E-learning systems provide a wide range of educational resources through which users could enhance their learning. However, such systems tend to burden users with choice overload when vast amounts of material are presented. Recommender systems would enhance the user’s learning experience by providing a select number of items that would be more likely to be of interest to the user. Therefore, users would be less likely to get overwhelmed and would find it easier to continue learning when presented with learning objects catering for their interests.
Three of the most common approaches to recommending items are content-based filtering, collaborative filtering, and hybrid filtering. This study has focused on the collaborative filtering approach, where learning objects would be recommended to a user according to the preferences of similar users. A dataset was created by web scraping the MERLOT repository, an online e-learning system that provides learning materials of various types, such as tutorials, quizzes, and
presentations. In this way, a dataset that would contain users, items and ratings was created and passed as input to the algorithms used for the experimentation phase.
This work experiments with two techniques, the k-nearest neighbor and matrix factorization. The former finds the most similar user to the target user through mathematical functions called similarity metrics, such as cosine similarity, while the latter technique computes matrix
multiplications to find the relationships between users and items. For a given user and item, both techniques used have the capability to predict the rating of the user for that specific item.
This work experiments with three variations of the KNN and two variations of the MF technique. For each technique, a set of different parameters was used to test the respective variations. One of the MF variations obtained the best results.
repository
Student: Jonathan Vella
Course: B.Sc. IT (Hons.) Software Development
Supervisor: Dr Conrad Attard