Adaptive MOOCs Based on Intended Learning Outcomes Using Naïve Bayesian Technique

Ahmad Ewais, Duaa Abu Samara

Abstract


Widespread adoption of MOOCs got researchers interest to support learners in their learning process. However, most of provided courses are teacher-centered approach rather than learner-centered approach. One of the possible solutions to enhance the learning process is to enable learner to learn a course that achieve a number of Intended Learning Outcomes (ILOs). Therefore, the main goal of this research work is to propose an approach for adapting MOOCs learning materials based on ILOs using classification algorithm namely Naïve Bayesian algorithm. Furthermore, the proposed approach considered the pedagogical aspects by generating a learning path based on the pedagogical relationship between learning concepts which are mapped to learning materials. As a result, the learner will be able to follow a course generated automatically based on selected ILOs and pedagogical relationships. To validate the proposed approach, a prototype has been developed and the effectiveness of the adopted technique has been validated using a precision-recall indicator. The results were promising as the precision-recall indicators provided interesting results in the classification process.

Keywords


MOOCs, Bayesian Network Classifier, Adaptive MOOCs, Intended Learning Outcomes.

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Copyright (c) 2020 Ahmad Ewais, Duaa Abu Samara


International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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