MORAIS CANELLAS Camila

PhD student at Sorbonne University
Team : MOCAH
https://lip6.fr/Camila.Canellas

Supervision : Vanda LUENGO

Co-supervision : BOUCHET François

A Learning Analytics Metamodel

This work is part of a process of implementing a learning analytics process in a context where documentary production is carried out via an engineering approach driven by models. We are mainly interested in the possibilities that could emerge if the same approach is used in order to achieve such an implementation. Our problematic concerns the identification of these possibilities, in particular by ensuring to allow, via the proposed metamodel, the enrichment of learning indicators with the semantics and the structure of the educational documents consulted by the learners, as well as an a priori definition of relevant indicators.
In order to design the metamodel in question, we first carried out an exploratory study with learners, aimed at knowing their needs and the reception of enriched indicators. On the other hand, we carried out a systematic review of the literature of existing interaction indicators in the field of learning analytics in order to gather the potential elements to be abstracted for the construction of the corresponding metamodel. The challenge was to design a metamodel where the elements necessary for the abstraction of this domain are present without being unnecessarily complex, making it possible to model both learning indicators based on a descriptive analysis and those used for a prediction or a diagnosis. We then proceeded to a proof of concept and an evaluation of this metamodel.

Defence : 11/08/2021

Jury members :

Nathalie GUIN. Maître de conférences HDR. Université Lyon 1 [Rapporteure]
Serge GARLATTI. Professeur des universités. IMT Atlantique [Rapporteur]
Tewfik ZIADI. Maître de conférences HDR. Sorbonne Université.
Sébastien IKSAL. Professeur des universités. Université du Mans.
Vanda LUENGO. Professeur des universités. Sorbonne Université
François BOUCHET. Maître de conférences. Sorbonne Université

Departure date : 12/31/2021

2020-2022 Publications