Abstract
Despite the massive number of enrollments in MOOC (Massive Open Online Course) platforms, dropout rates are very high. This problem can be due to several factors: Social, pedagogical, prior knowledge as well as a demotivation. To deal with this type of problems, we have designed an adaptive cMOOC (Connectivist MOOC) platform for each registered learner’s profile.
 From the first human-machine interaction, the process adapts the learner's need according to a pre-established model. It is based on the processing of statistical data collected by correspondence analysis and regression algorithms. Each generated learner’s profile will provide an adaptive navigation and pedagogical activities. The intelligent system presented in this work will be able to classify learners according to their preferences and learning styles.
Highlights
Learning is getting easier with digital resources deployed on the web [1]
To reach the main objective, we shall respond to the following research questions: RQ1: What are the minimum inputs to request from Abdelmalek Essaâdi University of Tetuan (AEU) students, to build an adaptive Connectivist MOOCs (cMOOCs) to their needs? RQ2: How do the selected factors influence the interest in collaborative learning among AEU students? RQ3: How will the machine learn to provide an adaptive navigation, taking into account each learner’s needs? The answers to these questions will allow us to build an adaptive model to each profile
To answer the question RQ1, we started by targeting the population studied through a survey, where eight parameters were considered in this initial stage: Sex, Age category, professional situation, establishment of the AEU, diploma obtained, field of study, comfort with technology and previous knowledge about MOOC
Summary
Learning is getting easier with digital resources deployed on the web [1]. Among these accessible resources we find MOOCs (Massive Online Open Courses) [2]. By MOOC developing, universities have facilitated and accelerated access to high-level learning, free or at a very low cost. The completion rate does not exceed 7% [3]. This rate is approved by the Software Engineering course offered by the University of California Berkeley on the Coursera platform; for more than 50 000 subscribers, only 7% of them were able to complete their courses [4,5]
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