The Latent of Student Learning Analytic with K-mean Clustering for Student Behaviour Classification

Andi Besse Firdausiah Mansur, Norazah Yusof

= http://dx.doi.org/10.20473/jisebi.4.2.156-161
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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.


Keywords


Student learning analytic;k-mean clustering;commitment;density

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