Skip to main content

Advertisement

SpringerLink
Log in

Educational data mining to predict students' academic performance: A survey study

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

Educational data mining is an emerging interdisciplinary research area involving both education and informatics. It has become an imperative research area due to many advantages that educational institutions can achieve. Along these lines, various data mining techniques have been used to improve learning outcomes by exploring large-scale data that come from educational settings. One of the main problems is predicting the future achievements of students before taking final exams, so we can proactively help students achieve better performance and prevent dropouts. Therefore, many efforts have been made to solve the problem of student performance prediction in the context of educational data mining. In this paper, we provide readers with a comprehensive understanding of student performance prediction and compare approximately 260 studies in the last 20 years with respect to i) major factors highly affecting student performance prediction, ii) kinds of data mining techniques including prediction and feature selection algorithms, and iii) frequently used data mining tools. The findings of the comprehensive analysis show that ANN and Random Forest are mostly used data mining algorithms, while WEKA is found as a trending tool for students’ performance prediction. Students’ academic records and demographic factors are the best attributes to predict performance. The study proves that irrelevant features in the dataset reduce the prediction results and increase model processing time. Therefore, almost half of the studies used feature selection techniques before building prediction models. This study attempts to provide useful and valuable information to researchers interested in advancing educational data mining. The study directs future researchers to achieve highly accurate prediction results in different scenarios using different available inputs or techniques. The study also helps institutions apply data mining techniques to predict and improve student outcomes by providing additional assistance on time.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. https://analyse.kmi.open.ac.uk/open_dataset

  2. https://www.mooc.org/

  3. https://moodle.org/

  4. https://archive.ics.uci.edu/ml/datasets/student+performance

References

  • Abu Tair, M. M., & El-Halees, A. M. (2012). Mining educational data to improve students' performance: A case study. International Journal of Information, 2(2).

  • Abubakar, Y., & Ahmad, N. B. H. (2017). Prediction of students’ performance in e-learning environment using random forest. International Journal of Innovative Computing, 7(2).

  • Acharya, A., & Sinha, D. (2014). Early prediction of students performance using machine learning techniques. International Journal of Computer Applications, 107(1).

  • Adebayo, A. O., & Chaubey, M. S. (2019). Data mining classification techniques on the analysis of student’s performance. GSJ, 7(4), 45–52.

    Google Scholar 

  • Adejo, O. W., & Connolly, T. (2018). Predicting student academic performance using multi-model heterogeneous ensemble approach. Journal of Applied Research in Higher Education.

  • Adekitan, A. I., & Noma-Osaghae, E. (2019). Data mining approach to predicting the performance of first year student in a university using the admission requirements. Education and Information Technologies, 24(2), 1527–1543.

    Article  Google Scholar 

  • Adekitan, A. I., & Salau, O. (2019). The impact of engineering students' performance in the first three years on their graduation result using educational data mining. Heliyon, 5(2), e01250.

    Article  Google Scholar 

  • Adekitan, A. I., & Salau, O. (2020). Toward an improved learning process: The relevance of ethnicity to data mining prediction of students’ performance. SN Applied Sciences, 2(1), 1–15.

    Article  Google Scholar 

  • Adhatrao, K., Gaykar, A., Dhawan, A., Jha, R., & Honrao, V. (2013). Predicting students' performance using ID3 and C4. In 5 classification algorithms arXiv preprint arXiv:1310.2071.

    Google Scholar 

  • Agrawal, H., & Mavani, H. (2015). Student performance prediction using machine learning. International Journal of Engineering Research and Technology, 4(03), 111–113.

    Google Scholar 

  • Ahmad, F., Ismail, N. H., & Aziz, A. A. (2015). The prediction of students’ academic performance using classification data mining techniques. Applied Mathematical Sciences, 9(129), 6415–6426.

    Article  Google Scholar 

  • Ahmad, Z., & Shahzadi, E. (2018). Prediction of students' academic performance using artificial neural network. Bulletin of Education and Research, 40(3), 157–164.

    Google Scholar 

  • Ahmed, M. R., Tahid, S. T. I., Mitu, N. A., Kundu, P., & Yeasmin, S. (2020a). A comprehensive analysis on undergraduate student academic performance using feature selection techniques on classification algorithms. In Paper presented at the 2020 11th international conference on computing, communication and networking technologies (ICCCNT).

    Google Scholar 

  • Ahmed, S. A., Billah, M. A., & Khan, S. I. (2020b). A machine learning approach to performance and dropout prediction in computer science: Bangladesh perspective. In Paper presented at the 2020 11th international conference on computing, communication and networking technologies (ICCCNT).

    Google Scholar 

  • Ahmed, S. T., Al-Hamdani, R., & Croock, M. S. (2020c). Enhancement of student performance prediction using modified K-nearest neighbor. Telkomnika, 18(4), 1777–1783.

    Article  Google Scholar 

  • Aina, C., Baici, E., Casalone, G., & Pastore, F. (2021). The determinants of university dropout: A review of the socio-economic literature. Socio-Economic Planning Sciences, 101102.

  • Akçapınar, G., Hasnine, M. N., Majumdar, R., Flanagan, B., & Ogata, H. (2019). Developing an early-warning system for spotting at-risk students by using eBook interaction logs. Smart Learning Environments, 6(1), 4.

    Article  Google Scholar 

  • Akinrotimi, A. O., Aremu, D. R., & Reuben, D. (2018). Student performance prediction using random student performance prediction using random tree and C4. 5 Algorithm ree and C4. 5 Algorithm.

    Google Scholar 

  • Al-Obeidat, F., Tubaishat, A., Dillon, A., & Shah, B. (2018). Analyzing students’ performance using multi-criteria classification. Cluster Computing, 21(1), 623–632.

    Article  Google Scholar 

  • Al-Radaideh, Q. A., Al-Shawakfa, E. M., & Al-Najjar, M. I. (2006). Mining student data using decision trees. In Paper presented at the international Arab conference on information technology (ACIT'2006). Yarmouk University.

    Google Scholar 

  • Alhassan, A., Zafar, B., & Mueen, A. (2020). Predict students' academic performance based on their assessment grades and online activity data. International Journal of Advanced Computer Science and Applications (IJACSA), 11(4).

  • Aljohani, N. R., Fayoumi, A., & Hassan, S.-U. (2019). Predicting at-risk students using clickstream data in the virtual learning environment. Sustainability, 11(24), 7238.

    Article  Google Scholar 

  • Alloghani, M., Al-Jumeily, D., Baker, T., Hussain, A., Mustafina, J., & Aljaaf, A. J. (2018). Applications of machine learning techniques for software engineering learning and early prediction of students’ performance. In Paper presented at the international conference on soft computing in data science.

    Google Scholar 

  • Altaf, S., Soomro, W., & Rawi, M. I. M. (2019). Student performance prediction using multi-layers artificial neural networks: A case study on educational data mining. In Paper presented at the proceedings of the 2019 3rd international conference on information system and data mining.

    Google Scholar 

  • Altujjar, Y., Altamimi, W., Al-Turaiki, I., & Al-Razgan, M. (2016). Predicting critical courses affecting students performance: A case study. Procedia Computer Science, 82, 65–71.

    Article  Google Scholar 

  • Aluko, R. O., Adenuga, O. A., Kukoyi, P. O., Soyingbe, A. A., & Oyedeji, J. O. (2016). Predicting the academic success of architecture students by pre-enrolment requirement: Using machine-learning techniques. Construction Economics and Building, 16(4), 86.

    Article  Google Scholar 

  • Aluko, R. O., Daniel, E. I., Oshodi, O. S., Aigbavboa, C. O., & Abisuga, A. O. (2018). Towards reliable prediction of academic performance of architecture students using data mining techniques. Journal of Engineering, Design and Technology.

