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: Applications and Challenges - Proceedings of the International Statistics Workshop
Pages11-16
Number of pages6
Publication statusPublished - 2006
Externally publishedYes
EventInternational Statistics Workshop on Contributions to Probability and Statistics: Applications and Challenges - Canberra, ACT, Australia
Duration: Apr 4 2005Apr 5 2005

Other

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

Fingerprint

Neural Network Model
Characteristics Method
Incidence Matrix
Grasping
Decision Theory
Education
Nonlinear Regression
Pattern Classification
Japan
Network Structure
Pattern Recognition
Reasoning
Classify
Attribute
Statistics
Knowledge
Similarity
Learning

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

Cite this

Hayashi, A. (2006). Two classification methods of individuals for educational data and an application. In Contributions to Probability and Statistics: Applications and Challenges - Proceedings of the International Statistics Workshop (pp. 11-16)

Two classification methods of individuals for educational data and an application. / Hayashi, Atsuhiro.

Contributions to Probability and Statistics: Applications and Challenges - Proceedings of the International Statistics Workshop. 2006. p. 11-16.

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

Hayashi, A 2006, Two classification methods of individuals for educational data and an application. in Contributions to Probability and Statistics: Applications and Challenges - Proceedings of the International Statistics Workshop. pp. 11-16, International Statistics Workshop on Contributions to Probability and Statistics: Applications and Challenges, Canberra, ACT, Australia, 4/4/05.
Hayashi A. Two classification methods of individuals for educational data and an application. In Contributions to Probability and Statistics: Applications and Challenges - Proceedings of the International Statistics Workshop. 2006. p. 11-16
Hayashi, Atsuhiro. / Two classification methods of individuals for educational data and an application. Contributions to Probability and Statistics: Applications and Challenges - Proceedings of the International Statistics Workshop. 2006. pp. 11-16
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