A Clustering Method using Entropy for Grouping Students

Byoung Kim; Jonathan Charles Mason; Jin Gon Shon

A Clustering Method using Entropy for Grouping Students

Byoung Kim, Jonathan Charles Mason, Jin Gon Shon

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings

Abstract

This study suggests a novel clustering method using entropy in information theory for setting cut-scores. Based on item response vectors from the examinees, we construct the Ordered Item Booklets (OIBs) based on the Rasch model which is a kind of Item Response Theory (IRT). The approach of the proposed method is to partition the scores into n-clusters and to construct probability distribution tables separately for each cluster from the item response vector. Using these probability distribution tables, mutual information and relative entropy (Kullback-leibler divergence) were computed between each of the clusters and then cut-scores were determined by the cluster’s partition to minimize mutual information values. Experimental results show that the approach of this proposed entropy method has a realistic possibility of application as a clustering evaluation method

Original language	English
Title of host publication	Workshop Proceedings of the 23rd International Conference on Computers in Education ICCE 2015
Editors	Hiroaki Ogata, Tomoko Kojiri, Thepchai Supnithi, Yonggu Wang, Ying-Tien Wu, Weiqin Chen, Siu Cheung Kong, Feiyue Qiu
Place of Publication	Japan
Publisher	Asia Pacific Society for Computers in Education (APSCE)
Pages	418-422
Number of pages	5
Volume	1
Edition	1
ISBN (Print)	978-4-9908014-7-2
Publication status	Published - 2015
Event	International Conference on Computers in Education (ICCE 2015) - Hangzhou China, Hangzhou, China Duration: 30 Nov 2015 → 4 Dec 2015 Conference number: 2015 (23rd)

Conference

Conference	International Conference on Computers in Education (ICCE 2015)
Abbreviated title	ICCE
Country	China
City	Hangzhou
Period	30/11/15 → 4/12/15

Access to Document

Link to conference proceedings

Fingerprint Dive into the research topics of 'A Clustering Method using Entropy for Grouping Students'. Together they form a unique fingerprint.

Cite this

Kim, B., Mason, J. C., & Shon, J. G. (2015). A Clustering Method using Entropy for Grouping Students. In H. Ogata, T. Kojiri, T. Supnithi, Y. Wang, Y-T. Wu, W. Chen, S. C. Kong, & F. Qiu (Eds.), Workshop Proceedings of the 23rd International Conference on Computers in Education ICCE 2015 (1 ed., Vol. 1, pp. 418-422). Asia Pacific Society for Computers in Education (APSCE).

Kim, Byoung ; Mason, Jonathan Charles ; Shon, Jin Gon. / A Clustering Method using Entropy for Grouping Students. Workshop Proceedings of the 23rd International Conference on Computers in Education ICCE 2015. editor / Hiroaki Ogata ; Tomoko Kojiri ; Thepchai Supnithi ; Yonggu Wang ; Ying-Tien Wu ; Weiqin Chen ; Siu Cheung Kong ; Feiyue Qiu. Vol. 1 1. ed. Japan : Asia Pacific Society for Computers in Education (APSCE), 2015. pp. 418-422

@inproceedings{c54998a9278044d4a56f1a5959ba2061,

title = "A Clustering Method using Entropy for Grouping Students",

abstract = "This study suggests a novel clustering method using entropy in information theory for setting cut-scores. Based on item response vectors from the examinees, we construct the Ordered Item Booklets (OIBs) based on the Rasch model which is a kind of Item Response Theory (IRT). The approach of the proposed method is to partition the scores into n-clusters and to construct probability distribution tables separately for each cluster from the item response vector. Using these probability distribution tables, mutual information and relative entropy (Kullback-leibler divergence) were computed between each of the clusters and then cut-scores were determined by the cluster{\textquoteright}s partition to minimize mutual information values. Experimental results show that the approach of this proposed entropy method has a realistic possibility of application as a clustering evaluation method",

author = "Byoung Kim and Mason, {Jonathan Charles} and Shon, {Jin Gon}",

year = "2015",

language = "English",

isbn = "978-4-9908014-7-2",

volume = "1",

pages = "418--422",

editor = "Hiroaki Ogata and Tomoko Kojiri and Thepchai Supnithi and Yonggu Wang and Ying-Tien Wu and Weiqin Chen and Kong, {Siu Cheung} and Feiyue Qiu",

booktitle = "Workshop Proceedings of the 23rd International Conference on Computers in Education ICCE 2015",

publisher = "Asia Pacific Society for Computers in Education (APSCE)",

edition = "1",

note = "International Conference on Computers in Education (ICCE 2015), ICCE ; Conference date: 30-11-2015 Through 04-12-2015",

