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Computer Engineering and Applications Journal
Article . 2017 . Peer-reviewed
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License: CC BY
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Article . 2019
License: CC BY
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Article . 2019
License: CC BY
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Comparative classification of student's academic failure through Social Network Mining and Hierarchical Clustering

Authors: Andi Besse firdausiah Mansur; Norazah Yusof;

Comparative classification of student's academic failure through Social Network Mining and Hierarchical Clustering

Abstract

Student academic failure are caused by several factors such as: family relationship, study time, absence, parent education, travel time and etc. This study observe several factors which are related to student academic failure by calculating the centrality degree between students to find the correlation between failure factors for each students. Furthermore, each student will be measured by measuring the geodesic distance for each factors for hierarchical clustering. The flow betwenness measure and hierarchical clustering show the promising result, where students who has similar factors value are tends to be grouped together in the same cluster. The student with high value of flow betwenness is considered as broker of network and play vital character inside network. The result of study is believed can bring important and useful information toward the student performance analysis for future better education.

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Keywords

Social network analysis, degree centrality, Students Academic failure, hierarchical clustering

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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