Abstract
The paper addresses the problem of academic course prerequisites and learning outcomes generation in learning analytics systems. For prerequisites generation, collaborative filtering, i.e., ALS algorithm for Matrix Factorization, is used. For learning outcomes generation, the study discusses an approach based on Computational Linguistics data extraction methods and content-based filtering to recommend potential outcomes. The recommendation mechanisms are designed to be implemented in the Educational Program Maker service for working with education process elements. The study's primary goal is to simplify, formalize and speed up the course development process. Implementation of the approach will make it possible to build unambiguous interdisciplinary connections, identify the closest intersections of the curriculum courses, and build individual learning pathways.
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Shnaider, P., Chernysheva, A., Khlopotov, M., Babayants, C. (2023). Generation of Course Prerequisites and Learning Outcomes Using Machine Learning Methods. In: Cheng, E.C.K., Wang, T., Schlippe, T., Beligiannis, G.N. (eds) Artificial Intelligence in Education Technologies: New Development and Innovative Practices. AIET 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 154. Springer, Singapore. https://doi.org/10.1007/978-981-19-8040-4_3
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DOI: https://doi.org/10.1007/978-981-19-8040-4_3
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