Next-Term Student Performance Prediction: A Recommender Systems Approach
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Published
Sep 25, 2016
Mack Sweeney
Jaime Lester
Huzefa Rangwala
Aditya Johri
Abstract
An enduring issue in higher education is student retention to successful graduation. National statistics indicate that most higher education institutions have four-year degree completion rates around 50%, or just half of their student populations. While there are prediction models which illuminate what factors assist with college student success, interventions that support course selections on a semester-to-semester basis have yet to be deeply understood. To further this goal, we develop a system to predict students' grades in the courses they will enroll in during the next enrollment term by learning patterns from historical transcript data coupled with additional information about students, courses and the instructors teaching them. We explore a variety of classic and state-of-the-art techniques which have proven effective for recommendation tasks in the e-commerce domain. In our experiments, Factorization Machines (FM), Random Forests (RF), and the Personalized Linear Multiple Regression model achieve the lowest prediction error. Application of a novel feature selection technique is key to the predictive success and interpretability of the FM. By comparing feature importance across populations and across models, we uncover strong connections between instructor characteristics and student performance. We also discover key differences between transfer and non-transfer students. Ultimately we find that a hybrid FM-RF method can be used to accurately predict grades for both new and returning students taking both new and existing courses. Application of these techniques holds promise for student degree planning, instructor interventions, and personalized advising, all of which could improve retention and academic performance.
How to Cite
Sweeney, M., Lester, J., Rangwala, H., & Johri, A. (2016). Next-Term Student Performance Prediction: A Recommender Systems Approach. Journal of Educational Data Mining, 8(1), 22–51. https://doi.org/10.5281/zenodo.3554603
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Keywords
matrix factorization, grade prediction, cold-start, recommender system, educational data mining, regression
References
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ADAMIC, L. A., LUKOSE, R. M., PUNIYANI, A. R., AND HUBERMAN, B. A. 2001. Search in powerlaw networks. Physical Review E 64, 4.
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AUD, S. AND WILKINSON-FLICKER, S. 2013. The Condition of Education 2013. Number NCES 2013- 037. U.S. Department of Education, National Center for Education Statistics.
BAKER, R. S., CORBETT, A. T., AND ALEVEN, V. 2008. Improving contextual models of guessing and slipping with a truncated training set. Human-Computer Interaction Institute, 17.
BARNES, T. AND STAMPER, J. 2008. Toward automatic hint generation for logic proof tutoring using historical student data. In Proc. of the 9th International Conference on Intelligent Tutoring Systems (2008). 373–382.
BAYER, I. 2015. fastFM: A library for factorization machines. arXiv:1505.00641v2.
BELL, R. M., KOREN, Y., AND VOLINSKY, C. 2007. The BellKor solution to the Netflix prize. AT&T Labs Research.
BREIMAN, L. 2001. Random forests. Machine Learning 45, 1, 5–32.
CORBETT, A. T. AND ANDERSON, J. R. 1994. Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction 4, 4, 253–278.
COUNCIL, N. R. 2001. Building a Workforce for the Information Economy. National Academies Press.
DESMARAIS, M. C. AND BAKER, R. S. J. D. 2011. A review of recent advances in learner and skill modeling in intelligent learning environments. User Modeling and User-Adapted Interaction 22, 1, 9–38.
DRACHSLER, H., VERBERT, K., SANTOS, O. C., AND MANOUSELIS, N. 2015. Panorama of Recommender Systems to Support Learning. In Recommender Systems Handbook, F. Ricci, L. Rokach, and B. Shapira, Eds. Springer US, Boston, MA, 421–451.
ELBADRAWY, A., STUDHAM, R. S., AND GEORGE KARYPIS. 2015. Personalized multi-regression models for predicting students’ performance in course activities. LAK, ’15.
EVANS, B. J., BAKER, R. B., AND DEE, T. S. 2016. Persistence Patterns in Massive Open Online Courses (MOOCs). Journal of Higher Education 87, 2 (Apr.), 206–242.
FREUDENTHALER, C., SCHMIDT-THIEME, L., AND RENDLE, S. 2009. Factorization Machines Factorized Polynomial Regression Models.
