Towards an Adaptive Learning Framework for MOOCs

Abstract

Massive Open Online Courses (MOOCs) are a new shaking development in higher education. They combine openness and scalability in a most energetic way. They have the capacity to broaden participation in higher education. In this way, they help to achieve social inclusion, the dissemination of knowledge and pedagogical innovation and also the internationalization of higher education institutions. However, one of the most essential elements for a massive open language learning experience to be efficient is to enhance learners and to facilitate networked learning experiences. In fact, MOOCs are meant to serve an undefined number of participants, thus serving a high heterogeneity of profiles, with various learning styles and schemata, and also contexts of contribution and diversity of online platforms. Personalization can play a primary role in this process. Accordingly, adaptive MOOCs use adaptive techniques so as to present personalized learning experiences, having as basis dynamic assessment and data collecting on the course. They count on networks of prerequisites and deal with learners according to their different personalized paths through the content. This has been described by the Gates Foundation as an essential novelty in the area for large-scale productivity in online courses. Analytics are also to be credited with bringing about change and improvement of the course in the future. This paper looks into the MOOCs system by reviewing the available literature, spotting the various limitations of traditional MOOC system and suggesting a proposed framework for adaptive MOOCs based on hybrid techniques. By so doing, we generate suggestions of learning paths adapted to the competences profile of each participant with a focus on objectives, such as reducing the rate of dropout and improving MOOCs quality.