Will this Course Increase or Decrease Your GPA? Towards Grade-aware Course Recommendation

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Published Sep 30, 2019
Sara Morsy George Karypis

Abstract

In order to help undergraduate students towards successfully completing their degrees, developing tools that can assist students during the course selection process is a significant task in the education domain. The optimal set of courses for each student should include courses that help him/her graduate in a timely fashion and for which he/she is well-prepared for so as to get a good grade in. To this end, we propose two different grade-aware course recommendation approaches to recommend to each student his/her optimal set of courses. The first approach ranks the courses by using an objective function that differentiates between courses that are expected to increase or decrease a student’s GPA. The second approach combines the grades predicted by grade prediction methods with the rankings produced by course recommendation methods to improve the final course rankings. To obtain the course rankings in both approaches, we adapt two widely-used representation learning techniques to learn the optimal temporal ordering between courses. Our experiments on a large dataset obtained from the University of Minnesota that includes students from 23 different majors show that the grade-aware course recommendation methods can do better on recommending more courses in which the students are expected to perform well and recommending fewer courses which they are expected not to perform well in than grade-unaware course recommendation methods.

How to Cite

Morsy, S., & Karypis, G. (2019). Will this Course Increase or Decrease Your GPA? Towards Grade-aware Course Recommendation. JEDM | Journal of Educational Data Mining, 11(2), 20-46. Retrieved from https://jedm.educationaldatamining.org/index.php/JEDM/article/view/405
Abstract 109 | PDF Downloads 94

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Keywords

representation learning, SVD, course2vec, grade prediction, course recommendation, GPA

References
BACKENKÖHLER, M., SCHERZINGER, F., SINGLA, A., AND WOLF, V. 2018. Data-driven approach towards a personalized curriculum. In Proceedings of the 11th International Conference on Educational Data Mining. 246–251.

BELL, R., KOREN, Y., AND VOLINSKY, C. 2007. Modeling relationships at multiple scales to improve accuracy of large recommender systems. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’07. ACM, New York, NY, USA, 95– 104.

BENDAKIR, N. AND AÏMEUR, E. 2006. Using association rules for course recommendation. In Proceedings of the AAAI Workshop on Educational Data Mining. Vol. 3. 1–10.

BHUMICHITR, K., CHANNARUKUL, S., SAEJIEM, N., JIAMTHAPTHAKSIN, R., AND NONGPONG, K. 2017. Recommender systems for university elective course recommendation. In 14th International Joint Conference on Computer Science and Software Engineering (JCSSE). IEEE, 1–5.

BRAXTON, J. M., HIRSCHY, A. S., AND MCCLENDON, S. A. 2011. Understanding and Reducing College Student Departure: ASHE-ERIC Higher Education Report, Volume 30, Number 3. Vol. 16. John Wiley & Sons.

CHEN, S., MOORE, J. L., TURNBULL, D., AND JOACHIMS, T. 2012. Playlist prediction via metric embedding. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 714–722.

CUCURINGU, M., MARSHAK, C. Z., MONTAG, D., AND ROMBACH, P. 2017. Rank aggregation for course sequence discovery. In International Workshop on Complex Networks and their Applications. Springer, 139–150.

ELBADRAWY, A. AND KARYPIS, G. 2016. Domain-aware grade prediction and top-n course recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 183–190.

ELBADRAWY, A., STUDHAM, R. S., AND KARYPIS, G. 2015. Collaborative multi-regression models for predicting students’ performance in course activities. In Proceedings of the 5th International Learning Analytics and Knowledge Conference. 103–107.

GOLUB, G. H. AND REINSCH, C. 1970. Singular value decomposition and least squares solutions. Numerische mathematik 14, 5, 403–420.

GONZÁLEZ-BRENES, J. P. AND MOSTOW, J. 2012. Dynamic cognitive tracing: Towards unified discovery of student and cognitive models. In Proceedings of the 5th International Conference on Educational Data Mining. 49–56.

GRBOVIC, M., RADOSAVLJEVIC, V., DJURIC, N., BHAMIDIPATI, N., SAVLA, J., BHAGWAN, V., AND SHARP, D. 2015. E-commerce in your inbox: Product recommendations at scale. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1809–1818.

GROVER, A. AND LESKOVEC, J. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 855–864.

HAGEMANN, N., O’MAHONY, M. P., AND SMYTH, B. 2018. Module advisor: Guiding students with recommendations. In Intelligent Tutoring Systems, R. Nkambou, R. Azevedo, and J. Vassileva, Eds. Springer International Publishing, Cham, 319–325.

HERSHKOVITZ, A., GOWDA, S. M., AND CORBETT, A. T. 2013. Predicting future learning better using quantitative analysis of moment-by-moment learning. In Proceedings of the 6th International Conference on Educational Data Mining. 74–81.

HU, Q. AND RANGWALA, H. 2018. Course-specific Markovian models for grade prediction. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 29–41.

HUANG, E. H., SOCHER, R., MANNING, C. D., AND NG, A. Y. 2012. Improving word representations via global context and multiple word prototypes. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume 1. Association for Computational Linguistics, 873–882.

HWANG, C.-S. AND SU, Y.-C. 2015. Unified clustering locality preserving matrix factorization for student performance prediction. IAENG International Journal of Computer Science 42, 3, 245–253.