  • Aman, F., Rauf, A., Ali, R., Iqbal, F., & Khattak, A. M. (2019). A predictive model for predicting students academic performance. In Paper presented at the 2019 10th international conference on information, intelligence, systems and applications (IISA).

    Google Scholar 

  • Amazona, M. V., & Hernandez, A. A. (2019). Modelling student performance using data mining techniques: Inputs for academic program development. In Paper presented at the proceedings of the 2019 5th international conference on computing and data engineering.

    Google Scholar 

  • Amra, I. A. A., & Maghari, A. Y. (2017). Students performance prediction using KNN and Naïve Bayesian. In Paper presented at the 2017 8th international conference on information technology (ICIT).

    Google Scholar 

  • Amrieh, E. A., Hamtini, T., & Aljarah, I. (2016). Mining educational data to predict student’s academic performance using ensemble methods. International Journal of Database Theory and Application, 9(8), 119–136.

    Article  Google Scholar 

  • Anoopkumar, M., & Rahman, A. (2018). Model of tuned J48 classification and analysis of performance prediction in educational data mining. The International Journal of Applied Engineering Research (IJAER), 13(20), 14717–14727.

    Google Scholar 

  • Anuradha, C., & Velmurugan, T. (2015). A comparative analysis on the evaluation of classification algorithms in the prediction of students performance. Indian Journal of Science and Technology, 8(15), 1–12.

    Article  Google Scholar 

  • Anwar, M. A., & Rani, R. (2020). Data science for prediction of grades in a mathematics course based on performance in its prerequisites.

    Google Scholar 

  • Arsad, P. M., & Buniyamin, N. (2013). A neural network students' performance prediction model (NNSPPM). In Paper presented at the 2013 IEEE international conference on smart instrumentation, measurement and applications (ICSIMA).

    Google Scholar 

  • Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students' performance using educational data mining. Computers & Education, 113, 177–194.

    Article  Google Scholar 

  • Asogbon, M. G., Samuel, O. W., Omisore, M. O., & Ojokoh, B. A. (2016). A multi-class support vector machine approach for students academic performance prediction (p. 4).

    Google Scholar 

  • Aydoğdu, Ş. (2020). Predicting student final performance using artificial neural networks in online learning environments. Education and Information Technologies, 25(3), 1913–1927.

    Article  Google Scholar 

  • Banu, S. R., & Manjupargavi, R. (2021). Performance analysis and prediction of students results using machine learning and big data approach. In Paper presented at the in 2021 2nd international conference on smart electronics and communication (ICOSEC).

    Google Scholar 

  • Baradwaj, B. K., & Pal, S. (2012). Mining educational data to analyze students' performance. arXiv preprint arXiv, 1201.3417.

  • Barbosa Manhães, L. M., da Cruz, S. M. S., & Zimbrão, G. (2015). Towards automatic prediction of student performance in STEM undergraduate degree programs. In Paper presented at the proceedings of the 30th annual ACM symposium on applied computing.

    Google Scholar 

  • Batool, S., Rashid, J., Nisar, M. W., Kim, J., Mahmood, T., & Hussain, A. (2021). A random forest students’ performance prediction (rfspp) model based on students’ demographic features. In Paper presented at the 2021 Mohammad Ali Jinnah University international conference on computing (MAJICC).

    Google Scholar 

  • Bekele, R., & McPherson, M. (2011). A Bayesian performance prediction model for mathematics education: A prototypical approach for effective group composition. British Journal of Educational Technology, 42(3), 395–416.

    Article  Google Scholar 

  • Bekele, R., & Menzel, W. (2005). A bayesian approach to predict performance of a student (bapps): A case with ethiopian students. Algorithms, 22(23), 24.

    Google Scholar 

  • Bhardwaj, B. K., & Pal, S. (2012). Data mining: A prediction for performance improvement using classification. arXiv preprint arXiv:1201.3418.

    Google Scholar 

  • Bhutto, E. S., Siddiqui, I. F., Arain, Q. A., & Anwar, M. (2020). Predicting students’ academic performance through supervised machine learning. In Paper presented at the 2020 international conference on information science and communication technology (ICISCT).

    Google Scholar 

  • Borges, V. R. P., Esteves, S., de Nardi Araújo, P., de Oliveira, L. C., & Holanda, M. (2018). Using principal component analysis to support students' performance prediction and data analysis. In Paper presented at the Brazilian symposium on computers in education (Simpósio Brasileiro de Informática na Educação-SBIE).

    Google Scholar 

  • Bravo, L. E. C., Molano, J. I. R., & Trujillo, E. R. (2020). Exploration of a system to determine the academic performance of engineering students through machine learning. International Journal of Advanced Science and Technology, 29(7), 11894–11905.

    Google Scholar 

  • Bresfelean, V. P. (2007). Analysis and predictions on students' behavior using decision trees in Weka environment. In Paper presented at the 2007 29th international conference on information technology interfaces.

    Google Scholar 

  • Brinton, C. G., & Chiang, M. (2015). MOOC performance prediction via clickstream data and social learning networks. In Paper presented at the 2015 IEEE conference on computer communications (INFOCOM).

    Google Scholar 

  • Bruce, A. (2019). The prediction of student performance through the use of machine learning.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Burgos, C., Campanario, M. L., de la Peña, D., Lara, J. A., Lizcano, D., & Martínez, M. A. (2018). Data mining for modeling students’ performance: A tutoring action plan to prevent academic dropout. Computers & Electrical Engineering, 66, 541–556.

    Article  Google Scholar 

  • Burman, I., & Som, S. (2019). Predicting students academic performance using support vector machine. In Paper presented at the 2019 Amity International conference on artificial intelligence (AICAI).

    Google Scholar 

  • Calvo-Flores, M. D., Galindo, E. G., Jiménez, M. P., & Pineiro, O. P. (2006). Predicting students’ marks from Moodle logs using neural network models. Current Developments in Technology-Assisted Education, 1(2), 586–590.

    Google Scholar 

  • Cao, Y., Gao, J., Lian, D., Rong, Z., Shi, J., Wang, Q., & Zhou, T. (2018). Orderliness predicts academic performance: Behavioural analysis on campus lifestyle. Journal of the Royal Society Interface, 15(146), 20180210.

    Article  Google Scholar 

  • Cavazos, R., & Garza, S. E. (2017). Learning models for student performance prediction. Paper presented at the Mexican International Conference on Artificial Intelligence.

  • Çevik, M., & Tabaru-Örnek, G. (2020). Comparison of MATLAB and SPSS software in the prediction of academic achievement with artificial neural networks: Modeling for elementary school students. International Online Journal of Education and Teaching, 7(4), 1689–1707.

    Google Scholar 

  • Chand, K. S. P., Prabakaran, N., Ramani, S., Rao, D. V., & Vemparala, S. (2020). Assessment analysis and performance prediction using M5 rules.

    Google Scholar 

  • Chang, C.-T., Tu, C.-S., & Hajiyev, J. (2019). Integrating academic type of social media activity with perceived academic performance: A role of task-related and non-task-related compulsive internet use. Computers & Education, 139, 157–172.

    Article  Google Scholar 

  • Chanlekha, H., & Niramitranon, J. (2018). Student performance prediction model for early-identification of at-risk students in traditional classroom settings. In Paper presented at the proceedings of the 10th international conference on Management of Digital EcoSystems.

    Google Scholar 

  • Chen, Y., Zheng, Q., Ji, S., Tian, F., Zhu, H., & Liu, M. (2020). Identifying at-risk students based on the phased prediction model. Knowledge and Information Systems, 62(3), 987–1003.

    Article  Google Scholar 

  • Chounta, I.-A., & Carvalho, P. F. (2019). Square it up! How to model step duration when predicting student performance. In Paper presented at the proceedings of the 9th international conference on Learning Analytics & Knowledge.