GANTNER, Z., DRUMOND, L., FREUDENTHALER, C., RENDLE, S., AND SCHMIDT-THIEME, L. 2010. Learning attribute-to-feature mappings for cold-start recommendations. In Proc. of the 2010 IEEE International Conference on Data Mining (2010). ICDM ’10. IEEE Computer Society, 176–185.
GRACE KENA, LAUREN MUSU-GILLETTE, JENNIFER ROBINSON, XIAOLEI WANG, AMY RATHBUN, JIJUN ZHANG SIDNEY WILKINSON-FLICKER, AMY BARMER, AND ERIN DUNLOP VELEZ. 2015. The Condition of Education 2015. Tech. Rep. NCES 2015-144, U.S. Department of Education, National Center for Education Statistics, Washington, DC. May.
GRAYSON, A., MILLER, H., AND CLARKE, D. D. 1998. Identifying barriers to help-seeking: a qualitative analysis of students’ preparedness to seek help from tutors. British Journal of Guidance and Counselling 26, 2, 237–253.
HUETE, J. F., FERNNDEZ-LUNA, J. M., DE CAMPOS, L. M., AND RUEDA-MORALES, M. A. 2012. Using past-prediction accuracy in recommender systems. Information Sciences 199, 78–92.
JAYAPRAKASH, S. M., MOODY, E. W., LAURA, E. J., REGAN, J. R., AND BARON, J. D. 2014. Early alert of academically at-risk students: An open source analytics initiative. 1, 1, 6–47.
KANAGAL, B., AHMED, A., PANDEY, S., JOSIFOVSKI, V., YUAN, J., AND GARCIA-PUEYO, L. 2012. Supercharging recommender systems using taxonomies for learning user purchase behavior. Proc. VLDB Endow. 5, 10, 956–967.
KEVIN CAREY. 2005. Choosing to improve: Voices from colleges and universities with better graduation rates.
KOREN, Y. AND BELL, R. 2011. Advances in collaborative filtering. In Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds. Springer, 145–186.
LINDEN, G., SMITH, B., AND YORK, J. 2003. Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing 7, 1, 76–80.
LIYANAGUNAWARDENA, T. R., ADAMS, A. A., AND WILLIAMS, S. A. 2013. MOOCs: A systematic study of the published literature 2008-2012. The International Review of Research in Open and Distributed Learning 14, 3 (June), 202–227.
NEDUNGADI, P. AND SMRUTHY, T. K. 2016. Personalized multi-relational matrix factorization model for predicting student performance. In Intelligent Systems Technologies and Applications, S. Berretti, S. M. Thampi, and P. R. Srivastava, Eds. Vol. 384. Springer International Publishing, 163–172.
OGNJANOVIC, I., GASEVIC, D., AND DAWSON, S. 2016. Using institutional data to predict student course selections in higher education. The Internet and Higher Education 29, 49–62.
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.
PARDOS, Z. A., WANG, Q. Y., AND TRIVEDI, S. 2012. The real world significance of performance prediction. International Educational Data Mining Society.
PENA-AYALA, A. 2014. Educational data mining: A survey and a data mining-based analysis of recent works. Expert Systems with Applications 41, 4, 1432–1462.
PEDREGOSA, F., VAROQUAUX, G., GRAMFORT, A. E. A., MICHEL, V., THIRION, B., GRISEL, O., BLONDEL, M., PRETTENHOFER, P., WEISS, R., DUBOURG, V., VANDERPLAS, J., PASSOS, A., COURNAPEAU, D., BRUCHER, M., PERROT, M., AND DUCHESNAY, E. 2011. Scikit-learn: Machine learning in Python. JMLR 12, 2825–2830.
RENDLE, S. 2012. Factorization machines with libFM. ACM TIST 3, 3.
RHODES, N., UNG, M., ZUNDEL, A., HEROLD, J., AND STAHOVICH, T. 2013. Using a lexical analysis of student’s self-explanation to predict course performance. In Educational Data Mining 2013 (2013).
ROMERO, C., LOPEZ ´ , M., LUNA, J., AND VENTURA, S. 2013. Predicting students’ final performance from participation in on-line discussion forums. Computers & Education 68, 458–472.
ROMERO, C. AND VENTURA, S. 2010. Educational Data Mining: A Review of the State of the Art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 40, 6 (Nov.), 601–618.
