The Critical Feature Selection Approach using

The international arab journal of information technology

article

Muhammad Qasim Memon¹  Shengquan Yu²  Aasma Memon³  Abdul Rehman Memon⁴ 

[1] Department of Information and Computing, University of Sufism and Modern Sciences;Advanced Innovation Center for Future Education, Beijing Normal University;School of Management and conomics, Beijing University of Technology;Department of Chemical Engineering, Mehran University of Engineering and Technology

关键词: Educational data mining;    students' prediction;    machine learning;    ensemble meta-based models;    feature selection;   

DOI  :  10.34028/iajit/19/3A/12

学科分类：计算机科学（综合）

来源: Zarqa University

【 摘 要 】

In this work, machine learning techniques are deemed to predict student academic performances in their historicalperformance of Final Grades (FGs). Acceptance of Technology enabled the teaching-learning processes, as it has become avital element to perceive the goal of academic quality. Research is improving and growing fast in Educational Data Mining(EDM) due to many students' information. Researchers urge to invent valuable patterns about students' learning behaviorusing their data that needs to be adequately processed to transform it into helpful information. This paper proposes aprediction model of students' academic performances with new data features, including student's behavioral features,Psychometric, family support, learning logs via e-learning management systems, and demographic information. In this paper,data collection and pre-processing are firstly conducted following the grouping of students with similar patterns of academicscores. Later, we selected the applicable supervised learning algorithms, and then the experimental work was implemented.The performance of the student's predictive model assessment is comprised of three steps: First, the critical Feature selectionapproach is evaluated. Second, a set of renowned classifiers are trained and tested. Third, ensemble meta-based models areimprovised to boost the accuracy of the classifier. Subsequently, the present study is associated with the solutions that help thestudents evaluate and improve their academic performance with a glimpse of their historical grades. Ultimately, the resultswere produced and evaluated. The results showed the effectiveness of our proposed framework in predicting students'academic performance.

【 授权许可】

Unknown