KENA, G., HUSSAR, W., MCFARLAND, J., DE BREY, C., MUSU-GILLETTE, L., WANG, X., ZHANG, J., RATHBUN, A., WILKINSON-FLICKER, S., DILIBERTI, M., BARMER, A., BULLOCK MANN, F., AND DUNLOP VELEZ, E. 2016. The condition of education 2016. Tech. Rep. NCES 2016-144, National Center for Education Statistics.

KOREN, Y. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 426–434.

LAN, A. S., WATERS, A. E., STUDER, C., AND BARANIUK, R. G. 2014. Sparse factor analysis for learning and content analytics. The Journal of Machine Learning Research 15, 1, 1959–2008.

LE, Q. AND MIKOLOV, T. 2014. Distributed representations of sentences and documents. In Proceedings of the 31st International Conference on Machine Learning (ICML-14). 1188–1196.

LEE, Y. AND CHO, J. 2011. An intelligent course recommendation system. SmartCR 1, 1, 69–84.

MEIER, Y., XU, J., ATAN, O., AND SCHAAR, M. V . D . 2015. Personalized grade prediction: A data mining approach. In IEEE International Conference on Data Mining. 907–912.

MIKOLOV, T., CHEN, K., CORRADO, G., AND DEAN, J. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.

MIKOLOV, T., SUTSKEVER, I., CHEN, K., CORRADO, G. S., AND DEAN, J. 2013. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems. 3111–3119.

MORSY, S. AND KARYPIS, G. 2017. Cumulative knowledge-based regression models for next-term grade prediction. In Proceedings of the 2017 SIAM International Conference on Data Mining. SIAM, 552–560.

MORSY, S. AND KARYPIS, G. 2019. A study on curriculum planning and its relationship with graduation gpa and time to degree. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge. ACM, 26–35.

PARAMESWARAN, A., VENETIS, P., AND GARCIA-MOLINA, H. 2011. Recommendation systems with complex constraints: A course recommendation perspective. ACM Transactions on Information Systems (TOIS) 29, 4, 20:1–20:33.

PARAMESWARAN, A. G. AND GARCIA-MOLINA, H. 2009. Recommendations with prerequisites. In Proceedings of the Third ACM Conference on Recommender Systems. ACM, 353–356.

PARAMESWARAN, A. G., GARCIA-MOLINA, H., AND ULLMAN, J. D. 2010. Evaluating, combining and generalizing recommendations with prerequisites. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management. ACM, 919–928.

PARAMESWARAN, A. G., KOUTRIKA, G., BERCOVITZ, B., AND GARCIA-MOLINA, H. 2010. Recsplorer: recommendation algorithms based on precedence mining. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data. ACM, 87–98.

PARDOS, Z. A., FAN, Z., AND JIANG, W. 2019. Connectionist recommendation in the wild: on the utility and scrutability of neural networks for personalized course guidance. User Modeling and User-Adapted Interaction, 1–39.

PATEREK, A. 2007. Improving regularized singular value decomposition for collaborative filtering. In Proceedings of KDD Cup and Workshop. Vol. 2007. 5–8.

PENNINGTON, J., SOCHER, R., AND MANNING, C. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 1532–1543.

PEROZZI, B., AL-RFOU, R., AND SKIENA, S. 2014. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 701–710.

POLYZOU, A. AND KARYPIS, G. 2016. Grade prediction with course and student specific models. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 89–101.

REDDY, S., LABUTOV, I., AND JOACHIMS, T. 2016. Latent skill embedding for personalized lesson sequence recommendation. arXiv preprint.

ROMERO, C., VENTURA, S., ESPEJO, P. G., AND HERV ÁS, C. 2008. Data mining algorithms to classify students. In Proceedings of the 1st International Conference on Educational Data Mining. 8–17.

SARWAR, B., KARYPIS, G., KONSTAN, J., AND RIEDL, J. 2000. Application of dimensionality reduction in recommender system a case study. In Proceeding of WebKDD-2000 Workshop.

SWEENEY, M., LESTER, J., RANGWALA, H., AND JOHRI, A. 2016. Next-term student performance prediction: A recommender systems approach. Journal of Educational Data Mining 8, 1, 22–51.

TANG, J., QU, M., WANG, M., ZHANG, M., YAN, J., AND MEI, Q. 2015. Line: Large-scale information network embedding. In Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 1067–1077.

THAI-NGHE, N., DRUMOND, L., HORVÁTH, T., AND SCHMIDT-THIEME, L. 2012. Using factorization machines for student modeling. In UMAP Workshops.

THAI-NGHE, N., HORVÁTH, T., AND SCHMIDT-THIEME, L. 2011. Factorization models for forecasting student performance. In Proceedings of the 4th International Conference on Educational Data Mining. 11–20.

WANG, P., GUO, J., LAN, Y., XU, J., WAN, S., AND CHENG, X. 2015. Learning hierarchical representation model for nextbasket recommendation. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 403–412.

XU, J., XING, T., AND VANDERSCHAAR, M. 2016. Personalized course sequence recommendations. IEEE Transactions on Signal Processing 64, 20, 5340–5352.
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EDM 2019 Journal Track