LSBCTR: A Learning Style-Based Recommendation Algorithm
Thayron C. H. Moraes, Itana Stiubiener, Juliana Cristina Braga, Edson P. Pimentel · 2020
This Research Full Paper presents a hybrid algorithm for the recommendation of Learning Objects (LO) aimed at students' learning profiles. In this sense, the Learning Styles-based Collaborative Topic Recommender (LSBCTR) algorithm was developed based on the Collaborative Topic Regression (CTR) model, a hybrid recommendation algorithm that combines a method of Collaborative Filtering (CF) and probabilistic topic modeling. The Learning Style is incorporated into the CTR to predict LO classification. The proposed model controls which classifications are more effective in the students' learning process and which LO recommendations fit better to the student's learning profile. Experiments were carried out in a real-world dataset collected from a Virtual Learning Environment (VLE) that was based on the inventory proposed by Felder and Soloman.