A literature review on Learner Models for MOOC to support Lifelong Learning

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 learner's follow-up, the course content personalization and the trainers/teachers' practices in different 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 paper we focus on Learner Modelling in Massive Open Online Courses in an Lifelong Learning context. To our knowledge, currently there is no research work that addresses the literature review of existing Learner Models for Massive Open Online Courses in this context in the last five years. This study will allow us to compare and highlight features in existing Learner Models for a Massive Open Online Course from a Lifelong Learning perspective. This work is dedicated to MOOC designers/providers, pedagogical engineers and researchers who meet difficulties to model and evaluate MOOCs' learners based on Learning Analytics.