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 |
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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) |
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Abbreviated title | ICCE |
Country/Territory | China |
City | Hangzhou |
Period | 30/11/15 → 4/12/15 |