A New Competence-based Approach for Personalizing MOOCs in a Mobile Collaborative and Networked Environment

• Main Authors: António Teixeira, Antonio Garcia-Cabot, Eva Garcia-Lopez, José Mota, Luis de-Marcos
• Format: Article
• Language: English
• Published: Asociación Iberoamericana de Educación Superior y a Distancia (AIESAD) 2015-09-01
• Series: RIED: Revista Iberoamericana de Educación a Distancia
• Subjects: framework; adaptation; personalization; learning paths; mobile devices; MOOC
• Online Access: http://revistas.uned.es/index.php/ried/article/view/14578
• ISSN: 1138-2783; 1390-3306

Massive Open Online Courses (MOOCs) are a new disruptive development in higher education that combines openness and scalability in a most powerful way. They have the potential to widen participation in higher education. Thus, they contribute to social inclusion, the dissemination of knowledge and pedagogical innovation and also the internationalization of higher education institutions. However, one of the critical elements for a massive open language learning experience to be successful is to empower learners and to facilitate networked learning experiences. In fact, MOOCs are designed for an undefined number of participants, thus serving a high heterogeneity of profiles, with diverse learning styles and prior knowledge, and also contexts of participation and diversity of online platforms. Personalization can play a key role in this process. The iMOOC pedagogical model introduced the notion of diversity to MOOC design, allowing for a clear differentiation of learning paths and also virtual environments. In this article, the authors present a proposal based on the iMOOC approach for a new framework for personalizing and adapting MOOCs designed in a collaborative, networked pedagogical approach by identifying each participant's competence profile and prior knowledge, as well as the respective mobile communication device used to generate matching personalized learning. This article also shows the results obtained in a laboratory environment after an experiment has been performed with a prototype of the framework. It can be observed that creating personalized learning paths is possible and the next step is to test this framework with real experimental groups.