CRITICAL LITERATURE REVIEW ON CURRENT STATE-OF-THE ART IN PREDICTING STUDENTS’ PERFORMANCE USING MACHINE LEARNING ALGORITHM IN BLENDED LEARNING ENVIRONMENT
Abstract
Background of the study: Predicting and analyzing the performance of the student in a blended learning environment is important to help educators identify poor performing students and improve their academic score. Meanwhile, achieving accurate predictions require selecting machine learning techniques that can produce optimum score. However, there seems to be no critical literature review on current state of art in predicting students’ performance using machine learning algorithms in blended learning environment.
Methodology: This critical literature review focuses on, studies on the current state of the art in predicting students’ performance in the blended learning for past 10 years, sources of dataset used by various authors and the machined learning algorithm with high prediction accuracy.
Findings: Naïve Bayes was the most frequently used algorithm for predicting students’ performance. Authors mostly used online data for their student’s performance prediction. Finally, artificial neural network was found to give higher prediction accuracy of 98.7%.
Keywords: Students’ Performance, Machine Learning Algorithm, Datasets, Moodle, LMS, Blended Learning
References
Agrawal, S., Vishwakarma, S. K., & Sharma, A. K. (2017). Using data mining classifier for predicting student’s performance in UG level. International Journal of Computer Applications, 172(8), 39-44.
Ahmed, K., & Mesonovich, M. (2019). Learning Management Systems and Student Performance. International Journal for E-Learning Security, 8(1), 582–591.
Albreiki, B., Zaki, N., & Alashwal, H. (2021). A systematic literature review of student performance prediction using machine learning techniques. Education Sciences, 11(9), 552.
AlMahamid, F., & Grolinger, K. (2021). Reinforcement learning algorithms: An overview and classification. In 2021 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) (pp. 1-7). IEEE.
Alturki, U., & Aldraiweesh, A. (2021). Application of learning management system (Lms) during the covid-19 pandemic: A sustainable acceptance model of the expansion technology approach. Sustainability (Switzerland), 13(19).
Ashraf, A., Anwer, S., & Khan, M. G. (2018). A Comparative study of predicting student’s performance by use of data mining techniques. American Academic Scientific Research Journal for Engineering, Technology, and Sciences, 44(1), 122-136
Baashar, Y., Alkawsi, G., Ali, N. A., Alhussian, H., & Bahbouh, H. T. (2021). Predicting student’s performance using machine learning methods: A systematic literature review. In 2021 International Conference on Computer & Information Sciences (ICCOINS) (pp. 357-362).
Bassi,J. S., Dada,E. G., Hamidu, A., A. & Dauda, M., E. (2019). Students Graduation on Time Prediction Model Using Artificial Neural Network, Journal of Computer Engineering, 21(3), 28-35.
Bhutto, E. S., Siddiqui, I. F., Arain, Q. A., & Anwar, M. (2020). Predicting students’ academic performance through supervised machine learning. In 2020 International Conference on Information Science and Communication Technology (ICISCT) (pp. 1-6). IEEE.
Buenaño-Fernández, D., Gil, D., & Luján-Mora, S. (2019). Application of Machine Learning in Predicting Performance for Computer Engineering Students: A Case Study. Sustainability, 11(10), 2833-2851.
Buschetto, L. A., Cechinel, C., Batista Machado, M. F., Faria Culmant Ramos, V., & Munoz, R. (2019). Predicting students’ success in blended learning—evaluating different interactions inside learning management systems. Applied Sciences, 9(24), 5523.
Conijn, R., Snijders, C., Kleingeld, A., & Matzat, U. (2016). Predicting student performance from LMS data: A comparison of 17 blended courses using Moodle LMS. IEEE Transactions on Learning Technologies, 10(1), 17-29.
Fahd, K., Miah, S. J., & Ahmed, K. (2021). Predicting student performance in a blended learning environment using learning management system interaction data. Applied Computing and Informatics.
Gerritsen, L. (2017). Predicting student performance with Neural Network, Tilburg University, Netherlands.
Hussain, M., Zhu, W., Zhang, W., Abidi, S. M. R., & Ali, S. (2019). Using machine learning to predict student difficulties from learning session data. Artificial Intelligence Review, 52, 381-407.
Jayaprakash, S., Balamurugan E. & Chandar, V. (2018). Predicting Students Academic Performance using Naive Bayes Algorithm, BlueCrest College Accra, Ghana.
Kehinde, A. J., Adeniyi, A. E., Ogundokun, R. O., Gupta, H., & Misra, S. (2022). Prediction of students’ performance with artificial neural network using demographic traits. In Recent Innovations in Computing: Proceedings of ICRIC 2021, Volume 2 (pp. 613-624). Singapore: Springer Singapore.
