Automatic Recommendation of Contents and Reinforcement Activities to Facilitate the Autonomous Learning in a Data Structure and Programming Course

Autonomous learning is increasingly recognized as an effective approach for achieving academic success, particularly in e-learning environments. It offers students greater flexibility, engagement, and motivation. A valuable tool for facilitating autonomous learning and tailoring materials and reinforcement activities to students' needs is a recommendation system based on their questions, doubts, and concerns expressed in the course forum. In this study, we propose the use of advanced natural language processing techniques to design and implement a recommender that supports students in autonomous learning. When a student posts a query in the course forum, our recommender system provides links to other discussion threads where related questions have been raised and additional activities to reinforce the study of topics that have been challenging. We have developed a content-based recommender that utilizes an algorithm capable of extracting key phrases, terms, and embeddings that describe the concepts in the student query and those present in other conversations and reinforcement activities with high precision. The recommender considers the similarity of the concepts extracted from the query and those covered in the course discussion forum and the exercise database to recommend the most relevant content for the student. Our results indicate that we can recommend both posts and activities with high precision (above 80%) using key phrases to represent the textual content.