ROMERO, C., VENTURA, S., AND GARC´IA, E. 2008. Data mining in course management systems: Moodle case study and tutorial. Computers & Education 51, 1, 368–384.
SALAKHUTDINOV, R., MNIH, A., AND HINTON, G. 2007. Restricted boltzmann machines for collaborative filtering. In Proc. of the 24th ICML. ACM, 791–798.
SHAN, H. AND BANERJEE, A. 2010. Generalized probabilistic matrix factorizations for collaborative filtering. In ICDM ’10 (2010-12). 1025–1030.
SHANI, G., HECKERMAN, D., AND BRAFMAN, R. I. 2005. An MDP-based recommender system. JMLR 6, 1265–1295.
SHIMODAIRA, H. 2000. Improving predictive inference under covariate shift by weighting the loglikelihood function. Journal of Statistical Planning and Inference 90, 2, 227–244.
SOROUR, S. E., MINE, T., GODA, K., AND HIROKAWA, S. 2015. A predictive model to evaluate student performance. Journal of Information Processing 23, 2, 192–201.
SWEENEY, M., LESTER, J., AND RANGWALA, H. 2015. Next-term student grade prediction. In 2015 IEEE International Conference on Big Data (Big Data). 970–975.
THAI-NGHE, N., DRUMOND, L., HORVATH, T., NANOPOULOS, A., AND SCHMIDT-THIEME, L. 2011. Matrix and tensor factorization for predicting student performance. In CSEDU (1) (2011). 69–78.
THAI-NGHE, N., DRUMOND, L., HORVATH, T., AND SCHMIDT-THIEME, L. 2011. Multi-relational factorization models for predicting student performance. In KDD Workshop on Knowledge Discovery in Educational Data (KDDinED) (2011).
THAI-NGHE, N., DRUMOND, L., HORVATH, T., AND SCHMIDT-THIEME, L. 2012. Using factorization machines for student modeling. In UMAP Workshops (2012).
TINTO, V. 2006. Research and practice of student retention: what next? Journal of College Student Retention: Research, Theory & Practice 8, 1, 1–19.
TINTO, V. 2012. Completing College: Rethinking Institutional Action. University of Chicago Press.
XU, Y. AND MOSTOW, J. 2012. Comparison of Methods to Trace Multiple Subskills: Is LR-DBN Best? International Educational Data Mining Society.
XU, Y. AND MOSTOW, J. 2015. A unified 5-dimensional framework for student models. International Educational Data Mining Society.
ADAMIC, L. A., LUKOSE, R. M., PUNIYANI, A. R., AND HUBERMAN, B. A. 2001. Search in powerlaw networks. Physical Review E 64, 4.
ADOMAVICIUS, G. AND TUZHILIN, A. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. on Knowl. and Data Eng. 17, 6, 734–749.
AIKEN-WISNIEWSKI, S. A., JOHNSON, A., LARSON, J., AND BARKEMEYER, J. 2015. A Preliminary Report of Advisor Perceptions of Advising and of a Profession. NACADA Journal 35, 2 (Nov.), 60– 70.
ALSUMAIT, L., WANG, P., DOMENICONI, C., AND BARBARA, D. 2010. Embedding semantics in LDA topic models. In Text Mining, M. W. Berry and J. Kogan, Eds. John Wiley & Sons, Ltd, 183–204.
ARKADIUSZ PATEREK. 2007. Improving regularized singular value decomposition for collaborative filtering. KDD.
ARNOLD, K. E. AND PISTILLI, M. D. 2012. Course Signals at Purdue: Using Learning Analytics to Increase Student Success. In Proceedings of the 2Nd International Conference on Learning Analytics and Knowledge. LAK ’12. ACM, New York, NY, USA, 267–270.
AUD, S. AND WILKINSON-FLICKER, S. 2013. The Condition of Education 2013. Number NCES 2013- 037. U.S. Department of Education, National Center for Education Statistics.
BAKER, R. S., CORBETT, A. T., AND ALEVEN, V. 2008. Improving contextual models of guessing and slipping with a truncated training set. Human-Computer Interaction Institute, 17.
BARNES, T. AND STAMPER, J. 2008. Toward automatic hint generation for logic proof tutoring using historical student data. In Proc. of the 9th International Conference on Intelligent Tutoring Systems (2008). 373–382.
