Prediction Method based DRSA to Improve the Individual Knowledge Appropriation in a Collaborative Learning Environment: Case of MOOCs
Prediction Method based DRSA to Improve the Individual Knowledge Appropriation in a Collaborative Learning Environment: Case of MOOCs
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Date
2017-01-04
Authors
Bouzayane, Sarra
Saad, Inès
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Abstract
This paper proposes a prediction method that relies on the Dominance-based Rough Set Approach (DRSA) to improve the individual knowledge appropriation when the learning process occurs in a collaborative environment such as the Massive Open Online Courses (MOOCs). This method is based on two phases: the first has to be applied at the end of each week of the MOOC and aims at inferring a preference model resulting in a set of decision rules; the second is applied at the beginning of each week of the same MOOC and consists of classifying each learner in one of the three defined decision classes, which are Cl1 of the “At-risk Learners”, Cl2 of the \ “Struggling Learners” and Cl3 of the “Leader Learners”, based on the previously inferred preference model. This method runs weekly. It has \ been validated on real data of a French MOOC proposed by a Business School in France.
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DRSA,
Knowledge appropriation,
Learning,
MOOC,
Prediction method
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10 pages
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Proceedings of the 50th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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