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International Journal of Data Science and Analytics
Erschienen in: International Journal of Data Science and Analytics 3-4/2016

01.12.2016 | Regular Paper

Grade prediction with models specific to students and courses

verfasst von: Agoritsa Polyzou, George Karypis

Erschienen in: International Journal of Data Science and Analytics | Ausgabe 3-4/2016

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Abstract

The accurate estimation of students’ grades in future courses is important as it can inform the selection of next term’s courses and create personalized degree pathways to facilitate successful and timely graduation. This paper presents future course grade predictions methods based on sparse linear and low-rank matrix factorization models that are specific to each course or student–course tuple. These methods identify the predictive subsets of prior courses on a course-by-course basis and better address problems associated with the not-missing-at-random nature of the student–course historical grade data. The methods were evaluated on a dataset obtained from the University of Minnesota, for two different departments with different characteristics. This evaluation showed that focusing on course-specific data improves the accuracy of grade prediction.
Vorheriger Artikel A fast and complete algorithm for enumerating pseudo-cliques in large graphs

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Metadaten
Titel
Grade prediction with models specific to students and courses
verfasst von
Agoritsa Polyzou
George Karypis
Publikationsdatum
01.12.2016
Verlag
Springer International Publishing
Erschienen in
International Journal of Data Science and Analytics / Ausgabe 3-4/2016
Print ISSN: 2364-415X
Elektronische ISSN: 2364-4168
DOI
https://doi.org/10.1007/s41060-016-0024-z

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