    Google Scholar 

  • Chui, K. T., Fung, D. C. L., Lytras, M. D., & Lam, T. M. (2020). Predicting at-risk university students in a virtual learning environment via a machine learning algorithm. Computers in Human Behavior, 107, 105584.

    Article  Google Scholar 

  • Costa, E. B., Fonseca, B., Santana, M. A., de Araújo, F. F., & Rego, J. (2017). Evaluating the effectiveness of educational data mining techniques for early prediction of students' academic failure in introductory programming courses. Computers in Human Behavior, 73, 247–256.

    Article  Google Scholar 

  • Coussement, K., Phan, M., De Caigny, A., Benoit, D. F., & Raes, A. (2020). Predicting student dropout in subscription-based online learning environments: The beneficial impact of the logit leaf model. Decision Support Systems, 135, 113325.

    Article  Google Scholar 

  • Czibula, G., Mihai, A., & Crivei, L. M. (2019). S PRAR: A novel relational association rule mining classification model applied for academic performance prediction. Procedia Computer Science, 159, 20–29.

    Article  Google Scholar 

  • Daud, A., Aljohani, N. R., Abbasi, R. A., Lytras, M. D., Abbas, F., & Alowibdi, J. S. (2017). Predicting student performance using advanced learning analytics. In Paper presented at the proceedings of the 26th international conference on world wide web companion.

    Google Scholar 

  • Deepika, K., & Sathyanarayana, N. (2019). Relief-F and budget tree random forest based feature selection for student academic performance prediction. International Journal of Intelligent Engineering and Systems, 12(1), 30–39.

    Article  Google Scholar 

  • Devasia, T., Vinushree, T., & Hegde, V. (2016). Prediction of students performance using educational data mining. In Paper presented at the 2016 international conference on data mining and advanced computing (SAPIENCE).

    Google Scholar 

  • DEY, A. (2020). Prediction and analysis of student performance by data mining in WEKA. West Bengal University of Technology.

    Google Scholar 

  • Evwiekpaefe, A. E., Isa, M. M., & Ajakaiye, F. (2014). Analyzing factors affecting academic performance of postgraduate students using data mining techniques.

    Google Scholar 

  • Fachrie, M. (2019). Development of educational data mining model for predicting student punctuality and graduation predicate. International Journal of Technology and Engineering Studies, 5(5), 151–156.

    Article  Google Scholar 

  • Farissi, A., & Dahlan, H. M. (2020). Genetic algorithm based feature selection with ensemble methods for student academic performance prediction. In Paper presented at the journal of physics: Conference series.

    Google Scholar 

  • Fernandes, E., Holanda, M., Victorino, M., Borges, V., Carvalho, R., & Van Erven, G. (2019). Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil. Journal of Business Research, 94, 335–343.

    Article  Google Scholar 

  • Figueroa-Cañas, J., & Sancho-Vinuesa, T. (2020). Early prediction of dropout and final exam performance in an online statistics course. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 15(2), 86–94.

    Article  Google Scholar 

  • Francis, B. K., & Babu, S. S. (2019). Predicting academic performance of students using a hybrid data mining approach. Journal of Medical Systems, 43(6), 1–15.

    Article  Google Scholar 

  • Freitas, F., Vasconcelos, F., Peixoto, F., Hassan, M., Ali Akber Dewan, M., & de Albuquerque, V. (2020). IoT system for school dropout prediction using machine learning techniques based on socioeconomic data. Electronics, 9(10), 1613.

    Article  Google Scholar 

  • Gil, P. D., da Cruz Martins, S., Moro, S., & Costa, J. M. (2020). A data-driven approach to predict first-year students’ academic success in higher education institutions. Education and Information Technologies, 1–26.

  • Guo, B., Zhang, R., Xu, G., Shi, C., & Yang, L. (2015). Predicting students performance in educational data mining. In Paper presented at the 2015 international symposium on educational technology (ISET).

    Google Scholar 

  • Hamoud, A. (2016). Selection of best decision tree algorithm for prediction and classification of students’ action. American International Journal of Research in Science, Technology, Engineering & Mathematics, 16(1), 26–32.

    Google Scholar 

  • Hamoud, A., Hashim, A. S., & Awadh, W. A. (2018). Predicting student performance in higher education institutions using decision tree analysis. International Journal of Interactive Multimedia and Artificial Intelligence, 5, 26–31.

    Article  Google Scholar 

  • Hamoud, A., & Humadi, A. (2019). Student’s success prediction model based on artificial neural networks (ANN) and a combination of feature selection methods. Journal of Southwest Jiaotong University, 54(3).

  • Hamsa, H., Indiradevi, S., & Kizhakkethottam, J. J. (2016). Student academic performance prediction model using decision tree and fuzzy genetic algorithm. Procedia Technology, 25, 326–332.

    Article  Google Scholar 

  • Harvey, J. L., & Kumar, S. A. (2019). A practical model for educators to predict student performance in K-12 education using machine learning. In Paper presented at the 2019 IEEE symposium series on computational intelligence (SSCI).

    Google Scholar 

  • Hasan, H. R., Rabby, A. S. A., Islam, M. T., & Hossain, S. A. (2019). Machine learning algorithm for student's performance prediction. In Paper presented at the 2019 10th international conference on computing, communication and networking technologies (ICCCNT).

    Google Scholar 

  • Hasan, M. (2019). Predicting student performance to reduce dropout using J48 decision tree algorithm. Daffodil International University.

    Google Scholar 

  • He, Y., Chen, R., Li, X., Hao, C., Liu, S., Zhang, G., & Jiang, B. (2020). Online at-risk student identification using RNN-GRU joint neural networks. Information, 11(10), 474.

    Article  Google Scholar 

  • Helal, S., Li, J., Liu, L., Ebrahimie, E., Dawson, S., Murray, D. J., & Long, Q. (2018). Predicting academic performance by considering student heterogeneity. Knowledge-Based Systems, 161, 134–146.

    Article  Google Scholar 

  • Herzog, S. (2006). Estimating student retention and degree-completion time: Decision trees and neural networks Vis-à-Vis regression. New Directions for Institutional Research, 2006(131), 17–33.

    Article  Google Scholar 

  • Heuer, H., & Breiter, A. (2018). Student success prediction and the trade-off between big data and data minimization. In DeLFI 2018-Die 16. Fachtagung Informatik.

    Google Scholar 

  • Hew, K. F., Hu, X., Qiao, C., & Tang, Y. (2020). What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach. Computers & Education, 145, 103724.

    Article  Google Scholar 

  • Hidayah, I., Permanasari, A. E., & Ratwastuti, N. (2013). Student classification for academic performance prediction using neuro fuzzy in a conventional classroom. In Paper presented at the 2013 international conference on information technology and electrical engineering (ICITEE).

    Google Scholar 

  • Howard, E., Meehan, M., & Parnell, A. (2018). Contrasting prediction methods for early warning systems at undergraduate level. The Internet and Higher Education, 37, 66–75.

    Article  Google Scholar 

  • Hsu, P.-L., Lai, R., & Chiu, C. (2003). The hybrid of association rule algorithms and genetic algorithms for tree induction: An example of predicting the student course performance. Expert Systems with Applications, 25(1), 51–62.

    Article  Google Scholar 

  • Hu, Y.-H., Lo, C.-L., & Shih, S.-P. (2014). Developing early warning systems to predict students’ online learning performance. Computers in Human Behavior, 36, 469–478.

    Article  Google Scholar 

  • Huang, S., & Fang, N. (2013). Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models. Computers & Education, 61, 133–145.

    Article  Google Scholar 

  • Hughes, G., & Dobbins, C. (2015). The utilization of data analysis techniques in predicting student performance in massive open online courses (MOOCs). Research and Practice in Technology Enhanced Learning, 10(1), 1–18.

