Learner Models in MOOCs in a Lifelong Learning perspective

Colloque du CRIFPE
Communication orale
Les formations à l’enseignement (initiales et continues), Le numérique éducatif
Nowadays, Learning Analytics is an emerging topic in the Technology Enhanced Learning and the Lifelong Learning fields. Learner Models also have an essential role on the use and exploitation of learner-generated data in a variety of Learning Environments. Many research studies focus on the added value of Learner Models and their importance to facilitate the learners follow-up and the trainers/teachers’ practices in different e-learning environments. Among these environments, we choose Massive Open Online Courses because they represent a reliable and considerable amount of data generated by Lifelong Learners. In this abstract we focus on Learner Modelling in Massive Open Online Courses. To our knowledge, currently there is no research work that addresses the literature review of existing Learner Models for Massive Open Online Courses. This study will allow us to compare and choose the most suitable Learner Model for a Massive Open Online Course in a Lifelong Learning perspective. This chosen model will help us to evaluate and personalize course content according to different learners’ profiles, backgrounds and experiences. This work is dedicated to MOOC designers/providers, pedagogical engineers and researchers who meet difficulties to evaluate MOOCs' learners based on Learning Analytics. This work benefits from the french I-SITE ULNE funding.
  • Sergio RAMIREZ - Université de Lille - Laboratoire CIREL / Trigone
  • Nour EL Mawas - Université de Lille
  • Jean Heutte - Université de Lille - Laboratoire CIREL / Trigone
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Durée de la vidéo
13 minutes

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