Authors:
Karima Boussaha
1
;
Mouhamed Beggas
2
and
Khalil Khoualdi
2
Affiliations:
1
Department of Computer Science, Research Laboratory ond Computer Science’s Complex Systems (ReLa(CS)2), University of Oum El Bouaghi, Algeria
;
2
Department of Computer Science, University of Oum El Bouaghi, Algeria
Keyword(s):
Massive Open Online Course (MOOC), Practical Work MOOC, Learner Motivation, Social Cognitive View, Computer Programming Language Practical Works, Bloom’s Pyramid, Grimard’s Pyramid.
Abstract:
Due to the outbreak of the coronavirus pandemic and the total confinement imposed on all countries to prevent the spread of the virus, Massive open online courses (MOOC) systems have been widely used in recent years, and have attracted more attention in educational institutions, especially. But MOOCs intended for learning practical work have not been adequately addressed. However, everyone knows that the chances of dropping out of MOOCs are very high compared to conventional offline courses. Researchers have implemented extensive and diverse methods to determine the reasons behind learner attrition or lack of interest to apply timely interventions. We decided to address the dropout problem due to the lack of motivation among learners, with special practical works MOOCs. We have hybridized two methodologies: cognitive levels of learners, namely, Bloom’s taxonomy and Grimard’s pyramid for motivation this hybridization allowed us to create a new categorization for practical works, and w
e propose a new MOOC for learning practical works activities for programming languages in computer science. The main objective of this MOOC platform is to automatically generate practical works of different levels of complexity to be solved according to the level of motivation related to the learner. It composed into three principal components: IMMS survey component, motivation component, and practical works generator component.
(More)