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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

Prediction of Students’ Achievements in E-Learning Courses Based on Adaptive Neuro-Fuzzy Inference System

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)

Received: August 21, 2021; Revised: November 15, 2021; Accepted: March 7, 2022

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.

Mithq Nama Raheema received a Ph.D. degree in Electrical Engineering from the University of Technology, Iraq. His research interests are AI and smart prostheses.

E-mail: methaq.n.rhiama@uokerbala.edu.iq

Ahmed M. Al-Khazzar received a Ph.D. degree in Computer Security from the University of Portsmouth, UK. His research interests are biometrics, AI and smart prostheses.

E-mail: ahmed.m.ahmed@uokerbala.edu.iq

Jabbar Salman Hussain received a Ph.D. degree in Electrical Engineering from the University of Technology, Iraq. His research interests are AI and smart prostheses.

E-mail: jabbar.salman@uokerbala.edu.iq

Article

Original Article

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.

Prediction of Students’ Achievements in E-Learning Courses Based on Adaptive Neuro-Fuzzy Inference System

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)

Received: August 21, 2021; Revised: November 15, 2021; Accepted: March 7, 2022

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.

Abstract

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

Fig 1.

Figure 1.

ANFIS process of each layer.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 213-222https://doi.org/10.5391/IJFIS.2022.22.2.213

Fig 2.

Figure 2.

Proposed ANFIS structure.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 213-222https://doi.org/10.5391/IJFIS.2022.22.2.213

Fig 3.

Figure 3.

Error surface result.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 213-222https://doi.org/10.5391/IJFIS.2022.22.2.213

Fig 4.

Figure 4.

ANFIS reasoning process.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 213-222https://doi.org/10.5391/IJFIS.2022.22.2.213

Fig 5.

Figure 5.

Actual training output.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 213-222https://doi.org/10.5391/IJFIS.2022.22.2.213

Fig 6.

Figure 6.

Error between training outputs.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 213-222https://doi.org/10.5391/IJFIS.2022.22.2.213

Fig 7.

Figure 7.

Rounded training outputs.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 213-222https://doi.org/10.5391/IJFIS.2022.22.2.213

Fig 8.

Figure 8.

Training error for rounded output.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 213-222https://doi.org/10.5391/IJFIS.2022.22.2.213

Fig 9.

Figure 9.

Rounded test outputs.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 213-222https://doi.org/10.5391/IJFIS.2022.22.2.213

Fig 10.

Figure 10.

Confusion matrix result.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 213-222https://doi.org/10.5391/IJFIS.2022.22.2.213

Table 1 . Structure of dataset elements.

VariableDescriptionDomain
TTime the student spent answering an individual question0–60 seconds
DAverage difficulty of the question50–100 degrees
AAchievement of the student1–8 level

Table 2 . Structure of class output.

OutputClass regionClass description
10–30Fail
231–40Very weak
341–50Weak
451–60Acceptable
561–70Average
671–80Good
781–90Very good
891–100Excellent

Table 3 . RMSE result comparison.

StudyRMSE
Rusli et al. [25]0.3100
Abidin and Dom [31]Training0.0967
Testing01384
This workTraining0.0345
Testing0.0965

Table 4 . Accuracy result comparison with different methods.

StudyClassifierAccuracy (%)
Fahd et al. [40]Random Forest with booster ensemble tuning85.7
Hussain and Khan [41]Genetic algorithm with decision-tree96.64
Alturki and Alturki [42]Random forest92.60
Yousafzai et al. [43]Attention-based BiLSTM90.16
Riyadi Yanto et al. [44]Fuzzy soft set89.00
This workANFIS99.10

Table 5 . Accuracy results comparison with neuro-fuzzy works.

StudyAccuracy (%)
Do and Chen [1]90.03
This work99.10

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