Open AccessA Multi-Layers Perceptron for predicting weekly learner commitment in MOOCs
Author(s) -
Youssef Mourdi,
Mohammed Sadgal,
Hamada El Kabtane,
Hasna El Alaoui El Abdallaoui
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1743/1/012027
Subject(s) - perceptron , computer science , complement (music) , set (abstract data type) , face (sociological concept) , artificial intelligence , machine learning , drop out , multilayer perceptron , field (mathematics) , order (exchange) , mathematics education , data science , psychology , mathematics , artificial neural network , social science , biochemistry , chemistry , finance , complementation , sociology , economics , gene , phenotype , pure mathematics , programming language , demographic economics
Since they were first set up in 2008, MOOCs have continued to integrate very deeply into the distance learning field. They have been adopted by a very large number of universities in order to complement face-to-face learning and thus remedy the massive number of students that the infrastructures can no longer support. In spite of the investments made for their development, MOOCs suffer from a huge drop-out rate of around 90%. This problem creates a number of difficulties for the instructors, including monitoring learners and group formation. In order to help the instructors to identify learners at risk of dropping out, this paper presents a model based on Multi-Layer Perceptron (MLP) that provides weekly predictions of each learner's engagement based on their behaviour. Our model has been tested on a data set of 3585 learners and has shown a high ability to identify this type of learner with an average accuracy of 90.3%.