Abstract
Since COVID-19, technology's rapid growth and remote learning's increased demand have led to the evolution of online education. Platforms like Udemy, Coursera, and Skillshare offer free and paid courses. However, the abundance of scattered content overwhelms learners. Amidst hundreds of studies on E-Learning websites, self-learners need a specific learning plan and procedures tailored to everyone’s abilities. Recognizing this need, we aim to enhance existing online learning systems by constructing an advanced recommendation system that elevates the personalized learning experience for individuals. We employ a method of community detection within social networks to segment the programming knowledge dataset into specific communities. Subsequently, based on the user's existing skills, we place each user in the right neighborhood for them and provide a roadmap accordingly. Algorithms used for clustering include Louvain, SLPA, LFM, and combined forms like SLPA-Louvain, SLPA-LFM, and LFM-Louvain. Closeness centrality measures are also used to propose related courses.
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This research is funded by Vietnam National University HoChiMinh City (VNU-HCM) under grant number DS2022–26-03.
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Thuan, N.D., Quan, T.M., An, D.N.P., Nhut, N.M. (2023). Community Detection for Personalized Learning Pathway Recommendations on IT E-Learning System. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2023. Communications in Computer and Information Science, vol 1925. Springer, Singapore. https://doi.org/10.1007/978-981-99-8296-7_45
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DOI: https://doi.org/10.1007/978-981-99-8296-7_45
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