    Article  Google Scholar 

  • Hussain, S., Dahan, N. A., Ba-Alwib, F. M., & Ribata, N. (2018). Educational data mining and analysis of students’ academic performance using WEKA. Indonesian Journal of Electrical Engineering and Computer Science, 9(2), 447–459.

    Article  Google Scholar 

  • Hussain, S., Muhsion, Z. F., Salal, Y. K., Theodorou, P., Kurtoglu, F., & Hazarika, G. (2019). Prediction model on student performance based on internal assessment using deep learning. iJET, 14(8), 4–22.

    Google Scholar 

  • Iam-On, N., & Boongoen, T. (2017). Improved student dropout prediction in Thai University using ensemble of mixed-type data clusterings. International Journal of Machine Learning and Cybernetics, 8(2), 497–510.

    Article  Google Scholar 

  • Ilic, M., Spalevic, P., Veinovic, M., & Alatresh, W. S. (2016). Students’ success prediction using Weka tool. Infoteh-Jahorina, 15, 684–688.

    Google Scholar 

  • Iyanda, A. R., Ninan, O. D., Ajayi, A. O., & Anyabolu, O. G. (2018). Predicting student academic performance in computer science courses: A comparison of neural network models. International Journal of Modern Education & Computer Science, 10(6).

  • Jha, N. I., Ghergulescu, I., & Moldovan, A.-N. (2019). OULAD MOOC dropout and result prediction using ensemble, deep learning and regression techniques. In Paper presented at the CSEDU (2).

    Google Scholar 

  • Jishan, S. T., Rashu, R. I., Haque, N., & Rahman, R. M. (2015). Improving accuracy of students’ final grade prediction model using optimal equal width binning and synthetic minority over-sampling technique. Decision Analytics, 2(1), 1–25.

    Article  Google Scholar 

  • Kabakchieva, D. (2013). Predicting student performance by using data mining methods for classification. Cybernetics and information technologies, 13(1), 61–72.

    Article  Google Scholar 

  • Kabra, R., & Bichkar, R. (2011). Performance prediction of engineering students using decision trees. International Journal of Computer Applications, 36(11), 8–12.

    Google Scholar 

  • Kalles, D., & Pierrakeas, C. (2006). Analyzing student performance in distance learning with genetic algorithms and decision trees. Applied Artificial Intelligence, 20(8), 655–674.

    Article  Google Scholar 

  • Kamal, P., & Ahuja, S. (2019). An ensemble-based model for prediction of academic performance of students in undergrad professional course. Journal of Engineering.

    Book  Google Scholar 

  • Karamouzis, S. T., & Vrettos, A. (2008). An artificial neural network for predicting student graduation outcomes. In Paper presented at the proceedings of the world congress on engineering and computer science.

    Google Scholar 

  • Karimi, H., Derr, T., Huang, J., & Tang, J. (2020). Online academic course performance prediction using relational graph convolutional neural network. In Paper presented at the proceedings of the 13th international conference on educational data mining (EDM 2020).

    Google Scholar 

  • Karlık, M., & Karlık, B. (2020). Prediction of student’s performance with deep neural networks. International Journal of Artificial Intelligence and Expert Systems (IJAE).

  • Kaur, G., & Singh, W. (2016). Prediction of student performance using weka tool. An International Journal of Engineering Sciences, 17, 8–16.

    Google Scholar 

  • Kaur, H., & Bathla, E. G. (2018). Student performance prediction using educational data mining techniques. International journal on future revolution in Computer Science & Communication. Engineering, 4(12), 93–97-93–97.

    Google Scholar 

  • Kaur, P., Singh, M., & Josan, G. S. (2015). Classification and prediction based data mining algorithms to predict slow learners in education sector. Procedia Computer Science, 57, 500–508.

    Article  Google Scholar 

  • Kaviyarasi, R., & Balasubramanian, T. (2018). Exploring the high potential factors that affects students’ academic performance. International Journal of Education and Management Engineering, 8(6), 15–23.

    Article  Google Scholar 

  • Kaviyarasi, R., & Balasubramanian, T. (2020). Predictive analysis of academic performance of college students using ensemble stacking. Kongunadu Research Journal, 7(2), 94–98.

    Article  Google Scholar 

  • Kemper, L., Vorhoff, G., & Wigger, B. U. (2020). Predicting student dropout: A machine learning approach. European Journal of Higher Education, 10(1), 28–47.

    Article  Google Scholar 

  • Khan, A., & Ghosh, S. K. (2021). Student performance analysis and prediction in classroom learning: A review of educational data mining studies. Education and Information Technologies, 26(1), 205–240.

    Article  Google Scholar 

  • Khasanah, A. U. (2017). A comparative study to predict student’s performance using educational data mining techniques. In Paper presented at the IOP conference series: Materials science and engineering.

    Google Scholar 

  • Khazaaleh, M. K. (2020). Predictive model to predict the test scores of the computer skills-2 course for future students .

    Google Scholar 

  • Kiu, C.-C. (2018). Data mining analysis on student’s academic performance through exploration of student’s background and social activities. In Paper presented at the 2018 fourth international conference on advances in computing, Communication & Automation (ICACCA).

    Google Scholar 

  • Kotsiantis, S., Patriarcheas, K., & Xenos, M. (2010). A combinational incremental ensemble of classifiers as a technique for predicting students’ performance in distance education. Knowledge-Based Systems, 23(6), 529–535.

    Article  Google Scholar 

  • Kotsiantis, S., Pierrakeas, C., & Pintelas, P. (2002). Efficiency of machine learning techniques in predicting students’ performance in distance learning systems. University of Patras, Greece.

    Google Scholar 

  • Kotsiantis, S. B., & Pintelas, P. E. (2005). Predicting students marks in hellenic open university. In Paper presented at the fifth IEEE international conference on advanced learning technologies (ICALT'05).

    Google Scholar 

  • Koutina, M., & Kermanidis, K. L. (2011). Predicting postgraduate students’ performance using machine learning techniques. In Artificial intelligence applications and innovations (pp. 159–168). Springer.

    Chapter  Google Scholar 

  • Kovacic, Z. (2010). Early prediction of student success: Mining students' enrolment data.

    Google Scholar 

  • Kulkarni, P., & Ade, R. (2014). Prediction of student’s performance based on incremental learning. International Journal of Computer Applications, 99(14), 10–16.

    Article  Google Scholar 

  • Kumar, T. R., Vamsidhar, T., Harika, B., Kumar, T. M., & Nissy, R. (2019). Students performance prediction using data mining techniques. In Paper presented at the 2019 international conference on intelligent sustainable systems (ICISS).

    Google Scholar 

  • Kumar, V., & Garg, M. (2019). Comparison of machine learning models in student result prediction. In Paper presented at the international conference on advanced computing networking and informatics.

    Google Scholar 

  • Lenin, T., & Chandrasekaran, N. (2019). Students’ performance prediction Modelling using classification technique in R.

    Book  Google Scholar 

  • Li, J., Sun, S., Yin, H., Dawson, P., & Doss, R. (2020). SEPN: A sequential engagement based academic performance prediction model. IEEE Intelligent Systems.

  • Liang, J., Li, C., & Zheng, L. (2016). Machine learning application in MOOCs: Dropout prediction. In Paper presented at the 2016 11th international conference on Computer Science & Education (ICCSE).

    Google Scholar 

  • Lin, J., Imbrie, P., & Reid, K. J. (2009). Student retention modelling: An evaluation of different methods and their impact on prediction results. Research in Engineering Education Sysmposium, 1–6.

