Tahani Soud Alharbi

Work place: Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia

E-mail: t.alharbi@qu.edu.sa


Research Interests: Applied computer science, Computer systems and computational processes, Theoretical Computer Science


Tahani Soud Alharbi: received her B.Sc. in Computer Science from the Faculty of Computer and Information Technology, Taif University, Saudi Arabia in 2012.  Currently, she is studying MSc in Computer Science, at Qassim University. In addition, from 2013 to this date, she is working as a Teaching Assistant in the Computer College, Qassim University, Saudi Arabia.

Author Articles
Data Mining Methods for Detecting the Most Significant Factors Affecting Students’ Performance

By Mohammed Abdullah Al-Hagery Maryam Abdullah Alzaid Tahani Soud Alharbi Moody Abdulrahman Alhanaya

DOI: https://doi.org/10.5815/ijitcs.2020.05.01, Pub. Date: 8 Oct. 2020

The field of using Data Mining (DM) techniques in educational environments is typically identified as Educational Data Mining (EDM). EDM is rapidly becoming an important field of research due to its ability to extract valuable knowledge from various educational datasets. During the past decade, an increasing interest has arisen within many practical studies to study and analyze educational data especially students’ performance. The performance of students plays a vital role in higher education institutions. In keeping with this, there is a clear need to investigate factors influencing students’ performance. This study was carried out to identify the factors affecting students’ academic performance. K-means and X-means clustering techniques were applied to analyze the data to find the relationship of the students' performance with these factors. The study finding includes a set of the most influencing personal and social factors on the students’ performance such as parents’ occupation, parents’ qualification, and income rate. Furthermore, it is contributing to improving the education quality, as well as, it motivates educational institutions to benefit and discover the unseen patterns of knowledge in their students' accumulated data. 

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