EVALUATION THE USE OF ANFIS IN PREDICTING STUDENT’S PERFORMANCE | ||
| Kerbala Journal for Engineering Sciences | ||
| Volume 3, Issue 3, September 2023, Pages 101-113 PDF (1.17 M) | ||
| Document Type: Research Article | ||
| Authors | ||
| Zainab R khudhair* 1; Mithaq Raheema2; Jabbar Salman3 | ||
| 1Deprtment OF Electrical and Electronic Engineering, University of Kerbala | ||
| 2College of Engineering/ University of Kerbala, Kerbala, Iraq. | ||
| 3PROSTBETIC&ORTHETIC Eng. College of Engineering Karbala University | ||
| Abstract | ||
| This article suggests the implementation of an online distance education system as a way to enhance the academic performance of students. It emphasizes the importance of monitoring and predicting the Learning Academic Performance (LAP) to enable appropriate adjustments and ensure continuous academic progress. In this education mode, predicting LAP accurately is a challenging task. Adaptive Neuro-Fuzzy Inference System (ANFIS), executed by ‘MATLAB R2020a’, creates systems that can successfully achieve the aim. The suggested ANFIS models were used to forecast students' Cumulative Grade Point Averages (CGPAs). The interactions between input variables and their associated impacts on the output values were further investigated using the models. The results showed high accuracy levels, varying scores from 83.8% to 90%. Additionally, this study found a significant correlation between CGPA and the examination scores, indicating a direct proportional relationship. These findings emphasize the crucial role of examination scores in evaluating academic performance and the importance for students to prioritize and focus on their exam performance. | ||
| Keywords | ||
| : Adaptive Neuro-Fuzzy Inference System (ANFIS); Performance Prediction; and Cumulative Grade Point Average | ||
| References | ||
|
[1] S. S. Abu-Naser, I. S. Zaqout, M. Abu Ghosh, R. R. Atallah, and E. Alajrami, "Predicting student performance using artificial neural network: In the engineering and information technology faculty," 2015.
[2] Q. H. Do and J.-F. Chen, "A neuro-fuzzy approach in the classification of students' academic performance," vol. 2013, pp. 6-6, 2013.
[3] A. M. Shahiri and W. Husain, "A review on predicting student's performance using data mining techniques," vol. 72, pp. 414-422, 2015.
[4] I. E. Livieris, K. Drakopoulou, and P. Pintelas, "Predicting students' performance using artificial neural networks," pp. 321-328, 2012.
[5] J. Undavia, P. Dolia, and A. Patel, "Comparison of Decision Tree Classification Algorithm to Predict Students Post Graduate Degree in Weka Environment," vol. 1, no. 2, pp. 17-21, 2014.
[6] K. Colchester, H. Hagras, D. Alghazzawi, and G. Aldabbagh, "A survey of artificial intelligence techniques employed for adaptive educational systems within e-learning platforms," vol. 7, no. 1, pp. 47-64, 2017.
[7] N. M. Rusli, Z. Ibrahim, and R. M. Janor, "Predicting students’ academic achievement: Comparison between logistic regression, artificial neural network, and Neuro-fuzzy," in 2008 international symposium on information technology, 2008, vol. 1, pp. 1-6: IEEE.
[8] O. Taylan, B. J. C. Karagözoğlu, and I. Engineering, "An adaptive neuro-fuzzy model for predicting student’s academic performance," vol. 57, no. 3, pp. 732-741, 2009.
[9] I. Hidayah, A. E. Permanasari, and N. Ratwastuti, "Student classification for academic performance prediction using neuro-fuzzy in a conventional classroom," in 2013 International Conference on Information Technology and Electrical Engineering (ICITEE), 2013, pp. 221-225: IEEE.
[10] N. Arora and J. R. Saini, "A fuzzy probabilistic neural network for student’s academic performance prediction," vol. 2, no. 9, pp. 4425-4432, 2013.
[11] P. Ajith, B. Tejaswi, and M. Sai, "Rule mining framework for students performance evaluation," vol. 2, no. 6, pp. 201-6, 2013.
[12] C. Romero and S. Ventura, "Data mining in education," vol. 3, no. 1, pp. 12-27, 2013.
[13] S. Dawson, D. Gašević, G. Siemens, and S. Joksimovic, "Current state and future trends: A citation network analysis of the learning analytics field," in Proceedings of the Fourth International Conference on learning analytics and knowledge, 2014, pp. 231-240.
[14] O. Yildiz, A. Bal, and S. Gulsecen, "Improved fuzzy modelling to predict the academic performance of distance education students," vol. 14, no. 5, pp. 144-165, 2013.
[15] H. Hamsa, S. Indiradevi, and J. Kizhakkethottam, "Student academic performance prediction model using decision tree and fuzzy genetic algorithm," vol. 25, pp. 326-332, 2016.
[16] G. Kostopoulos, A.-D. Lipitakis, S. Kotsiantis, and G. Gravvanis, "Predicting student performance in distance higher education using active learning," in Engineering Applications of Neural Networks: 18th International Conference, EANN 2017, Athens, Greece, August 25–27, 2017, Proceedings, 2017, pp. 75-86: Springer.
[17] Q. Zhao, J.-L. Wang, T.-L. Pao, and L.-Y. Wang, "Modified fuzzy rule-based classification system for early warning of student learning," vol. 48, no. 3, pp. 385-406, 2020.
[18] A. Altaher and O. BaRukab, "Prediction of student's academic performance based on adaptive neuro-fuzzy inference," vol. 17, no. 1, p. 165, 2017.
[19] A. Ikuomola and O. Arowolo, "Evaluation Of Student Academic Performance Using Adaptive Neuro-Fuzzy Approach," vol. 11, no. 1, pp. 11-23, 2012.
[20] K. Fahd, S. J. Miah, and K. Ahmed, "Predicting student performance in a blended learning environment using learning management system interaction data," no. Ahead-of-print, 2021.
[21] I. T. R. Yanto, E. Sutoyo, A. Rahman, R. Hidayat, A. A. Ramli, and M. F. M. Fudzee, "Classification of student academic performance using fuzzy soft set," in 2020 International Conference on Smart Technology and Applications (ICoSTA), 2020, pp. 1-6: IEEE.
[22] M. Adil, F. Tahir, and S. Maqsood, "Predictive analysis for student retention using neuro-fuzzy algorithm," in 2018 10th Computer Science and Electronic Engineering (CEEC), 2018, pp. 41-45: IEEE. | ||
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