Implementation of a Machine Learning-Based MOOC Recommender System Using Learner Motivation Prediction

The phenomenon of high dropout rates has been the concern MOOC providers and educators since emergence this disruptive technology in online learning. This led to focus on learner motivation studies from different aspects: demotivation signs detection, learning path personalization, course recommendation, etc. Our paper aims predict for MOOCs select right learner. So, we an educational data mining approach by extracting preprocessing learners' navigation traces a platform building machine model that predicts accurately given MOOC. comparison performance four supervised algorithms resulted selection random forest classifier as modeling technique prediction. Afterward, Machine Learning-based recommendation function was tested learners dataset recommend Top-10 suitable target Finally, further research characteristics considered recommender systems could enlarge scope maintain motivation.