Towards a modular and flexible Learning Analytics framework

Yves Noel (1, 2) , François Bouchet (1, 2) , Roland Mergoil (2) , Vanda Luengo (1, 2)

1 MOCAH - Modèles et Outils en ingénierie des Connaissances pour l'Apprentissage Humain
2 SU - Sorbonne Université

This paper introduces a Learning Analytics platform which aims at being modular, evolving and flexible. The general framework architecture is completely independent from the digital systems to which it is connected. It collects learning data of various origins in data storages. Then it extracts a subset of the data which is aggregated into a data warehouse. Finally, these data are processed through various algorithms. Such a framework reinforces the control of data integrity in an experimental context and allows the students to refine the authorizations they give about their data. These data processing lead to indicators that will be used in student and teacher dashboards allowing a clear and fast access to learning information. In a second step, the platform will compute student profiles, facilitating the design of adaptive courses for each student.

Mots clés: Modular framework; Trusted learning analytics; Student consent

Domaines: Environnements Informatiques pour l'Apprentissage Humain

Yves Noel, François Bouchet, Roland Mergoil, Vanda Luengo. Towards a modular and flexible Learning Analytics framework. Learning Analytics and Knowledge 2020, SOLAR, Mar 2020, Frankfurt, Germany. pp.178-179. ⟨hal-03005330⟩