Project aims: We first create predictive Learning Analytics-based models that enable unobtrusive assessment of students’ well-being. Thereafter, we co-design and test theory-guided personalized educational interventions and feedback to strengthen students' academic resilience.

The Corona transition at TU/e led to a very fundamental change in the form of teaching and student learning. All interaction has shifted to online. This has consequences for student learning and staff teaching that we should monitor closely and improve wherever possible.

Currently, Learning Management Systems (LMS) are being used in the majority of educational institutions to provide learning materials online. As a by-product of these systems—every click is recorded—one gets a rich amount of data about students’ online behaviour. Recently many researchers have started to investigate these data. Interpreting and contextualizing data about students, to improve lear…

Online lectures provide many benefits to students. Students are more flexible in when and where they learn, and they can go through the material at their own pace. However, online lectures also provide challenges. Research examining how students learn has revealed that online education critically depends on self-control skills (Schacter & Szpunar, in press). Selfcontrol fosters the ability to sta…

In blended courses of Bachelor’s programs at TU/e, the project combines Learning Analytics and mindset interventions to increase student engagement. It establishes an activating form of blended learning that other teachers can implement by following our online tutorial.