The Latent of Student Learning Analytic with K-mean Clustering for Student Behaviour Classification
| Main Authors: | Mansur, Andi Besse Firdausiah, Yusof, Norazah |
|---|---|
| Other Authors: | NA |
| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
Universitas Airlangga
, 2018
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| Subjects: | |
| Online Access: |
http://e-journal.unair.ac.id/JISEBI/article/view/9411 http://e-journal.unair.ac.id/JISEBI/article/view/9411/5670 |
Daftar Isi:
- 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.