Abstract
The accurate estimation of students’ grades in prospective courses is important as it can support the procedure of making an informed choice concerning the selection of next semester courses. As a consequence, the process of creating personal academic pathways is facilitated. This paper provides a comparison of several models for future course grade prediction based on three matrix factorization methods. We attempt to improve the existing techniques by combining matrix factorization with prior knowledge about the similarity between students and courses calculated using the SimRank algorithm. The evaluation of the proposed models is conducted on an internal dataset of anonymized student record data.
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Acknowledgments
This work is a result within the project SISng (Study Information Systems of the Next Generation) [11], which is currently ongoing at the Faculty of Computer Science and Engineering in Skopje. The authors would also like to thank Ljupcho Rechkoski for the provided materials.
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Krstova, A., Stevanoski, B., Mihova, M., Ajanovski, V.V. (2018). Initialization of Matrix Factorization Methods for University Course Recommendations Using SimRank Similarities. In: Kalajdziski, S., Ackovska, N. (eds) ICT Innovations 2018. Engineering and Life Sciences. ICT 2018. Communications in Computer and Information Science, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-00825-3_15
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DOI: https://doi.org/10.1007/978-3-030-00825-3_15
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