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Machine Learning Techniques for Evaluating Student Performance

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Advances in Intelligent Computing Techniques and Applications (IRICT 2023)

Abstract

In various sectors, particularly in education, artificial intelligence has significantly influenced outcomes by deploying AI algorithms to gain valuable insights into student learning processes, emphasizing the importance of assessing students’ knowledge for understanding their learning levels and improving educational strategies. Traditional assessment methods have limitations due to biases and time constraints. With advancements in artificial intelligence and machine learning, this study employs machine learning algorithms to create a predictive model identifying students with academic challenges. By utilizing diverse features, this research identifies key factors influencing academic outcomes, achieving an impressive classification accuracy of 90.91%. Logistic Regression outperformed other models like XGBoost and Gradient Boost. Integrating AI and ML techniques revolutionizes assessment methods, offering objective insights for timely interventions. This approach not only enhances students’ academic journey but also creates a conducive learning environment, promoting sustainable academic success in higher education.

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References

  1. Hoffait, A.S., Schyns, M.: Early detection of university students with potential difficulties. Decis. Support. Syst. 101, 1–11 (2017)

    Article  Google Scholar 

  2. Siemens, G., Baker, R.S.D.: Learning analytics and educational data mining: towards communication and collaboration. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 252–254, April 2012

    Google Scholar 

  3. Ahmad, F., Ismail, N.H., Aziz, A.A.: The prediction of students’ academic performance using classification data mining techniques. Appl. Math. Sci. 9(129), 6415–6426 (2015)

    Google Scholar 

  4. Baker, R.S., Yacef, K.: The state of educational data mining in 2009: a review and future visions. J. Educ. Data Mining 1(1), 3–17 (2009)

    Google Scholar 

  5. Siemens, G., Long, P.: Penetrating the fog: analytics in learning and education. EDUCAUSE Rev. 46(5), 30 (2011)

    Google Scholar 

  6. Berland, M., Baker, R.S., Blikstein, P.: Educational data mining and learning analytics: applications to constructionist research. Technol. Knowl. Learn. 19, 205–220 (2014)

    Article  Google Scholar 

  7. Bañeres, D., Rodríguez, M.E., Guerrero-Roldán, A.E., Karadeniz, A.: An early warning system to detect at-risk students in online higher education. Appl. Sci. 10(13), 4427 (2020)

    Article  Google Scholar 

  8. Tomasevic, N., Gvozdenovic, N., Vranes, S.: An overview and comparison of supervised data mining techniques for student exam performance prediction. Comput. Educ. 143, 103676 (2020)

    Article  Google Scholar 

  9. Kotsiantis, S.B., Pierrakeas, C.J., Pintelas, P.E.: Preventing student dropout in distance learning using machine learning techniques. In: Palade, V., Howlett, R.J., Jain, L. (eds.) Knowledge-Based Intelligent Information and Engineering Systems. LNCS (LNAI), vol. 2774, pp. 267–274. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-45226-3_37

    Chapter  Google Scholar 

  10. Minaei-Bidgoli, B., Kashy, D.A., Kortemeyer, G., Punch, W.F.L.: Predicting student performance: an application of data mining methods with an educational web-based system. In: 33rd Annual Frontiers in Education, FIE 2003, vol. 1, pp. T2A-13. IEEE, November 2003

    Google Scholar 

  11. Sekeroglu, B., Dimililer, K., Tuncal, K.: Student performance prediction and classification using machine learning algorithms. In: Proceedings of the 2019 8th International Conference on Educational and Information Technology, pp. 7–11, March 2019

    Google Scholar 

  12. Kumar, A.D., Selvam, R.P., Kumar, K.S.: Review on prediction algorithms in educational data mining. Int. J. Pure Appl. Math. 118(8), 531–537 (2018)

    Google Scholar 

  13. Adekitan, A.I., Noma-Osaghae, E.: Data mining approach to predicting the performance of first year student in a university using the admission requirements. Educ. Inf. Technol. 24, 1527–1543 (2019)

    Article  Google Scholar 

  14. Almeda, M.V., Zuech, J., Utz, C., Higgins, G., Reynolds, R., Baker, R.S.: Comparing the factors that predict completion and grades among for-credit and Open/MOOC students in online learning. Online Learn. 22(1), 1–18 (2018)

    Article  Google Scholar 

  15. Kemper, L., Vorhoff, G., Wigger, B.U.: Predicting student dropout: a machine learning approach. Eur. J. High. Educ. 10(1), 28–47 (2020)

    Article  Google Scholar 

  16. Özdağoğlu, G., Öztaş, G.Z., Çağliyangil, M.: An application framework for mining online learning processes through event-logs. Bus. Process. Manag. J. 25(5), 860–886 (2019)

    Article  Google Scholar 

  17. Abu Zohair, L.M.: Prediction of student’s performance by modelling small dataset size. Int. J. Educ. Technol. High. Educ. 16(1), 1–18 (2019). https://doi.org/10.1186/s41239-019-0160-3

    Article  Google Scholar 

  18. Corrigan, O., Smeaton, A.F.: A course agnostic approach to predicting student success from VLE log data using recurrent neural networks. In: Lavoué, É., Drachsler, H., Verbert, K., Broisin, J., Pérez-Sanagustín, M. (eds.) Data Driven Approaches in Digital Education. LNCS, vol. 10474, pp. 545–548. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66610-5_59

    Chapter  Google Scholar 

  19. Okubo, F., Yamashita, T., Shimada, A., Ogata, H.: A neural network approach for students’ performance prediction. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, pp. 598–599, March 2017

    Google Scholar 

  20. Kaggle. https://www.kaggle.com/datasets/thedevastator/higher-education-predictors-of-sudent-retention. Accessed 26 Aug 2023

  21. Kaggle. https://www.kaggle.com/code/akashsdas/predict-students-grades/input. Accessed 17 Sept 2023

  22. James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning, vol. 112, p. 18. Springer, New York (2013). https://doi.org/10.1007/978-1-4614-7138-7

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Correspondence to Josephine Oludipe or Faisal Saeed .

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Oludipe, J., Saeed, F., Mohammed, R. (2024). Machine Learning Techniques for Evaluating Student Performance. In: Saeed, F., Mohammed, F., Fazea, Y. (eds) Advances in Intelligent Computing Techniques and Applications. IRICT 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-031-59707-7_27

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