Grade prediction with course and student specific models

Agoritsa Polyzou, George Karypis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 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 languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings
EditorsRuili Wang, James Bailey, Takashi Washio, Joshua Zhexue Huang, Latifur Khan, Gillian Dobbie
PublisherSpringer Verlag
Pages89-101
Number of pages13
ISBN (Print)9783319317526
DOIs
StatePublished - 2016
Event20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016 - Auckland, New Zealand
Duration: Apr 19 2016Apr 22 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9651
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016
Country/TerritoryNew Zealand
CityAuckland
Period4/19/164/22/16

Bibliographical note

Funding Information:
This work was supported in part by NSF (IIS-0905220, OCI-1048018, CNS-1162405, IIS-1247632, IIP-1414153, IIS-1447788) and the Digital Technology Center at the University of Minnesota. Access to research and computing facilities was provided by the Digital Technology Center and the Minnesota Supercomputing Institute. http://www.msi.umn.edu .

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

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