Prediction of Student’s Academic Performance through Data Mining Approach
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Abstract
The universities and institutes produce a large amount of student data that can be used in a disciplinary way and useful information can be extracted by using an automated approach. Educational Data Mining (EDM) is an emerging discipline used in the educational environment to deal with big student data and extract useful information. The data mining of students’ data can help the At-risk students as well as the stakeholders by the early warning. This study aims to predict the performance of the students based on student-related data to increase the overall performance. In existing studies, insufficient attributes and complexity of network models is a problem. The student’s current records and grades need to be analyzed. In this approach, the Levenberg Marquardt Algorithm (MLA) deep learning algorithm is used. The data consists of the class test, attendance, assignment and midterm scores. The neural network model consists of four input variables, three hidden and one output layer. The performance of the deep neural network is evaluated by accuracy, precision, recall and F1 score. The proposed model gained a higher accuracy of 88.6% than existing studies. The study successfully predicts the student's final grades using current academic records. This research will be beneficial to the students, educators and educational authorities as a whole.
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References
E. Aksoy, S. Narli, and M. A. Aksoy, "An Educational Data Mining Application by Using Multiple Intelligences," in Examining Multiple Intelligences and Digital Technologies for Enhanced Learning Opportunities, ed: IGI Global, 2020, pp. 93-110.
A. Tufekci and E. A. G. Yilmaz, "Educational Data Mining: A Systematic Literature Mapping Study," Engineering Education Trends in the Digital Era, pp. 70-82, 2020.
N. Tomasevic, N. Gvozdenovic, and S. Vranes, "An overview and comparison of supervised data mining techniques for student exam performance prediction," Computers & education, vol. 143, p. 103676, 2020.
I. Issah, O. Appiah, P. Appiahene, and F. Inusah, "A systematic review of the literature on machine learning application of determining the attributes influencing academic performance," Decision Analytics Journal, p. 100204, 2023.
K. S. Bhagavan, J. Thangakumar, and D. V. Subramanian, "Predictive analysis of student academic performance and employability chances using HLVQ algorithm," Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 3789-3797, 2021.
A. Abdelmagid and A. Qahmash, "Utilizing the Educational Data Mining Techniques" Orange Technology" for Detecting Patterns and Predicting Academic Performance of University Students," Inf. Sci. Lett, vol. 12, pp. 1415-1431, 2023.
M. H. B. Roslan and C. J. Chen, "Predicting students’ performance in English and Mathematics using data mining techniques," Education and Information Technologies, vol. 28, pp. 1427-1453, 2023.
N. Z. Salih and W. Khalaf, "Prediction of student’s performance through educational data mining techniques," Indonesian Journal of Electrical Engineering and Computer Science, vol. 22, pp. 1708-1715, 2021.
L. Gerritsen and R. Conijn, "Predicting student performance with Neural Networks," Tilburg University, Netherlands, 2017.
M. Al-Saleem, N. Al-Kathiry, S. Al-Osimi, and G. Badr, "Mining educational data to predict students’ academic performance," in Machine Learning and Data Mining in Pattern Recognition: 11th International Conference, MLDM 2015, Hamburg, Germany, July 20-21, 2015, Proceedings 11, 2015, pp. 403-414.
S. Batool, J. Rashid, M. W. Nisar, J. Kim, H.-Y. Kwon, and A. Hussain, "Educational data mining to predict students' academic performance: A survey study," Education and Information Technologies, vol. 28, pp. 905-971, 2023.
E. Lau, L. Sun, and Q. Yang, "Modelling, prediction and classification of student academic performance using artificial neural networks," SN Applied Sciences, vol. 1, pp. 1-10, 2019.
K. T. Chui, R. W. Liu, M. Zhao, and P. O. De Pablos, "Predicting students’ performance with school and family tutoring using generative adversarial network-based deep support vector machine," IEEE Access, vol. 8, pp. 86745-86752, 2020.
I. Burman and S. Som, "Predicting students academic performance using support vector machine," in 2019 Amity international conference on artificial intelligence (AICAI), 2019, pp. 756-759.
H. A. Mengash, "Using data mining techniques to predict student performance to support decision making in university admission systems," Ieee Access, vol. 8, pp. 55462-55470, 2020.
S. Hussain, S. Gaftandzhieva, M. Maniruzzaman, R. Doneva, and Z. F. Muhsin, "Regression analysis of student academic performance using deep learning," Education and Information Technologies, vol. 26, pp. 783-798, 2021.
H. Waheed, S.-U. Hassan, N. R. Aljohani, J. Hardman, S. Alelyani, and R. Nawaz, "Predicting academic performance of students from VLE big data using deep learning models," Computers in Human behavior, vol. 104, p. 106189, 2020.
J. de Deus Guterres, K. A. Laksitowening, and F. Sthevanie, "PREDICTING STUDENTS’PERFORMANCE IN BASIC ALGORITHMS PROGRAMMING IN AN E-LEARNING ENVIRONMENT USING DECISION TREE APPROACH," Journal of Syntax Literate, vol. 7, 2022.
M. Al Karim, M. Y. Ara, M. M. Masnad, M. Rasel, and D. Nandi, "Student performance classification and prediction in fully online environment using Decision tree," AIUB Journal of Science and Engineering (AJSE), vol. 20, pp. 70-76, 2021.
J. Figueroa-Canas and T. Sancho-Vinuesa, "Early prediction of dropout and final exam performance in an online statistics course," IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, vol. 15, pp. 86-94, 2020.
R. BUTUNER and M. H. Calp, "Estimation of the Academic Performance of Students in Distance Education Using Data Mining Methods," International Journal of Assessment Tools in Education, vol. 9, pp. 410-429, 2022.
S. Mallak, M. Kanan, N. Al-Ramahi, A. Qedan, and H. Khalilia, "Using Markov Chains and Data Mining Techniques to Predict Students’ Academic Performance," 2023.
H. Pallathadka, A. Wenda, E. Ramirez-Asis, M. Asis-Lopez, J. Flores-Albornoz, and K. Phasinam, "Classification and prediction of student performance data using various machine learning algorithms," Materials today: proceedings, vol. 80, pp. 3782-3785, 2023.
M. Yagci, "Educational data mining: prediction of students' academic performance using machine learning algorithms," Smart Learning Environments, vol. 9, p. 11, 2022.
S. T. Lim, J. Y. Yuan, K. W. Khaw, and X. Chew, "Predicting Travel Insurance Purchases in an Insurance Firm through Machine Learning Methods after COVID-19," Journal of Informatics and Web Engineering, vol. 2, pp. 43-58, 2023.
Z. Yujiao, L. W. Ang, S. Shaomin, and S. Palaniappan, "Dropout Prediction Model for College Students in MOOCs Based on Weighted Multi-feature and SVM," Journal of Informatics and Web Engineering, vol. 2, pp. 29-42, 2023.
R. Mehdi and M. Nachouki, "A neuro-fuzzy model for predicting and analyzing student graduation performance in computing programs," Education and Information Technologies, vol. 28, pp. 2455-2484, 2023.
M. Abou Naaj, R. Mehdi, E. A. Mohamed, and M. Nachouki, "Analysis of the Factors Affecting Student Performance Using a Neuro-Fuzzy Approach," Education Sciences, vol. 13, p. 313, 2023.