A Comparative Study of Student Performance Prediction using Pre-Course Data
Abstract Students at Saudi universities face difficulty registering for the right course since Student performance there is no support offered to students that uniquely consider each situation. Machine learning techniques could be applied to fill this gap by predicting grades of new courses for each student based on their historical data. This paper experiments with nine different prediction algorithms to predict course grades for public university students. The data-set includes grades for 215 students and 180 various courses. The models utilize grades obtained in semesters between the 2015 and 2018 academic years and evaluated on grades obtained in the 2019 academic year. Our result shows that the K-nearest neighbor with ZScore model outperforms the remaining models with respect to the Percentage of Tick Accuracy (PTA), which is the difference between two consecutive letter grades for the predicted letter grade and the observed letter grade. Our work achieved an 84% accuracy score in PTA2, where the difference between the predicted letter grade and the actual letter grade is less than or equal to two consecutive letter grades.
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Ren, Z., Ning, X., and Rangwala, H., 2018. Ale: Additive latent effect models for grade prediction. In Proceedings of the 2018 SIAM International Conference on Data Mining, pages 477–485. SIAM.
Ricci, F., Rokach, L., and Shapira, B., 2011. Introduction to recommender systems handbook. In Recommender systems handbook, pages 1–35. Springer.
Zafra, A. and Ventura, S., 2009. Predicting Student Grades in Learning Management Systems with Multiple Instance Genetic Programming. International working group on educational data mining.
Zhang, S., Wang, W., Ford, J., and Makedon, F., 2006. Learning from incomplete ratings using non-negative matrix factorization. In Proceedings of the 2006 SIAM international conference on data mining, pages 549–553. SIAM.
Buenaño-Fernández, D., Gil, D., and Luján-Mora, S., 2019. Application of machine learning in predicting performance for computer engineering students: A case study. Sustainability, 11(10):2833.
Chai, T. and Draxler, R. R., 2014. Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geoscientific model development, 7(3):1247–1250.
Cheadle, C., Vawter, M. P., Freed, W. J., and Becker, K. G., 2003. Analysis of microarray data using Z score transformation. The Journal of molecular diagnostics, 5(2):73–81.
George, T. and Merugu, S., 2005. A scalable collaborative filtering framework based on co-clustering. In Fifth IEEE International Conference on Data Mining (ICDM’05), pages 4–pp. IEEE.
Hug, N., 2020. Surprise: A Python library for recommender systems. Journal of Open Source Software, 5(52):2174.
Iqbal, Z., Qadir, J., Mian, A. N., and Kamiran, F., 2017. Machine learning based student grade prediction: A case study. arXiv preprint arXiv:1708.08744.
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, pages 426–434.
Koren, Y., 2010. Factor in the neighbors: Scalable and accurate collaborative filtering. ACM Transactions on Knowledge Discovery from Data (TKDD), 4(1):1–24.
Lemire, D. and Maclachlan, A., 2005. Slope one predictors for online rating-based collaborative filtering. In Proceedings of the 2005 SIAM International Conference on Data Mining, pages 471–475. SIAM.
Luo, X., Zhou, M., Xia, Y., and Zhu, Q., 2014. An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems. IEEE Transactions on Industrial Informatics, 10(2):1273– 1284.
Mnih, A. and Salakhutdinov, R. R., 2008. Probabilistic matrix factorization. In Advances in neural information processing systems, pages 1257–1264.
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, pages 552–560. SIAM.
Morsy, S. and Karypis, G., 2019. Will this Course Increase or Decrease Your GPA? Towards Grade-aware Course Recommendation. arXiv preprint arXiv:1904.11798.
Polyzou, A. and Karypis, G., 2016. Grade prediction with course and student specific models. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 89–101. Springer.
Ren, Z., Ning, X., and Rangwala, H., 2018. Ale: Additive latent effect models for grade prediction. In Proceedings of the 2018 SIAM International Conference on Data Mining, pages 477–485. SIAM.
Ricci, F., Rokach, L., and Shapira, B., 2011. Introduction to recommender systems handbook. In Recommender systems handbook, pages 1–35. Springer.
Zafra, A. and Ventura, S., 2009. Predicting Student Grades in Learning Management Systems with Multiple Instance Genetic Programming. International working group on educational data mining.
Zhang, S., Wang, W., Ford, J., and Makedon, F., 2006. Learning from incomplete ratings using non-negative matrix factorization. In Proceedings of the 2006 SIAM international conference on data mining, pages 549–553. SIAM.
Alharbi, B., Assiri, F. ., & Alharbi, B. . (2021). A Comparative Study of Student Performance Prediction using Pre-Course Data. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 10(1), 49–61. https://doi.org/10.14201/ADCAIJ20211014961
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