Web-Based Recommendation System For Ict Strategies Based On Learning Styles And Their Didactics

Abstract

Student dropout in the early semesters of university is a significant challenge in Colombian higher education. This study addresses the issue through the development of LearSty, a web-based recommendation system designed to identify learning styles in first-semester Systems Engineering students at UPCA and recommend appropriate ICT strategies. Using the agile Extreme Programming (XP) methodology, the Kolb learning style test was integrated into the platform. Surveys revealed a high acceptance (92.3%) of these tolos The main learning difficulties were a lack of practice and comprehension issues, each affecting about 38.5% of students. LearSty proved effective, allowing for the personalization of teaching-learning strategies and concluding that diversifying teaching methods and using technology adapted to learning styles can significantly improve academic performance and reduce student dropout rates.