TY - GEN
T1 - Domain-aware grade prediction and top-n course recommendation
AU - Elbadrawy, Asmaa
AU - Karypis, George
PY - 2016/9/7
Y1 - 2016/9/7
N2 - Automated course recommendation can help deliver personalized and e.ective college advising and degree planning. Nearest neighbor and matrix factorization based collaborative filtering approaches have been applied to student-course grade data to help students select suitable courses. However, the student-course enrollment patterns exhibit grouping structures that are tied to the student and course academic features, which lead to grade data that are not missing at random (NMAR). Existing approaches for dealing with NMAR data, such as Response-aware and contextaware matrix factorization, do not model NMAR data in terms of the user and item features and are not designed with the characteristics of grade data in mind. In this work we investigate how the student and course academic features influence the enrollment patterns and we use these features to define student and course groups at various levels of granularity. We show how these groups can be used to design grade prediction and top-n course ranking models for neighborhood-based user collaborative filtering, matrix factorization and popularity-based ranking approaches. These methods give lower grade prediction error and more accurate top-n course rankings than the other methods that do not take domain knowledge into account.
AB - Automated course recommendation can help deliver personalized and e.ective college advising and degree planning. Nearest neighbor and matrix factorization based collaborative filtering approaches have been applied to student-course grade data to help students select suitable courses. However, the student-course enrollment patterns exhibit grouping structures that are tied to the student and course academic features, which lead to grade data that are not missing at random (NMAR). Existing approaches for dealing with NMAR data, such as Response-aware and contextaware matrix factorization, do not model NMAR data in terms of the user and item features and are not designed with the characteristics of grade data in mind. In this work we investigate how the student and course academic features influence the enrollment patterns and we use these features to define student and course groups at various levels of granularity. We show how these groups can be used to design grade prediction and top-n course ranking models for neighborhood-based user collaborative filtering, matrix factorization and popularity-based ranking approaches. These methods give lower grade prediction error and more accurate top-n course rankings than the other methods that do not take domain knowledge into account.
KW - Grade Prediction
KW - Multi-Granularity Grouping
KW - Top-n Course Ranking
UR - http://www.scopus.com/inward/record.url?scp=84991199428&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84991199428&partnerID=8YFLogxK
U2 - 10.1145/2959100.2959133
DO - 10.1145/2959100.2959133
M3 - Conference contribution
AN - SCOPUS:84991199428
T3 - RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems
SP - 183
EP - 190
BT - RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems
PB - Association for Computing Machinery, Inc
T2 - 10th ACM Conference on Recommender Systems, RecSys 2016
Y2 - 15 September 2016 through 19 September 2016
ER -