Adapted Regulation Level’s Flipped Classroom using Educational Data-mining

Abstract

Adaptation and individualization of learning is a major challenge when using flipped class as a teaching method. In this paper, we propose a recommendation system for flipped classroom to individualize learning in the classroom based on Data Mining algorithms. This system allows the teacher to predict a classification of learners before administering the tasks to be accomplished and the adapted teaching resources, using attributes related to the activity logs on the e-learning platform, to the online evaluations (Quiz) and to demographic data. The results show that the use of this model as a learning strategy optimizes the time of learning and improves the learner's performance.

Design of Repository and Search Platform for Art Painting Teaching Resources in Universities Based on Model of Decision Tree

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“…And the generated branching points are split by the same method until the resulting child nodes of the split are all leaf nodes labeled with classes. e test cost for each attribute was randomly assigned between [20,90], and the cost and gain matrices for each data set were set by reference to manual settings. e test results based on the information gain rate criterion are shown in Table 1.…”

Experimentation of ICT for the Benefit of French as a Foreign Language Teachers

“…During the lockdown, we observed a lot of improvisation in decisionmaking, resources and practices implemented to ensure educational continuity. For this reason, during the last Covid-19 health crisis, the use of computer tools and distance learning allowed us to maintain educational continuity and save the school year despite the constraints we faced (Mimis et al, 2018).…”