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.
|Original language||English (US)|
|Number of pages||13|
|Journal||International Journal of Data Science and Analytics|
|State||Published - Dec 1 2016|
Bibliographical noteFunding 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
© 2016, Springer International Publishing Switzerland.
- Course-specific models
- Learning analytics
- Next-term grade prediction