  • Liu, H., Zhu, Y., Zang, T., Yu, J., & Cai, H. (2020). Jointly modeling individual student behaviors and social influence for prediction tasks. In Paper presented at the proceedings of the 29th ACM international conference on Information & Knowledge Management.

    Google Scholar 

  • Liu, W. (2019). An improved back-propagation neural network for the prediction of college students' english performance. International Journal of Emerging Technologies in Learning, 14(16).

  • Livieris, I. E., Drakopoulou, K., Mikropoulos, T. A., Tampakas, V., & Pintelas, P. (2018). An ensemble-based semi-supervised approach for predicting students’ performance. In Research on e-learning and ICT in education (pp. 25–42). Springer.

    Chapter  Google Scholar 

  • Lottering, R., Hans, R., & Lall, M. (2020). A model for the identification of students at risk of dropout at a university of technology. In Paper presented at the 2020 international conference on artificial intelligence, big data, computing and data communication systems (icABCD).

    Google Scholar 

  • Lu, H., & Yuan, J. (2018). Student performance prediction model based on discriminative feature selection. International Journal of Emerging Technologies in Learning, 13(10).

  • Lykourentzou, I., Giannoukos, I., Mpardis, G., Nikolopoulos, V., & Loumos, V. (2009). Early and dynamic student achievement prediction in e-learning courses using neural networks. Journal of the American Society for Information Science and Technology, 60(2), 372–380.

    Article  Google Scholar 

  • Majeed, E. A., & Junejo, K. N. (2016). Grade prediction using supervised machine learning techniques. In E-proceedings of the 4th global summit on education.

    Google Scholar 

  • Makombe, F., & Lall, M. (2020). A predictive model for the determination of academic performance in private higher education institutions. International Journal of Advanced Computer Science and Applications (IJACSA), 11(9).

  • Marbouti, F., Diefes-Dux, H. A., & Madhavan, K. (2016). Models for early prediction of at-risk students in a course using standards-based grading. Computers & Education, 103, 1–15.

    Article  Google Scholar 

  • Márquez-Vera, C., Cano, A., Romero, C., Noaman, A. Y. M., Mousa Fardoun, H., & Ventura, S. (2016). Early dropout prediction using data mining: A case study with high school students. Expert Systems, 33(1), 107–124.

    Article  Google Scholar 

  • Masood, M. F., Khan, A., Hussain, F., Shaukat, A., Zeb, B., & Ullah, R. M. K. (2019). Towards the selection of best machine learning model for student performance analysis and prediction. In Paper presented at the 2019 6th international conference on Soft Computing & Machine Intelligence (ISCMI).

    Google Scholar 

  • Mativo, J. M., & Huang, S. (2014). Prediction of students' academic performance: Adapt a methodology of predictive modeling for a small sample size. In Paper presented at the 2014 IEEE Frontiers in education conference (FIE) proceedings.

    Google Scholar 

  • Meghji, A. F., Mahoto, N. A., Unar, M. A., & Shaikh, M. A. (2019). Predicting student academic performance using data generated in higher educational institutes.

    Book  Google Scholar 

  • Mengash, H. A. (2020). Using data mining techniques to predict student performance to support decision making in university admission systems. IEEE Access, 8, 55462–55470.

    Article  Google Scholar 

  • Mi, C. (2019). Data-driven student learning performance prediction based on RBF neural network. International Journal of Performability Engineering, 15(6), 1560.

    Google Scholar 

  • Mi, C., Peng, X., Cai, Z., Deng, Q., & Zhao, C. (2018). A genetic algorithm based method of early warning rule mining for student performance prediction. In Paper presented at the international conference on cloud computing and security.

    Google Scholar 

  • Miguéis, V. L., Freitas, A., Garcia, P. J., & Silva, A. (2018). Early segmentation of students according to their academic performance: A predictive modelling approach. Decision Support Systems, 115, 36–51.

    Article  Google Scholar 

  • Mikroskil, S. (2019). Information systems students’ study performance prediction using data mining approach.

    Google Scholar 

  • Mishra, T., & Kumawat, C. (2018). Critical evaluation of classification algorithms for performance prediction in higher education setup.

    Google Scholar 

  • Moreno-Marcos, P. M., Pong, T.-C., Munoz-Merino, P. J., & Kloos, C. D. (2020). Analysis of the factors influencing learners’ performance prediction with learning analytics. IEEE Access, 8, 5264–5282.

    Article  Google Scholar 

  • Moseley, L. G., & Mead, D. M. (2008). Predicting who will drop out of nursing courses: A machine learning exercise. Nurse Education Today, 28(4), 469–475.

    Article  Google Scholar 

  • Mubarak, A. A., Cao, H., & Zhang, W. (2020). Prediction of students’ early dropout based on their interaction logs in online learning environment. Interactive Learning Environments, 1–20.

  • Mueen, A., Zafar, B., & Manzoor, U. (2016). Modeling and predicting students' academic performance using data mining techniques. International Journal of Modern Education & Computer Science, 8(11).

  • Mutanu, L., & Machoka, P. (2019). Enhancing computer students’ academic performance through predictive modelling-a proactive approach. In Paper presented at the 2019 14th international conference on Computer Science & Education (ICCSE).

    Google Scholar 

  • Nabizadeh, S., Hajian, S., Sheikhan, Z., & Rafiei, F. (2019). Prediction of academic achievement based on learning strategies and outcome expectations among medical students. BMC Medical Education, 19(1), 99.

    Article  Google Scholar 

  • Naicker, N., Adeliyi, T., & Wing, J. (2020). Linear support vector machines for prediction of student performance in school-based education. mathematical problems in engineering, 2020.

  • Namoun, A., & Alshanqiti, A. (2021). Predicting student performance using data mining and learning analytics techniques: A systematic literature review. Applied Sciences, 11(1), 237.

    Article  Google Scholar 

  • Nandeshwar, A., & Chaudhari, S. (2009). Enrollment prediction models using data mining. Retrieved January, 10, 2010.

  • Narayanasamy, S. K., & Elçi, A. (2020). An effective prediction model for online course dropout rate. International Journal of Distance Education Technologies (IJDET), 18(4), 94–110.

    Article  Google Scholar 

  • Nghe, N. T., Janecek, P., & Haddawy, P. (2007). A comparative analysis of techniques for predicting academic performance. In Paper presented at the 2007 37th annual frontiers in education conference-global engineering: Knowledge without borders, opportunities without passports.

    Google Scholar 

  • Nuankaew, W., & Thongkam, J. (2020). Improving student academic performance prediction models using feature selection. In Paper presented at the 2020 17th international conference on electrical engineering/electronics, computer, telecommunications and information technology (ECTI-CON).

    Google Scholar 

  • Ogor, E. N. (2007). Student academic performance monitoring and evaluation using data mining techniques. In Paper presented at the electronics, robotics and automotive mechanics conference (CERMA 2007).

    Google Scholar 

  • Olalekan, A. M., Egwuche, O. S., & Olatunji, S. O. (2020). Performance evaluation of machine learning techniques for prediction of graduating students in tertiary institution. In Paper presented at the 2020 international conference in mathematics, computer engineering and computer science (ICMCECS).

    Google Scholar 

  • Osmanbegovic, E., & Suljic, M. (2012). Data mining approach for predicting student performance. Economic Review: Journal of Economics and Business, 10(1), 3–12.

    Google Scholar 

  • Osmanbegović, E., Suljić, M., & Agić, H. (2014). Determining dominant factor for students performance prediction by using data mining classification algorithms. Tranzicija, 16(34), 147–158.

    Google Scholar 

  • Oyefolahan, I. O., Idris, S., Etuk, S. O., & Alabi, I. O. (2018). Academic performance prediction for success rate improvement in higher institutions of learning: An application of data mining classification algorithms.

