The International Arab Journal of Information Technology (IAJIT)


The Critical Feature Selection Approach using

In this work, machine learning techniques are deemed to predict student academic performances in their historical performance of Final Grades (FGs). Acceptance of Technology enabled the teaching-learning processes, as it has become a vital 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 behavior using their data that needs to be adequately processed to transform it into helpful information. This paper proposes a prediction 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 academic scores. 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 selection approach is evaluated. Second, a set of renowned classifiers are trained and tested. Third, ensemble meta-based models are improvised to boost the accuracy of the classifier. Subsequently, the present study is associated with the solutions that help the students evaluate and improve their academic performance with a glimpse of their historical grades. Ultimately, the results were produced and evaluated. The results showed the effectiveness of our proposed framework in predicting students' academic performance.

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[11] Wan H., Ding J., Gao X., and Pritchard D., “Dropout Prediction in MOOCs using Learners’ Study Habits Features,” in Proceeding of the 10th International Conference on Educational Data Mining, pp. 408-409. 2017. Muhammad Qasim Memon is currently working as an Assistant Professor in the Department of Computer Science, University of Sufism and Modern Sciences, Bhitshah. Dr. Memon is also a Post- doctorate fellow at the Advanced Innovation Center for Future Education (AICFE), Faculty of Education, Beijing Normal University, China. He received his Ph.D. degree from the School of Software Engineering at Beijing University of Technology, China, in 2018. He received his Bachelor of Engineering and Master of Engineering from Mehran University of Engineering & Technology Jamshoro (MUET) in 2009 and 2014. Dr. Qasim has published several papers in international conferences and research journals indexed in SCI., EI., and Scopus. Dr.Memon's research interests include Educational Data Mining, Text Analytics, Information Extraction, and Technology Education. Yu Lu received his Ph.D. degree from the National University of Singapore. He is currently an Associate Professor with the Faculty of Education, Beijing Normal University, where he also serves as the director of the artificial intelligence (AI) lab and leads the research team for AI in education. Dr. Lu's research interests include educational data mining, learning analytics, pervasive computing and educational robotics. Shengquan Yu is the executive director of AICFE at Beijing Normal University, and director of the joint laboratory for Mobile Learning, Ministry of Education- China Mobile Communication corporation. He also serves as the deputy dean of the faculty of education, Beijing Normal University. Professor Yu’s research fields include mobile and ubiquitous learning, ICT and curriculum integration, network learning technology, and education information policyK Aasma Memon completed her Ph.D. from the School of Economics and Management at Beijing University of Technology, China. She received her Bachelor in Arts and Masters in Public Administration from the University of Sindh, Jamshoro, Pakistan, in 2008 and 2012. Her research interests include firm performance and corporate sustainability, human resource management, and data mining. Abdul Rehman Memon is currently a professor in the department of Chemical Engineering, Mehran University of Engineering & Technology, Jamshoro. Dr. Memon received his Ph.D. in Environmental Engg. from University of Nottingham in 2011. He completed his master and bachelor from MUET Jamshoro in 2004 and 1991. Dr. Memon's research interests include algal biofuels, Waste water Bioremediations, Bioenergy engineering, and pollution control engineering.