International Journal of Fuzzy Logic and Intelligent Systems 2022; 22(2): 213-222
Published online June 25, 2022
https://doi.org/10.5391/IJFIS.2022.22.2.213
© The Korean Institute of Intelligent Systems
Mithaq Nama Raheema, Ahmed M. Al-Khazzar, and Jabbar Salman Hussain
Department of Prosthetics & Orthotics Engineering, College of Engineering, University of Kerbala, Kerbala, Iraq
Correspondence to :
Ahmed M. Al-Khazzar (ahmed.m.ahmed@uokerbala.edu.iq)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
A prediction of students’ achievements is important for educational organizations. It helps to revise plans and improve students’ achievements throughout their education period. A neurofuzzy system for predicting student achievement is presented in this study. The motivation behind it is to propose a promising achievement predictor for real-time systems associated with e-learning courses. The proposed neuro-fuzzy predictor uses the time that a student needs to answer a question and the difficulty level of that question as input variables. The predictor output was the level of the student’s achievement. Real data were used from e-learning courses at the University of Kerbala, Iraq. The proposed system achieved an excellent accuracy of up to 99% and an root mean square error (RMSE) value of 0.0965 for recognizing unknown test samples. The proposed prediction system based on adaptive neuro-fuzzy inference system(ANFIS) achieved better results than previous techniques. It is hoped that the results of this work will improve college admission processes and support future planning in educational organizations.
Keywords: Machine learning, ANFIS, Adaptive neuro-fuzzy, Student achievement prediction, E-learning
No potential conflict of interest relevant to this article was reported.
E-mail: methaq.n.rhiama@uokerbala.edu.iq
E-mail: ahmed.m.ahmed@uokerbala.edu.iq
E-mail: jabbar.salman@uokerbala.edu.iq
International Journal of Fuzzy Logic and Intelligent Systems 2022; 22(2): 213-222
Published online June 25, 2022 https://doi.org/10.5391/IJFIS.2022.22.2.213
Copyright © The Korean Institute of Intelligent Systems.
Mithaq Nama Raheema, Ahmed M. Al-Khazzar, and Jabbar Salman Hussain
Department of Prosthetics & Orthotics Engineering, College of Engineering, University of Kerbala, Kerbala, Iraq
Correspondence to:Ahmed M. Al-Khazzar (ahmed.m.ahmed@uokerbala.edu.iq)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
A prediction of students’ achievements is important for educational organizations. It helps to revise plans and improve students’ achievements throughout their education period. A neurofuzzy system for predicting student achievement is presented in this study. The motivation behind it is to propose a promising achievement predictor for real-time systems associated with e-learning courses. The proposed neuro-fuzzy predictor uses the time that a student needs to answer a question and the difficulty level of that question as input variables. The predictor output was the level of the student’s achievement. Real data were used from e-learning courses at the University of Kerbala, Iraq. The proposed system achieved an excellent accuracy of up to 99% and an root mean square error (RMSE) value of 0.0965 for recognizing unknown test samples. The proposed prediction system based on adaptive neuro-fuzzy inference system(ANFIS) achieved better results than previous techniques. It is hoped that the results of this work will improve college admission processes and support future planning in educational organizations.
Keywords: Machine learning, ANFIS, Adaptive neuro-fuzzy, Student achievement prediction, E-learning
ANFIS process of each layer.
Proposed ANFIS structure.
Error surface result.
ANFIS reasoning process.
Actual training output.
Error between training outputs.
Rounded training outputs.
Training error for rounded output.
Rounded test outputs.
Confusion matrix result.
Table 1 . Structure of dataset elements.
Variable | Description | Domain |
---|---|---|
T | Time the student spent answering an individual question | 0–60 seconds |
D | Average difficulty of the question | 50–100 degrees |
A | Achievement of the student | 1–8 level |
Table 2 . Structure of class output.
Output | Class region | Class description |
---|---|---|
1 | 0–30 | Fail |
2 | 31–40 | Very weak |
3 | 41–50 | Weak |
4 | 51–60 | Acceptable |
5 | 61–70 | Average |
6 | 71–80 | Good |
7 | 81–90 | Very good |
8 | 91–100 | Excellent |
Table 4 . Accuracy result comparison with different methods.
Study | Classifier | Accuracy (%) |
---|---|---|
Fahd et al. [40] | Random Forest with booster ensemble tuning | 85.7 |
Hussain and Khan [41] | Genetic algorithm with decision-tree | 96.64 |
Alturki and Alturki [42] | Random forest | 92.60 |
Yousafzai et al. [43] | Attention-based BiLSTM | 90.16 |
Riyadi Yanto et al. [44] | Fuzzy soft set | 89.00 |
This work | ANFIS | 99.10 |
Amirthalakshmi Thirumalai Maadapoosi, Velan Balamurugan, V. Vedanarayanan, Sahaya Anselin Nisha, and R. Narmadha
International Journal of Fuzzy Logic and Intelligent Systems 2024; 24(3): 231-241 https://doi.org/10.5391/IJFIS.2024.24.3.231Nishant Chauhan and Byung-Jae Choi
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International Journal of Fuzzy Logic and Intelligent Systems 2023; 23(2): 214-228 https://doi.org/10.5391/IJFIS.2023.23.2.214ANFIS process of each layer.
|@|~(^,^)~|@|Proposed ANFIS structure.
|@|~(^,^)~|@|Error surface result.
|@|~(^,^)~|@|ANFIS reasoning process.
|@|~(^,^)~|@|Actual training output.
|@|~(^,^)~|@|Error between training outputs.
|@|~(^,^)~|@|Rounded training outputs.
|@|~(^,^)~|@|Training error for rounded output.
|@|~(^,^)~|@|Rounded test outputs.
|@|~(^,^)~|@|Confusion matrix result.