Hilal Almarabeh

Work place: King king Saud Bin Abdulaziz University for Health Sciences College of Science and Health Professions Riyadh, Kingdom of Saudi Arabia

E-mail: almarabehh@ksau-hs.edu.sa


Research Interests: Computer systems and computational processes, Computer Vision, Data Mining, Data Structures and Algorithms, Analysis of Algorithms


Hilal Almarabeh is a lecturer in Computer Science and Health Informatics at the Department of Basic Sciences and Health Professions, King king Saud Bin Abdulaziz University for Health Sciences. He has more than ten years experience in teaching and research. He received his Master degree in Computer Science from Amman Arab University for Graduate Studies. His research interests are: e-Learning, data mining, computer vision, algorithms and wireless network.

Author Articles
Analysis of Students' Performance by Using Different Data Mining Classifiers

By Hilal Almarabeh

DOI: https://doi.org/10.5815/ijmecs.2017.08.02, Pub. Date: 8 Aug. 2017

Data mining is the analysis of a large dataset to discover patterns and use those patterns to predict the likelihood of the future events. Data mining is becoming a very important field in educational sectors and it holds great potential for the schools and universities. There are many data mining classification techniques with different levels of accuracy. The objective of this paper is to analyze and evaluate the university students' performance by applying different data mining classification techniques by using WEKA tool. The highest accuracy of classifier algorithms depends on the size and nature of the data. Five classifiers are used NaiveBayes, Bayesian Network, ID3, J48 and Neural Network Different performance measures are used to compare the results between these classifiers. The results shows that Bayesian Network classifier has the highest accuracy among the other classifiers.

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