}

Kim, B, Mason, JC & Shon, JG 2015, A Clustering Method using Entropy for Grouping Students. in H Ogata, T Kojiri, T Supnithi, Y Wang, Y-T Wu, W Chen, SC Kong & F Qiu (eds), Workshop Proceedings of the 23rd International Conference on Computers in Education ICCE 2015. 1 edn, vol. 1, Asia Pacific Society for Computers in Education (APSCE), Japan, pp. 418-422, International Conference on Computers in Education (ICCE 2015), Hangzhou, China, 30/11/15.

A Clustering Method using Entropy for Grouping Students. / Kim, Byoung; Mason, Jonathan Charles; Shon, Jin Gon.

Workshop Proceedings of the 23rd International Conference on Computers in Education ICCE 2015. ed. / Hiroaki Ogata; Tomoko Kojiri; Thepchai Supnithi; Yonggu Wang; Ying-Tien Wu; Weiqin Chen; Siu Cheung Kong; Feiyue Qiu. Vol. 1 1. ed. Japan : Asia Pacific Society for Computers in Education (APSCE), 2015. p. 418-422.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings

TY - GEN

T1 - A Clustering Method using Entropy for Grouping Students

AU - Kim, Byoung

AU - Mason, Jonathan Charles

AU - Shon, Jin Gon

N1 - Conference code: 2015 (23rd)

PY - 2015

Y1 - 2015

N2 - This study suggests a novel clustering method using entropy in information theory for setting cut-scores. Based on item response vectors from the examinees, we construct the Ordered Item Booklets (OIBs) based on the Rasch model which is a kind of Item Response Theory (IRT). The approach of the proposed method is to partition the scores into n-clusters and to construct probability distribution tables separately for each cluster from the item response vector. Using these probability distribution tables, mutual information and relative entropy (Kullback-leibler divergence) were computed between each of the clusters and then cut-scores were determined by the cluster’s partition to minimize mutual information values. Experimental results show that the approach of this proposed entropy method has a realistic possibility of application as a clustering evaluation method

AB - This study suggests a novel clustering method using entropy in information theory for setting cut-scores. Based on item response vectors from the examinees, we construct the Ordered Item Booklets (OIBs) based on the Rasch model which is a kind of Item Response Theory (IRT). The approach of the proposed method is to partition the scores into n-clusters and to construct probability distribution tables separately for each cluster from the item response vector. Using these probability distribution tables, mutual information and relative entropy (Kullback-leibler divergence) were computed between each of the clusters and then cut-scores were determined by the cluster’s partition to minimize mutual information values. Experimental results show that the approach of this proposed entropy method has a realistic possibility of application as a clustering evaluation method

UR - http://www.apsce.net/icces.php?id=f51279ea-b952-4aeb-b5bb-1b2256ff48dd

M3 - Conference Paper published in Proceedings

SN - 978-4-9908014-7-2

VL - 1

SP - 418

EP - 422

BT - Workshop Proceedings of the 23rd International Conference on Computers in Education ICCE 2015

A2 - Ogata, Hiroaki

A2 - Kojiri, Tomoko

A2 - Supnithi, Thepchai

A2 - Wang, Yonggu

A2 - Wu, Ying-Tien

A2 - Chen, Weiqin

A2 - Kong, Siu Cheung

A2 - Qiu, Feiyue

PB - Asia Pacific Society for Computers in Education (APSCE)

CY - Japan

T2 - International Conference on Computers in Education (ICCE 2015)

Y2 - 30 November 2015 through 4 December 2015

ER -