    Google Scholar 

  • Paliwal, M., & Kumar, U. A. (2009). A study of academic performance of business school graduates using neural network and statistical techniques. Expert Systems with Applications, 36(4), 7865–7872.

    Article  Google Scholar 

  • Pandey, M., & Taruna, S. (2014). A multi-level classification model pertaining to the student's academic performance prediction. International Journal of Advances in Engineering & Technology, 7(4), 1329.

    Google Scholar 

  • Pandey, M., & Taruna, S. (2016). Towards the integration of multiple classifier pertaining to the Student's performance prediction. Perspectives in Science, 8, 364–366.

    Article  Google Scholar 

  • Pandey, M., & Taruna, S. (2018). An ensemble-based decision support system for the students’ academic performance prediction. In ICT Based Innovations (pp. 163–169). Springer.

    Chapter  Google Scholar 

  • Patacsil, F. F. (2020). Survival analysis approach for early prediction of student dropout using enrollment student data and ensemble models. Universal Journal of Educational Research, 8(9), 4036–4047.

    Article  Google Scholar 

  • Patil, P. A., & Mane, R. (2014). Prediction of students performance using frequent pattern tree. In Paper presented at the 2014 international conference on computational intelligence and communication networks.

    Google Scholar 

  • Patil, R., Salunke, S., Kalbhor, M., & Lomte, R. (2018). Prediction system for student performance using data mining classification. In Paper presented at the 2018 fourth international conference on computing communication control and automation (ICCUBEA).

    Google Scholar 

  • Pattanaphanchai, J., Leelertpanyakul, K., & Theppalak, N. (2019). The investigation of student dropout prediction model in thai higher education using educational data mining: A case study of faculty of science, prince of Songkla Uni-versity. Journal of University of Babylon for Pure and Applied Sciences, 27(1), 356–367.

    Article  Google Scholar 

  • Pereira, F. D., Fonseca, S. C., Oliveira, E. H., Oliveira, D. B., Cristea, A. I., & Carvalho, L. S. (2020). Deep learning for early performance prediction of introductory programming students: A comparative and explanatory study. Brazilian Journal of Computers in Education, 28, 723–749.

    Google Scholar 

  • Pereira, F. D., Oliveira, E. H., Fernandes, D., & Cristea, A. (2019). Early performance prediction for CS1 course students using a combination of machine learning and an evolutionary algorithm. In Paper presented at the 2019 IEEE 19th international conference on advanced learning technologies (ICALT).

    Google Scholar 

  • Perez, B., Castellanos, C., & Correal, D. (2018). Applying data mining techniques to predict student dropout: A case study. In Paper presented at the 2018 IEEE 1st Colombian conference on applications in computational intelligence (CoLCACI).

    Google Scholar 

  • Polyzou, A., & Karypis, G. (2018). Feature extraction for classifying students based on their academic performance. International Educational Data Mining Society.

    Google Scholar 

  • Poudyal, S., Nagahi, M., Nagahisarchoghaei, M., & Ghanbari, G. (2020). Machine learning techniques for determining students' academic performance: A sustainable development case for engineering education. In Paper presented at the 2020 international conference on decision aid sciences and application (DASA).

    Google Scholar 

  • Puarungroj, W., Boonsirisumpun, N., Pongpatrakant, P., & Phromkhot, S. (2018). Application of data mining techniques for predicting student success in English exit exam. In Paper presented at the proceedings of the 12th international conference on ubiquitous information management and communication.

    Google Scholar 

  • Qian, R., Sengan, S., & Juneja, S. (2022). English language teaching based on big data analytics in augmentative and alternative communication system (pp. 1–12).

    Google Scholar 

  • Qu, S., Li, K., Fan, Z., Wu, S., Liu, X., & Huang, Z. (2019). Behavior pattern and compiled information based performance prediction in MOOCs. arXiv preprint arXiv:1908.01304.

  • Raga, R. C., & Raga, J. D. (2019). Early prediction of student performance in blended learning courses using deep neural networks. In Paper presented at the 2019 international symposium on educational technology (ISET).

    Google Scholar 

  • Rahman, M. H., & Islam, M. R. (2017). Predict student's academic performance and evaluate the impact of different attributes on the performance using data mining techniques. In Paper presented at the 2017 2nd international conference on electrical & electronic engineering (ICEEE).

    Google Scholar 

  • Rajak, A., Shrivastava, A. K., & Vidushi. (2020). Applying and comparing machine learning classification algorithms for predicting the results of students. Journal of Discrete Mathematical Sciences and Cryptography, 23(2), 419–427.

    Article  MATH  Google Scholar 

  • Ramaswami, G., Susnjak, T., Mathrani, A., Lim, J., & Garcia, P. (2019). Using educational data mining techniques to increase the prediction accuracy of student academic performance. Information and Learning Sciences.

    Book  Google Scholar 

  • Ramaswami, M., & Bhaskaran, R. (2010). A CHAID based performance prediction model in educational data mining. arXiv preprint arXiv:1002.1144.

  • Ramaswami, M., & Rathinasabapathy, R. (2012). Student performance prediction. International Journal of Computational Intelligence and Informatics, 1(4), 231–235.

    Google Scholar 

  • Ramesh, V., Parkavi, P., & Ramar, K. (2013). Predicting student performance: A statistical and data mining approach. International Journal of Computer Applications, 63(8).

  • Rifat, M. R. I., Al Imran, A., & Badrudduza, A. (2019). Educational performance analytics of undergraduate business students. International Journal of Modern Education and Computer Science, 11(7), 44.

    Article  Google Scholar 

  • Rincón-Flores, E. G., López-Camacho, E., Mena, J., & López, O. O. (2020). Predicting academic performance with artificial intelligence (AI), a new tool for teachers and students. In Paper presented at the 2020 IEEE global engineering education conference (EDUCON).

    Google Scholar 

  • Rojanavasu, P. (2019). Educational data analytics using association rule mining and classification. In Paper presented at the 2019 joint international conference on digital arts, media and technology with ECTI northern section conference on electrical, electronics, computer and telecommunications engineering (ECTI DAMT-NCON).

    Google Scholar 

  • Romero, C., López, M.-I., Luna, J.-M., & Ventura, S. (2013). Predicting students' final performance from participation in on-line discussion forums. Computers & Education, 68, 458–472.

    Article  Google Scholar 

  • Rovira, S., Puertas, E., & Igual, L. (2017). Data-driven system to predict academic grades and dropout. PLoS One, 12(2), e0171207.

    Article  Google Scholar 

  • Ruby, J., & David, K. (2015). Analysis of influencing factors in predicting students performance using MLP-A comparative study. International Journal of Innovative Research in Computer and Communication Engineering, 3(2), 1085–1092.

    Google Scholar 

  • Saa, A. A. (2016). Educational data mining & students’ performance prediction. International Journal of Advanced Computer Science and Applications, 7(5), 212–220.

    Google Scholar 

  • Sahebi, S., & Brusilovsky, P. (2018). Student performance prediction by discovering inter-activity relations. International Educational Data Mining Society.

    Google Scholar 

  • Saheed, Y., Oladele, T., Akanni, A., & Ibrahim, W. (2018). Student performance prediction based on data mining classification techniques. Nigerian Journal of Technology, 37(4), 1087–1091.

    Article  Google Scholar 

  • Saifudin, A., & Desyani, T. (2020). Forward selection technique to choose the best features in prediction of student academic performance based on naïve bayes. In Paper presented at the journal of physics: Conference series.

    Google Scholar 

  • Salal, Y., Abdullaev, S., & Kumar, M. (2019). Educational data mining: Student performance prediction in academic. The International Journal of Engineering and Advanced Technology, 8(4C), 54–59.

    Google Scholar 

  • Sandoval, A., Gonzalez, C., Alarcon, R., Pichara, K., & Montenegro, M. (2018). Centralized student performance prediction in large courses based on low-cost variables in an institutional context. The Internet and Higher Education, 37, 76–89.

