A Clustering Method using Entropy for Grouping Students

Byoung Kim, Jonathan Charles Mason, Jin Gon Shon

    Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
    Title of host publicationWorkshop Proceedings of the 23rd International Conference on Computers in Education ICCE 2015
    EditorsHiroaki Ogata, Tomoko Kojiri, Thepchai Supnithi, Yonggu Wang, Ying-Tien Wu, Weiqin Chen, Siu Cheung Kong, Feiyue Qiu
    Place of PublicationJapan
    PublisherAsia Pacific Society for Computers in Education (APSCE)
    Pages418-422
    Number of pages5
    Volume1
    Edition1
    ISBN (Print)978-4-9908014-7-2
    Publication statusPublished - 2015
    EventInternational Conference on Computers in Education (ICCE 2015) - Hangzhou China, Hangzhou, China
    Duration: 30 Nov 20154 Dec 2015
    Conference number: 2015 (23rd)

    Conference

    ConferenceInternational Conference on Computers in Education (ICCE 2015)
    Abbreviated titleICCE
    CountryChina
    CityHangzhou
    Period30/11/154/12/15

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

    Cite this