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

• Main Authors: Al-Khazzar, A.M (Author), Hussain, J.S (Author), Raheema, M.N (Author)
• Format: Article
• Language: English
• Published: Korean Institute of Intelligent Systems 2022
• Subjects: Adaptive neuro-fuzzy; Anfis; E-learning; Machine learning; Student achievement prediction
• Online Access: View Fulltext in Publisher

 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. © 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.