BAYER, I. 2015. fastFM: A library for factorization machines. arXiv:1505.00641v2.
BELL, R. M., KOREN, Y., AND VOLINSKY, C. 2007. The BellKor solution to the Netflix prize. AT&T Labs Research.
BREIMAN, L. 2001. Random forests. Machine Learning 45, 1, 5–32.
CORBETT, A. T. AND ANDERSON, J. R. 1994. Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction 4, 4, 253–278.
COUNCIL, N. R. 2001. Building a Workforce for the Information Economy. National Academies Press.
DESMARAIS, M. C. AND BAKER, R. S. J. D. 2011. A review of recent advances in learner and skill modeling in intelligent learning environments. User Modeling and User-Adapted Interaction 22, 1, 9–38.
DRACHSLER, H., VERBERT, K., SANTOS, O. C., AND MANOUSELIS, N. 2015. Panorama of Recommender Systems to Support Learning. In Recommender Systems Handbook, F. Ricci, L. Rokach, and B. Shapira, Eds. Springer US, Boston, MA, 421–451.
ELBADRAWY, A., STUDHAM, R. S., AND GEORGE KARYPIS. 2015. Personalized multi-regression models for predicting students’ performance in course activities. LAK, ’15.
EVANS, B. J., BAKER, R. B., AND DEE, T. S. 2016. Persistence Patterns in Massive Open Online Courses (MOOCs). Journal of Higher Education 87, 2 (Apr.), 206–242.
FREUDENTHALER, C., SCHMIDT-THIEME, L., AND RENDLE, S. 2009. Factorization Machines Factorized Polynomial Regression Models.
GANTNER, Z., DRUMOND, L., FREUDENTHALER, C., RENDLE, S., AND SCHMIDT-THIEME, L. 2010. Learning attribute-to-feature mappings for cold-start recommendations. In Proc. of the 2010 IEEE International Conference on Data Mining (2010). ICDM ’10. IEEE Computer Society, 176–185.
GRACE KENA, LAUREN MUSU-GILLETTE, JENNIFER ROBINSON, XIAOLEI WANG, AMY RATHBUN, JIJUN ZHANG SIDNEY WILKINSON-FLICKER, AMY BARMER, AND ERIN DUNLOP VELEZ. 2015. The Condition of Education 2015. Tech. Rep. NCES 2015-144, U.S. Department of Education, National Center for Education Statistics, Washington, DC. May.
GRAYSON, A., MILLER, H., AND CLARKE, D. D. 1998. Identifying barriers to help-seeking: a qualitative analysis of students’ preparedness to seek help from tutors. British Journal of Guidance and Counselling 26, 2, 237–253.
HUETE, J. F., FERNNDEZ-LUNA, J. M., DE CAMPOS, L. M., AND RUEDA-MORALES, M. A. 2012. Using past-prediction accuracy in recommender systems. Information Sciences 199, 78–92.
JAYAPRAKASH, S. M., MOODY, E. W., LAURA, E. J., REGAN, J. R., AND BARON, J. D. 2014. Early alert of academically at-risk students: An open source analytics initiative. 1, 1, 6–47.
KANAGAL, B., AHMED, A., PANDEY, S., JOSIFOVSKI, V., YUAN, J., AND GARCIA-PUEYO, L. 2012. Supercharging recommender systems using taxonomies for learning user purchase behavior. Proc. VLDB Endow. 5, 10, 956–967.
KEVIN CAREY. 2005. Choosing to improve: Voices from colleges and universities with better graduation rates.
KOREN, Y. AND BELL, R. 2011. Advances in collaborative filtering. In Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds. Springer, 145–186.
LINDEN, G., SMITH, B., AND YORK, J. 2003. Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing 7, 1, 76–80.
LIYANAGUNAWARDENA, T. R., ADAMS, A. A., AND WILLIAMS, S. A. 2013. MOOCs: A systematic study of the published literature 2008-2012. The International Review of Research in Open and Distributed Learning 14, 3 (June), 202–227.
NEDUNGADI, P. AND SMRUTHY, T. K. 2016. Personalized multi-relational matrix factorization model for predicting student performance. In Intelligent Systems Technologies and Applications, S. Berretti, S. M. Thampi, and P. R. Srivastava, Eds. Vol. 384. Springer International Publishing, 163–172.
