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, 2018)
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.