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|
|Number of pages||13|
|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
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016|
|Period||4/19/16 → 4/22/16|
Bibliographical notePublisher Copyright:
© Springer International Publishing Switzerland 2016.