OGNJANOVIC, I., GASEVIC, D., AND DAWSON, S. 2016. Using institutional data to predict student course selections in higher education. The Internet and Higher Education 29, 49–62.
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.
PARDOS, Z. A., WANG, Q. Y., AND TRIVEDI, S. 2012. The real world significance of performance prediction. International Educational Data Mining Society.
PENA-AYALA, A. 2014. Educational data mining: A survey and a data mining-based analysis of recent works. Expert Systems with Applications 41, 4, 1432–1462.
PEDREGOSA, F., VAROQUAUX, G., GRAMFORT, A. E. A., MICHEL, V., THIRION, B., GRISEL, O., BLONDEL, M., PRETTENHOFER, P., WEISS, R., DUBOURG, V., VANDERPLAS, J., PASSOS, A., COURNAPEAU, D., BRUCHER, M., PERROT, M., AND DUCHESNAY, E. 2011. Scikit-learn: Machine learning in Python. JMLR 12, 2825–2830.
RENDLE, S. 2012. Factorization machines with libFM. ACM TIST 3, 3.
RHODES, N., UNG, M., ZUNDEL, A., HEROLD, J., AND STAHOVICH, T. 2013. Using a lexical analysis of student’s self-explanation to predict course performance. In Educational Data Mining 2013 (2013).
ROMERO, C., LOPEZ ´ , M., LUNA, J., AND VENTURA, S. 2013. Predicting students’ final performance from participation in on-line discussion forums. Computers & Education 68, 458–472.
ROMERO, C. AND VENTURA, S. 2010. Educational Data Mining: A Review of the State of the Art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 40, 6 (Nov.), 601–618.
ROMERO, C., VENTURA, S., AND GARC´IA, E. 2008. Data mining in course management systems: Moodle case study and tutorial. Computers & Education 51, 1, 368–384.
SALAKHUTDINOV, R., MNIH, A., AND HINTON, G. 2007. Restricted boltzmann machines for collaborative filtering. In Proc. of the 24th ICML. ACM, 791–798.
SHAN, H. AND BANERJEE, A. 2010. Generalized probabilistic matrix factorizations for collaborative filtering. In ICDM ’10 (2010-12). 1025–1030.
SHANI, G., HECKERMAN, D., AND BRAFMAN, R. I. 2005. An MDP-based recommender system. JMLR 6, 1265–1295.
SHIMODAIRA, H. 2000. Improving predictive inference under covariate shift by weighting the loglikelihood function. Journal of Statistical Planning and Inference 90, 2, 227–244.
SOROUR, S. E., MINE, T., GODA, K., AND HIROKAWA, S. 2015. A predictive model to evaluate student performance. Journal of Information Processing 23, 2, 192–201.
SWEENEY, M., LESTER, J., AND RANGWALA, H. 2015. Next-term student grade prediction. In 2015 IEEE International Conference on Big Data (Big Data). 970–975.
THAI-NGHE, N., DRUMOND, L., HORVATH, T., NANOPOULOS, A., AND SCHMIDT-THIEME, L. 2011. Matrix and tensor factorization for predicting student performance. In CSEDU (1) (2011). 69–78.
THAI-NGHE, N., DRUMOND, L., HORVATH, T., AND SCHMIDT-THIEME, L. 2011. Multi-relational factorization models for predicting student performance. In KDD Workshop on Knowledge Discovery in Educational Data (KDDinED) (2011).
THAI-NGHE, N., DRUMOND, L., HORVATH, T., AND SCHMIDT-THIEME, L. 2012. Using factorization machines for student modeling. In UMAP Workshops (2012).
TINTO, V. 2006. Research and practice of student retention: what next? Journal of College Student Retention: Research, Theory & Practice 8, 1, 1–19.
TINTO, V. 2012. Completing College: Rethinking Institutional Action. University of Chicago Press.
XU, Y. AND MOSTOW, J. 2012. Comparison of Methods to Trace Multiple Subskills: Is LR-DBN Best? International Educational Data Mining Society.
XU, Y. AND MOSTOW, J. 2015. A unified 5-dimensional framework for student models. International Educational Data Mining Society.
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