Adaptive MOOCs: A Framework for Adaptive Course based on Intended Learning Outcomes رسالة ماجستير

Abstract

Due to the increasing development in the educational domain, recent trends are pushing towards open learning environments (i.e., Massive Open Online Courses MOOCs) which offer many courses in different domains by a number of the top universities around the world. Accordingly, learners with different backgrounds and experiences around the world are able to browse and follow different online courses. Although the proposed systems to support adaptive MOOCs have many advantages over traditional online learning systems, they still suffer from several obstacles and drawbacks. On the other hand, the richness of courses in MOOcCs could be also a weakness point. For instance, giving the opportunity to different learners to be able to explore a huge number of courses can cause many problems that will not enable learners to get the desired benefits and goals. This is because the courses level is not suitable for the learners or the courses contents which do not match intended learning

(١L٥5)% juently , this is idered as a motivation in academic discussions on e-learning domain to support learners with adaptive online MOOCs based on ILO. This thesis proposes a novel adaptive MOOCs framework to support learners with suitable learning resources in MOOCs by adapting suitable learning resources and arranged them in a way that matches learner’s profile. In particular, this work elaborates on the principles, requirements and models used for delivering adaptive MOOCs courses for classifying learning resources based on i ded learning (ILOs). Additionally, this research proposes a conceptual framework to achieve the adaptation process automatically by

employing Naive Bayesian classifier techniques in MOOCs.

The proposed framework has been constructed and tested using learning materials collected

from free coursera courses. Furthermore, the effectiveness of the used technique has been lidated using a precisi ll indicators and the results were compared with the manual

results. After that, a pilot evaluation was conducted to measure the learners and educators satisfaction of the generated course based on the proposed framework. The results were promising as the precision-recall indicators provided a good results in the classification process. Additionally, the results of the questionnaire showed a good feedback and a positive

impression from the point of view of educators and learners.