Gradual Learners' Assessment in Massive Open Online Courses Based on ODALA Approach

Gradual Learners' Assessment in Massive Open Online Courses Based on ODALA Approach

Haddadi Lynda, Farida Bouarab-Dahmani
Copyright: © 2019 |Pages: 23
DOI: 10.4018/JITR.2019070102
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Abstract

This article proposes a gradual assessment process combined with an adequate learner modelling based on ODALA approach that can be an effective add-on for massive open online course (MOOC) platforms and engineering. The proposed learner modelling includes five dimensions: general information, disciplinary cognitive state, learning styles, preferences and behavior. This article focuses on the cognitive state dimension that is based on an assessment pyramid with four levels: closed-ended questions, half-open questions, open-ended questions and problem situations. The assessment pyramid is the backbone of the learning process since it needs a gradual progression with an adequate methodology where various computer aided or completely automated evaluation activities are proposed. The transition from a level to another is a conditional one since there are minimal threshold of disciplinary knowledge acquisition. An evaluation prototype was tested with the algorithmic discipline and developed so as to access the feasibility of our proposition.
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Based on research work conducted so far, in this section we give an overview about learner assessment and learners modelling, especially in MOOCs.

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