• Anwar Adnan Alnawas Nasiriyah Technical Institute, Southern Technical University, Thi-qar, Iraq
  • Mohammed Al-Jawad College of Computer Science and Information Technology, University of Kerbala, Karbala, 56001, Iraq
  • Hasanein Alharbi IT College, University of Babylon, Babylon, 51002, Iraq




Data Mining, E-Learning, Prediction, User behaviour, CRISP-DM


E-Learning has become an essential teaching approach during the COVID-19 pandemic. All over the world, various internet-based learning management systems (Google classroom, Moodle, etc.) were adopted to convey knowledge and enhance learning outcomes. However, measuring learning outcomes and knowledge acquisition in E-Learning environment is a controversial issue. To this end, this paper aims to predict learning outcomes using data mining techniques.  Student data are collected and analyzed to construct the prediction model. The collected data covered students from various undergraduate studies. Cross-Industry Standard Process for Data Mining is used as a research model. The obtained result shows the significant of some attributes in predicting learning outcomes. Four correlation-based attributes selection schemas are applied. The selected attributes are examined using four data mining algorithms: random forest, k-nearest neighbors, Decision Tree, and neural network. The overall performance of the constructed mining models is evaluated using various performance measures: Accuracy, Precision, Recall and F1-score are calculated. Overall, an 86% accuracy is secured.


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Submission Dates

Received 16/5/2022 

Accepted 23/5/2022 

Published 1/9/2022

How to Cite

Adnan Alnawas, A., Al-Jawad, M., & Alharbi, H. (2022). A PREDICITON MODEL BASED ON STUDENTS’S BEHAVIOR IN E-LEARNING ENVIRONMENTS USING DATA MINING TECHNIQUES. Journal of Engineering and Sustainable Development, 26(5), 115–126. https://doi.org/10.31272/jeasd.26.5.11