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Norazah Yusof
Faculty of Computing and Information Technology Rabigh, King Abdulaziz University, Jeddah, Makkah, Saudi Arabia
Computer Science & IT

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The Latent of Student Learning Analytic with K-mean Clustering for Student Behaviour Classification Andi Besse Firdausiah Mansur; Norazah Yusof
Journal of Information Systems Engineering and Business Intelligence Vol. 4 No. 2 (2018): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (411.877 KB) | DOI: 10.20473/jisebi.4.2.156-161

Abstract

Since the booming of “big data” or “data analytic” topics, it has drawn attention toward several research areas such as: student behavior classification, video surveillance, automatic navigation and etc. This paper present k-mean clustering technique to monitor and assess the student performance and behavior as well as give improvement toward e-learning system in the future. Data set of student performance along with teacher attributes are collected then analyzed, it was filtered into 6 attributes of teacher that may potentially affect the student performance. Afterwards, k-mean clustering applied into the filtered data set to generate particular cluster number. The result reveal that Teacher1 statistically hold the highest density (0.27) and teachers with good speech/lectures tend to have strong correlation with another factor such as: commitment of teacher on preparing lecture material and time management utilization. If this synergy between teacher and student running flawlessly, it will be great achievement for e-learning system to the society.
Co-Authors Andi Besse Firdausiah Mansur