Grade prediction with course and student specific models

Agoritsa Polyzou; George Karypis

doi:10.1007/978-3-319-31753-3_8

Grade prediction with course and student specific models

Agoritsa Polyzou, George Karypis

Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

31 Scopus citations

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 models and low-rank matrix factorizations 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. This evaluation showed that the course specific models outperformed various competing schemes with the best performing scheme achieving a RMSE across the different courses of 0.632 vs 0.661 for the best competing method.

Original language	English (US)
Title of host publication	Advances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings
Editors	Ruili Wang, James Bailey, Takashi Washio, Joshua Zhexue Huang, Latifur Khan, Gillian Dobbie
Publisher	Springer Verlag
Pages	89-101
Number of pages	13
ISBN (Print)	9783319317526
DOIs	https://doi.org/10.1007/978-3-319-31753-3_8
State	Published - 2016
Event	20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016 - Auckland, New Zealand Duration: Apr 19 2016 → Apr 22 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9651
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016
Country/Territory	New Zealand
City	Auckland
Period	4/19/16 → 4/22/16

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

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10.1007/978-3-319-31753-3_8

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Polyzou, A., & Karypis, G. (2016). Grade prediction with course and student specific models. In R. Wang, J. Bailey, T. Washio, J. Z. Huang, L. Khan, & G. Dobbie (Eds.), Advances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings (pp. 89-101). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9651). Springer Verlag. https://doi.org/10.1007/978-3-319-31753-3_8

Grade prediction with course and student specific models. / Polyzou, Agoritsa; Karypis, George.
Advances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings. ed. / Ruili Wang; James Bailey; Takashi Washio; Joshua Zhexue Huang; Latifur Khan; Gillian Dobbie. Springer Verlag, 2016. p. 89-101 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9651).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Polyzou, A & Karypis, G 2016, Grade prediction with course and student specific models. in R Wang, J Bailey, T Washio, JZ Huang, L Khan & G Dobbie (eds), Advances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9651, Springer Verlag, pp. 89-101, 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016, Auckland, New Zealand, 4/19/16. https://doi.org/10.1007/978-3-319-31753-3_8

Polyzou A, Karypis G. Grade prediction with course and student specific models. In Wang R, Bailey J, Washio T, Huang JZ, Khan L, Dobbie G, editors, Advances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings. Springer Verlag. 2016. p. 89-101. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-31753-3_8

Polyzou, Agoritsa ; Karypis, George. / Grade prediction with course and student specific models. Advances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings. editor / Ruili Wang ; James Bailey ; Takashi Washio ; Joshua Zhexue Huang ; Latifur Khan ; Gillian Dobbie. Springer Verlag, 2016. pp. 89-101 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Grade prediction with course and student specific models

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