Kim, H., Ko, S., Kim, B. J., Ryu, S. J., & Ahn, J. (2022). Predicting chemical structure using reinforcement learning with a stack-augmented conditional variational autoencoder. Journal of Cheminformatics, 14(1), 83
Olaniyi, A. S., Kayode, S. Y., Abiola, H. M., Tosin, S. I. T., & Babatunde, A. N. (2017). Student’s Performance Analysis Using Decision Tree Algorithms. Annals. Computer Science Series, 15(1), 55-62.
Oloruntoba, S. A., & Akinode, J. L. (2017). Student academic performance prediction using support vector machine. International Journal of Engineering Sciences and Research Technology, 6(12), 588-597.
Sandra, L., Lumbangaol, F., & Matsuo, T. (2021). Machine Learning Algorithm to Predict Student’s Performance: A Systematic Literature Review. TEM Journal, 10(4), 1919-1927.
Sekeroglu, B., Dimililer, K., & Tuncal, K. (2019). 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). ACM.
Shaziya, H., Zaheer, R., & Kavitha, G. (2015). Prediction of students’ performance in semester exams using a Naïve Bayes classifier. International Journal of Innovative Research in Science, Engineering and Technology, 4(10), 9823-9829.
Singh, R., & Pal, S. (2020). Application of machine Learning Algorithms to predict students’ performance. International Journal of Advanced Science and Technology, 29(5), 7249-7261.
Soule, P. (2017). Predicting Student Success: A Logistic Regression Analysis of Data from Multiple SIU-C Courses.
Sultana, M., J. Rani,U. & Farquad, M.A.H. (2019). Student’s Performance Prediction using Deep Learning and Data Mining Methods, International Journal of Recent Technology and Engineering, 8(1S4), 1018-1021.
Swamy, M. N., & Hanumanthappa, M. (2012). Predicting academic success from student enrolment data using decision tree technique. Int. J. Appl. Inf. Syst, 4(3), 1-6.
Umek, L., Tomaževic, N., Aristovnik, A., & Keržic, D. (2017). Predictors of Student Performance in a Blended-Learning Environment: An Empirical Investigation. International Association for Development of the Information Society.
Usman, O. L., & Adenubi, A. O. (2013). Artificial neural network (ANN) model for predicting students’ academic performance. Journal of Science and Information Technology, 1(2), 23-37.
Vinod K. P. & Bhatt, V. K. K. (2019). Performance Prediction for Post Graduate Students using Artificial Neural Network, International Journal of Innovative Technology and Exploring Engineering, 8(7S2),446-454.
Yadav, S. K., & Pal, S. (2012). Data mining: A prediction for performance improvement of engineering students using classification. arXiv preprint arXiv:1203.3832.
Ahmed, K., & Mesonovich, M. (2019). Learning Management Systems and Student Performance. International Journal for E-Learning Security, 8(1), 582–591.
Albreiki, B., Zaki, N., & Alashwal, H. (2021). A systematic literature review of student performance prediction using machine learning techniques. Education Sciences, 11(9), 552.
AlMahamid, F., & Grolinger, K. (2021). Reinforcement learning algorithms: An overview and classification. In 2021 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) (pp. 1-7). IEEE.
Alturki, U., & Aldraiweesh, A. (2021). Application of learning management system (Lms) during the covid-19 pandemic: A sustainable acceptance model of the expansion technology approach. Sustainability (Switzerland), 13(19).
Ashraf, A., Anwer, S., & Khan, M. G. (2018). A Comparative study of predicting student’s performance by use of data mining techniques. American Academic Scientific Research Journal for Engineering, Technology, and Sciences, 44(1), 122-136
Baashar, Y., Alkawsi, G., Ali, N. A., Alhussian, H., & Bahbouh, H. T. (2021). Predicting student’s performance using machine learning methods: A systematic literature review. In 2021 International Conference on Computer & Information Sciences (ICCOINS) (pp. 357-362).
Bassi,J. S., Dada,E. G., Hamidu, A., A. & Dauda, M., E. (2019). Students Graduation on Time Prediction Model Using Artificial Neural Network, Journal of Computer Engineering, 21(3), 28-35.
Bhutto, E. S., Siddiqui, I. F., Arain, Q. A., & Anwar, M. (2020). Predicting students’ academic performance through supervised machine learning. In 2020 International Conference on Information Science and Communication Technology (ICISCT) (pp. 1-6). IEEE.
Buenaño-Fernández, D., Gil, D., & Luján-Mora, S. (2019). Application of Machine Learning in Predicting Performance for Computer Engineering Students: A Case Study. Sustainability, 11(10), 2833-2851.
