or declining student well-being.

Through the extension of ICT-enhanced education, the project makes an important contribution to realizing one of the educational goals of the TU/e education vision 2030. Of particular interest is the issue of whether and to what extent assessment and support of student well-being and resilience need to be tailored to “traditional” engineering versus other students (e.g., Psychology & Technology students at IE&IS).

Results and learnings

To be announced

Recommendations

To be announced

Practical outcomes

To be announced