Knowledge discovery for course choice decision in Massive Open Online Courses using machine learning approaches

Massive Open Online Courses (MOOCs) provide learners with high-quality and flexible online courses with no limitations regarding time and location. Detecting users' behavior in MOOCs is an important task for course recommendations. Collaborative Filtering (CF) is considered the widely approach in recommender systems to provide a online learner courses according to similar learners' preferences in an e-learning environment. The current research provides a novel framework through machine learning techniques to propose course recommendations in MOOCs according to the uses' preferences and behavior. The method is developed using multicriteria ratings extracted from users' online reviews. We use Latent Dirichlet Allocation (LDA) for text mining, Decision Trees for decision rule generation, Self-Organizing Map (SOM) for users' reviews on courses and the fuzzy rule-based system for users' preferences prediction. We also adopt a feature selection method to select the most important criteria for users' preferences prediction. The method is evaluated using the data collected from an online learning platform, Udemy. The results showed that the method is able to accurately provide relevant courses to the users tailored to their preferences. The method has the potential to be implemented as a recommendation agent in the MOOC websites for course recommendations.

Automated summary

Massive Open Online Courses (MOOCs) offer learners high-quality and flexible online courses without limitations of time and location. Identifying users' behavior in MOOCs is crucial for course recommendations. This study proposes a novel framework using machine learning techniques to recommend courses in MOOCs based on users' preferences and behavior.