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

Andi Besse Firdausiah Mansur,Norazah Yusof

doi:10.20473/jisebi.4.2.156-161

Andi Besse Firdausiah Mansur, Norazah Yusof

Open Access

https://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.

Highlights

The student engagement in online discussion or forum plays important roles toward the high quality education system in the future
This paper reveals the behaviour of student by using k-mean clustering technique as well as hierarchical clustering
We have analysed the dataset from Gunduz, G. & Fokoue, E that consist of student performance evaluation during taking the courses [24]

Summary

Introduction

The student engagement in online discussion or forum plays important roles toward the high quality education system in the future. Some researchers have design the social learning analytic to monitor student discussion while doing webinar or online lecture [1]. They develop a framework that can convert the discussion in e-learning system into a kind of information that exposes student’s behavior information. MOOCs (Massive Open Online Courses) enable students to distribute and pathway toward their awareness. The analyzed information will be used to study pattern learning behavior of student that can bring a feedback toward the courses and teacher in the forthcoming

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Conclusion