Two classification methods of individuals for educational data and an application

Atsuhiro Hayashi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Both methods, Rule Space Method (RSM) and Neural Network Model (NNM), are techniques of statistical pattern recognition and classification approaches developed from different fields - one is for behavioural sciences and the other is for neural sciences. RSM is developed in the domain of educational statistics. It starts from the use of an incidence matrix Q that characterises the underlying cognitive processes and knowledge (Attribute) involved in each Item. It is a grasping method for each examinee's mastered/non-mastered learning level (Knowledge State) from item response patterns. RSM uses multivariate decision theory to classify individuals, and NNM, considered as a nonlinear regression method, uses the middle layer of the network structure as classification results. We have found some similarities and differences between the results from the two approaches, and moreover both methods have characteristics supplemental to each other when applied to the practice. In this paper, we compare both approaches by focusing on the structures of NNM and on knowledge States in RSM. Finally, we show an application result of RSM for a reasoning test in Japan.

Original languageEnglish
Title of host publicationContributions to Probability and Statistics
Subtitle of host publicationApplications and Challenges - Proceedings of the International Statistics Workshop
PublisherWorld Scientific Publishing Co. Pte Ltd
Pages11-16
Number of pages6
ISBN (Print)9812703918, 9789812703910
DOIs
Publication statusPublished - 2006
EventInternational Statistics Workshop on Contributions to Probability and Statistics: Applications and Challenges - Canberra, ACT, Australia
Duration: Apr 4 2005Apr 5 2005

Publication series

NameContributions to Probability and Statistics: Applications and Challenges - Proceedings of the International Statistics Workshop

Other

OtherInternational Statistics Workshop on Contributions to Probability and Statistics: Applications and Challenges
CountryAustralia
CityCanberra, ACT
Period4/4/054/5/05

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

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