Buschetto, L. A., Cechinel, C., Batista Machado, M. F., Faria Culmant Ramos, V., & Munoz, R. (2019). Predicting students’ success in blended learning—evaluating different interactions inside learning management systems. Applied Sciences, 9(24), 5523.
Conijn, R., Snijders, C., Kleingeld, A., & Matzat, U. (2016). Predicting student performance from LMS data: A comparison of 17 blended courses using Moodle LMS. IEEE Transactions on Learning Technologies, 10(1), 17-29.
Fahd, K., Miah, S. J., & Ahmed, K. (2021). Predicting student performance in a blended learning environment using learning management system interaction data. Applied Computing and Informatics.
Gerritsen, L. (2017). Predicting student performance with Neural Network, Tilburg University, Netherlands.
Hussain, M., Zhu, W., Zhang, W., Abidi, S. M. R., & Ali, S. (2019). Using machine learning to predict student difficulties from learning session data. Artificial Intelligence Review, 52, 381-407.
Jayaprakash, S., Balamurugan E. & Chandar, V. (2018). Predicting Students Academic Performance using Naive Bayes Algorithm, BlueCrest College Accra, Ghana.
Kehinde, A. J., Adeniyi, A. E., Ogundokun, R. O., Gupta, H., & Misra, S. (2022). Prediction of students’ performance with artificial neural network using demographic traits. In Recent Innovations in Computing: Proceedings of ICRIC 2021, Volume 2 (pp. 613-624). Singapore: Springer Singapore.
Kim, H., Ko, S., Kim, B. J., Ryu, S. J., & Ahn, J. (2022). Predicting chemical structure using reinforcement learning with a stack-augmented conditional variational autoencoder. Journal of Cheminformatics, 14(1), 83
Olaniyi, A. S., Kayode, S. Y., Abiola, H. M., Tosin, S. I. T., & Babatunde, A. N. (2017). Student’s Performance Analysis Using Decision Tree Algorithms. Annals. Computer Science Series, 15(1), 55-62.
Oloruntoba, S. A., & Akinode, J. L. (2017). Student academic performance prediction using support vector machine. International Journal of Engineering Sciences and Research Technology, 6(12), 588-597.
Sandra, L., Lumbangaol, F., & Matsuo, T. (2021). Machine Learning Algorithm to Predict Student’s Performance: A Systematic Literature Review. TEM Journal, 10(4), 1919-1927.
Sekeroglu, B., Dimililer, K., & Tuncal, K. (2019). 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). ACM.
Shaziya, H., Zaheer, R., & Kavitha, G. (2015). Prediction of students’ performance in semester exams using a Naïve Bayes classifier. International Journal of Innovative Research in Science, Engineering and Technology, 4(10), 9823-9829.
Singh, R., & Pal, S. (2020). Application of machine Learning Algorithms to predict students’ performance. International Journal of Advanced Science and Technology, 29(5), 7249-7261.
Soule, P. (2017). Predicting Student Success: A Logistic Regression Analysis of Data from Multiple SIU-C Courses.
Sultana, M., J. Rani,U. & Farquad, M.A.H. (2019). Student’s Performance Prediction using Deep Learning and Data Mining Methods, International Journal of Recent Technology and Engineering, 8(1S4), 1018-1021.
Swamy, M. N., & Hanumanthappa, M. (2012). Predicting academic success from student enrolment data using decision tree technique. Int. J. Appl. Inf. Syst, 4(3), 1-6.
Umek, L., Tomaževic, N., Aristovnik, A., & Keržic, D. (2017). Predictors of Student Performance in a Blended-Learning Environment: An Empirical Investigation. International Association for Development of the Information Society.
Usman, O. L., & Adenubi, A. O. (2013). Artificial neural network (ANN) model for predicting students’ academic performance. Journal of Science and Information Technology, 1(2), 23-37.
Vinod K. P. & Bhatt, V. K. K. (2019). Performance Prediction for Post Graduate Students using Artificial Neural Network, International Journal of Innovative Technology and Exploring Engineering, 8(7S2),446-454.
Yadav, S. K., & Pal, S. (2012). Data mining: A prediction for performance improvement of engineering students using classification. arXiv preprint arXiv:1203.3832.
Published
2023-08-28
How to Cite
Ofori, F., Matheka, A., & Maina, E. (2023). CRITICAL LITERATURE REVIEW ON CURRENT STATE-OF-THE ART IN PREDICTING STUDENTS’ PERFORMANCE USING MACHINE LEARNING ALGORITHM IN BLENDED LEARNING ENVIRONMENT. African Journal of Emerging Issues, 5(12), 23 - 38. Retrieved from https://ajoeijournals.org/sys/index.php/ajoei/article/view/465
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