    Article  Google Scholar 

  • Santoso, H. B. (2020). Fuzzy decision tree to predict student success in their studies. International Journal of Quantitative Research and Modeling, 1(3), 135–144.

    Article  Google Scholar 

  • Sari, E. Y., & Sunyoto, A. (2019). Optimization of weight backpropagation with particle swarm optimization for student dropout prediction. In Paper presented at the 2019 4th international conference on information technology, information systems and electrical engineering (ICITISEE).

    Google Scholar 

  • Sawant, T. U., Pol, U. R., & Patankar, P. S. (2019). Educational data mining prediction model using decision tree algorithm. International Journal of Emerging Technologies and Innovative Research, 2349(5162), 306–313. www.jetir.org

    Google Scholar 

  • Sen, P. C., Hajra, M., & Ghosh, M. (2020). Supervised classification algorithms in machine learning: A survey and review. In Emerging technology in modelling and graphics (pp. 99–111). Springer.

    Chapter  Google Scholar 

  • Senthil, S., & Lin, W. M. (2017). Applying classification techniques to predict students' academic results. In Paper presented at the 2017 IEEE international conference on current trends in advanced computing (ICCTAC).

    Google Scholar 

  • Shahiri, A. M., & Husain, W. (2015). A review on predicting student's performance using data mining techniques. Procedia Computer Science, 72, 414–422.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Singhani, S., Desai, S., Bailurkar, R., & Mantri, R. (2019). Student academic performance prediction using machine learning.

    Google Scholar 

  • Sivakumar, S., & Selvaraj, R. (2018). Predictive modeling of students performance through the enhanced decision tree. In Advances in electronics, communication and computing (pp. 21–36). Springer.

    Chapter  Google Scholar 

  • Sivakumar, S., Venkataraman, S., & Selvaraj, R. (2016). Predictive modeling of student dropout indicators in educational data mining using improved decision tree. Indian Journal of Science and Technology, 9(4), 1–5.

    Article  Google Scholar 

  • Sokkhey, P., & Okazaki, T. (2019). Comparative study of prediction models on high school student performance in mathematics. In Paper presented at the 2019 34th international technical conference on circuits/systems, computers and communications (ITC-CSCC).

    Google Scholar 

  • Sokkhey, P., & Okazaki, T. (2020a). Developing web-based support systems for predicting poor-performing students using educational data mining techniques. studies, 11(7).

  • Sokkhey, P., & Okazaki, T. (2020b). Development and optimization of deep belief networks applied for academic performance prediction with larger datasets. IEIE Transactions on Smart Processing & Computing, 9(4), 298–311.

    Article  Google Scholar 

  • Sokkhey, P., & Okazaki, T. (2020c). Hybrid machine learning algorithms for predicting academic performance. International Journal of Advanced Computer Science and Applications, 11, 32–41.

    Article  Google Scholar 

  • Solís, M., Moreira, T., Gonzalez, R., Fernandez, T., & Hernandez, M. (2018). Perspectives to predict dropout in university students with machine learning. In Paper presented at the 2018 IEEE international work conference on bioinspired intelligence (IWOBI).

    Google Scholar 

  • Soni, A., Kumar, V., Kaur, R., & Hemavath, D. (2018). Predicting student performance using data mining techniques. International Journal of Pure and Applied Mathematics, 119(12), 221–227.

    Google Scholar 

  • Sood, S., & Saini, M. (2020). Hybridization of cluster-based LDA and ANN for student performance prediction and comments evaluation. Education and Information Technologies, 1–16.

  • Stančin, I., & Jović, A. (2019). An overview and comparison of free Python libraries for data mining and big data analysis. In Paper presented at the 2019 42nd international convention on information and communication technology, electronics and microelectronics (MIPRO).

    Google Scholar 

  • Su, Y., Liu, Q., Liu, Q., Huang, Z., Yin, Y., Chen, E., & Hu, G. (2018). Exercise-enhanced sequential modeling for student performance prediction. In Paper presented at the proceedings of the AAAI conference on artificial intelligence.

    Google Scholar 

  • Sudha, M., & Kumaravel, A. (2017). Students’performance prediction based on rough sets. Indian Journal of Computer Science and Engineering, 8, 584–589.

    Google Scholar 

  • Sukhbaatar, O., Usagawa, T., & Choimaa, L. (2019). An artificial neural network based early prediction of failure-prone students in blended learning course. International Journal of Emerging Technologies in Learning (iJET), 14(19), 77–92.

    Article  Google Scholar 

  • Sultana, S., Khan, S., & Abbas, M. A. (2017). Predicting performance of electrical engineering students using cognitive and non-cognitive features for identification of potential dropouts. International Journal of Electrical Engineering Education, 54(2), 105–118.

    Article  Google Scholar 

  • Sundar, P. P. (2013). A comparative study for predicting students academic performance using Bayesian network classifiers. IOSR Journal of Engineering (IOSRJEN), e-ISSN, 2250-3021.

  • Sweeney, M., Lester, J., & Rangwala, H. (2015). Next-term student grade prediction. In Paper presented at the 2015 IEEE international conference on big data (big data).

    Google Scholar 

  • Tekin, A. (2014). Early prediction of students’ grade point averages at graduation: A data mining approach. Eurasian Journal of Educational Research, 54, 207–226.

    Article  Google Scholar 

  • Thakar, P., & Mehta, A. (2017). A unified model of clustering and classification to improve students’ employability prediction. International Journal of Intelligent Systems and Applications, 9(9), 10.

    Article  Google Scholar 

  • Tomasevic, N., Gvozdenovic, N., & Vranes, S. (2020). An overview and comparison of supervised data mining techniques for student exam performance prediction. Computers & Education, 143, 103676.

    Article  Google Scholar 

  • Tripathi, A., Yadav, S., & Rajan, R. (2019). Naive Bayes classification model for the student performance prediction. In Paper presented at the 2019 2nd international conference on intelligent computing, instrumentation and control technologies (ICICICT).

    Google Scholar 

  • Turabieh, H. (2019). Hybrid machine learning classifiers to predict student performance. In Paper presented at the 2019 2nd international conference on new trends in computing sciences (ICTCS).

    Google Scholar 

  • Ulloa-Cazarez, R. L., López-Martín, C., Abran, A., & Yáñez-Márquez, C. (2018). Prediction of online students performance by means of genetic programming. Applied Artificial Intelligence, 32(9–10), 858–881.

    Article  Google Scholar 

  • Umar, M. A. (2019). Student academic performance prediction using artificial neural networks: A case study. International Journal of Computer Applications, 975, 8887.

    Google Scholar 

  • Upadhyay, H., Juneja, S., Juneja, A., Dhiman, G., & Kautish, S. (2021). Evaluation of ergonomics-related disorders in online education using fuzzy AHP. Computational Intelligence and Neuroscience, 2021.

  • Upadhyay, J., & Gautam, P. (2016). Effect of numerous data sets on performance prediction. International Journal of Computer Applications, 147(5).

  • Usman, M. M., Owolabi, O., & Ajibola, A. A. (2020). Feature selection: It importance in performance prediction.

    Google Scholar 

  • Vijayalakshmi, V., & Venkatachalapathy, K. (2019). Comparison of predicting student’s performance using machine learning algorithms. International Journal of Intelligent Systems and Applications, 11(12), 34.

    Article  Google Scholar 

  • Vital, T. P., Sangeeta, K., & Kumar, K. K. (2021). Student classification based on cognitive abilities and predicting learning performances using machine learning models. International Journal of Computing and Digital Systems, 10(1), 63–75.

