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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%.