Distance education (E-Learning) is experiencing significant, rapid and con-tinuous evolution all over the world, especially with the arrival of the covid-19 pandemic. MOOCs are considered as a personal learning process, which are addressed to a massive and varied number of learners. The problem of the free opening MOOCs puts us in front of a massive number of registrants, which means a large number of heterogeneous profiles, which makes the teacher's task more complicated, either in terms of follow-up or framing. As a solution to this problem, in this present work, we propose an approach that allows the classification and categorization of learner profiles via an intelli-gent and autonomous system developed on the basis of neural networks and in particular the self-organizing map (SOM). This approach which is based on the traceability of learners, allowed us to get homogenous groups in order to direct them towards MOOCs that meet their characteristics and needs.
The tests carried out have shown that our approach is efficient in terms of classification and grouping of profiles, which allows us to manage a large number of learners either at the level of the choice of relevant contents or during the evaluation process.
Tracking the evolution of learners' learning in a MOOC supports the e-learning operation and allows teachers to easily manage the massive number of learners enrolled in a distance learning course. In this work we started with a study where we were interested in identifying the common parameters that allow us to have a vision on the evolution of learners through the use of SPSS statistical software. This operation allowed us to determine the level of the learners, classify them and group them into homogeneous groups that facilitated their orientation towards courses that meet the characteristics of their profiles. On the basis of our case study, we were able to develop a computer system approach based on K-means learning software and data pre-processing means, for data mining with the aim of analyzing and revealing the parameters that have a great positive impact on the learners' learning, the system uses the identified parameters to classify and group the learners according to their profiles. This type of system is characterized by its autonomy and the ability to process a large amount of data. On the basis of the data used in our case study, we carried out experimental tests on the proposed system which showed its performance in solving our problem.
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