    Article  Google Scholar 

  • Vivek Raj, S., & Manivannan, S. (2020). Predicting student failure in university examination using machine learning algorithms. forest, 84(66.14), 0.24.

    Google Scholar 

  • Vora, D. R., & Rajamani, K. (2019). A hybrid classification model for prediction of academic performance of students: A big data application. Evolutionary Intelligence, 1–14.

  • Waheed, H., Hassan, S.-U., Aljohani, N. R., Hardman, J., Alelyani, S., & Nawaz, R. (2020). Predicting academic performance of students from VLE big data using deep learning models. Computers in Human Behavior, 104, 106189.

    Article  Google Scholar 

  • Wakelam, E., Jefferies, A., Davey, N., & Sun, Y. (2020). The potential for student performance prediction in small cohorts with minimal available attributes. British Journal of Educational Technology, 51(2), 347–370.

    Article  Google Scholar 

  • Walia, N., Kumar, M., Nayar, N., & Mehta, G. (2020). Student’s academic performance prediction in academic using data mining techniques. Available at SSRN, 3565874.

  • Wang, W., Yu, H., & Miao, C. (2017). Deep model for dropout prediction in MOOCs. In Paper presented at the proceedings of the 2nd international conference on crowd science and engineering.

    Google Scholar 

  • Wati, M., Indrawan, W., Widians, J. A., & Puspitasari, N. (2017). Data mining for predicting students' learning result. In Paper presented at the 2017 4th international conference on computer applications and information processing technology (CAIPT).

    Google Scholar 

  • Whitehill, J., Mohan, K., Seaton, D., Rosen, Y., & Tingley, D. (2017). Delving deeper into MOOC student dropout prediction. arXiv preprint arXiv, 1702.06404.

  • Wiyono, S., Wibowo, D. S., Hidayatullah, M. F., & Dairoh, D. (2020). Comparative study of KNN, SVM and decision tree algorithm for student’s performance prediction. IJCSAM (International Journal of Computing Science and Applied Mathematics), 6(2), 50–53.

    Google Scholar 

  • Wong, J. C. F., & Yip, T. C. Y. (2020). Measuring students' academic performance through educational data mining. International Journal of Information and Education Technology, 10(11).

  • Wong, M. L., & Senthil, S. (2018). Applying attribute selection algorithms in academic performance prediction. In Paper presented at the international conference on intelligent data communication technologies and internet of things.

    Google Scholar 

  • Wook, M., Yahaya, Y. H., Wahab, N., Isa, M. R. M., Awang, N. F., & Seong, H. Y. (2009). Predicting NDUM student's academic performance using data mining techniques. In Paper presented at the 2009 second international conference on computer and electrical engineering.

    Google Scholar 

  • Wu, B., Qu, S., Ni, Y., Zhou, Y., Wang, P., & Li, Q. (2019a). Predicting student performance using weblogs. In Paper presented at the 2019 14th international conference on Computer Science & Education (ICCSE).

    Google Scholar 

  • Wu, N., Zhang, L., Gao, Y., Zhang, M., Sun, X., & Feng, J. (2019b). CLMS-Net: Dropout prediction in MOOCs with deep learning. In Paper presented at the proceedings of the ACM Turing Celebration Conference-China.

    Google Scholar 

  • Xu, X., Wang, J., Peng, H., & Wu, R. (2019). Prediction of academic performance associated with internet usage behaviors using machine learning algorithms. Computers in Human Behavior, 98, 166–173.

    Article  Google Scholar 

  • Yaacob, W. F. W., Nasir, S. A. M., Yaacob, W. F. W., & Sobri, N. M. (2019). Supervised data mining approach for predicting student performance. Indonesian Journal of Electrical Engineering and Computer Science, 16(3), 1584–1592.

    Article  Google Scholar 

  • Yadav, S. K., & Pal, S. (2012). Data mining: A prediction for performance improvement of engineering students using classification. arXiv preprint arXiv, 1203.3832.

  • Yağci, A., & Çevik, M. (2019). Prediction of academic achievements of vocational and technical high school (VTS) students in science courses through artificial neural networks (comparison of Turkey and Malaysia). Education and Information Technologies, 24(5), 2741–2761.

    Article  Google Scholar 

  • Yahaya, C. A. C., Yaakub, C. Y., Abidin, A. F. Z., Ab Razak, M. F., Hasbullah, N. F., & Zolkipli, M. F. (2020). The prediction of undergraduate student performance in chemistry course using multilayer perceptron. In Paper presented at the IOP conference series: Materials science and engineering.

    Google Scholar 

  • Yathongchai, W., Yathongchai, C., Kerdprasop, K., & Kerdprasop, N. (2003). Factor analysis with data mining technique in higher educational student drop out. Latest Advances in Educational Technologies.

  • Yu, R., Li, Q., Fischer, C., Doroudi, S., & Xu, D. (2020). Towards accurate and fair prediction of college success: Evaluating different sources of student data. In Paper presented at the proceedings of the 13th international conference on educational data mining (EDM 2020).

    Google Scholar 

  • Zaffar, M., Hashmani, M. A., Savita, K., Rizvi, S. S. H., & Rehman, M. (2020). Role of FCBF feature selection in educational data mining. Mehran University Research Journal of Engineering and Technology, 39(4), 772–778.

    Article  Google Scholar 

  • Zhang, Y., & Wu, B. (2019). Research and application of grade prediction model based on decision tree algorithm. In Paper presented at the proceedings of the ACM Turing Celebration conference-China.

    Google Scholar 

  • Zhao, L., Chen, K., Song, J., Zhu, X., Sun, J., Caulfield, B., & Mac Namee, B. (2020a). Academic performance prediction based on multisource, multifeature behavioral data. IEEE Access, 9, 5453–5465.

    Article  Google Scholar 

  • Zhao, Y., Ren, W., & Li, Z. (2020b). Prediction of english scores of college students based on multi-source data fusion and social behavior analysis prediction of english scores of college students based on multi-source data fusion and social behavior analysis.

    Google Scholar 

  • Zohair, L. M. A. (2019). Prediction of student’s performance by modelling small dataset size. International Journal of Educational Technology in Higher Education, 16(1), 1–18.

    Google Scholar 

  • Zong, J., Cui, C., Ma, Y., Yao, L., Chen, M., & Yin, Y. (2020). Behavior-driven student performance prediction with tri-branch convolutional neural network. In Paper presented at the proceedings of the 29th ACM international conference on Information & Knowledge Management.

    Google Scholar 

  • Zulfiker, M. S., Kabir, N., Biswas, A., Chakraborty, P., & Rahman, M. M. (2020). Predicting students’ performance of the private universities of Bangladesh using machine learning approaches. International Journal of Advanced Computer Science and Applications, 11(3), 672–679.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad, Pakistan

    Saba Batool & Muhammad Wasif Nisar

  2. Department of Computer Science and Engineering, Kongju National University, Cheonan, 31080, South Korea

    Junaid Rashid

  3. Department of Software, Department of Computer Science and Engineering, Kongju National University, Cheonan, 31080, South Korea

    Jungeun Kim

  4. Department of Industrial Engineering, Seoul National University of Science and Technology, Seoul, South Korea

    Hyuk-Yoon Kwon

  5. Data Science and Cyber Analytics Research Group, Edinburgh Napier University, Edinburgh, EH11 4DY, UK

    Amir Hussain

Authors
  1. Saba Batool
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junaid Rashid
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammad Wasif Nisar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jungeun Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hyuk-Yoon Kwon
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Amir Hussain
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Junaid Rashid or Jungeun Kim.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Batool, S., Rashid, J., Nisar, M.W. et al. Educational data mining to predict students' academic performance: A survey study. Educ Inf Technol 28, 905–971 (2023). https://doi.org/10.1007/s10639-022-11152-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-022-11152-y

Keywords

Search

Navigation