Data-driven education through learning analytics and machine learning (2020-2023). Funded by VR (Swedish Research Council). Project members: Jalal Nouri, Panos Papapetrou, Thashmee Karunaratne, Mohammed Saqr.
Learning analytics and digital assessment (2018-2019). Financed by Stockholm University.
This smaller project explores how learning analytics can be applied to understand performance of students when doing digital assessments and how learning analytics can inform the development and allignment of digital assessment with design of learning activities.
Thesis analytics (2018-2019). Financed by Stockholm University.
In this project, based on large data samples and by employing machine learning techniques we study droputout behaviour and construct predictive models that helps us identify factors that contribute to dropouts and performance of students when writing their final thesis.
Learning analytics in higher education (2018-2019). Financed by Stockholm University.
In this project, we study how learning analytics can be applied to understand and predict dropout behavior in large online courses, as well as how we can design artificially intelligent dashboards that help students to regulate their learning and help teachers intervene in time.
Using Learning Analytics to Understand and Support Collaborative Learning. 2016-2018.
In this project, we studied how learning analytics, social network analysis, and machine learning methods can be used to understand and support collaborative learning in online environments. See publications for more information.