Two classification methods of individuals for educational data and an application

Atsuhiro Hayashi

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

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.

本文言語英語
ホスト出版物のタイトルContributions to Probability and Statistics
ホスト出版物のサブタイトルApplications and Challenges - Proceedings of the International Statistics Workshop
出版社World Scientific Publishing Co. Pte Ltd
ページ11-16
ページ数6
ISBN(印刷版)9812703918, 9789812703910
DOI
出版ステータス出版済み - 2006
イベントInternational Statistics Workshop on Contributions to Probability and Statistics: Applications and Challenges - Canberra, ACT, オーストラリア
継続期間: 4 4 20054 5 2005

出版物シリーズ

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

その他

その他International Statistics Workshop on Contributions to Probability and Statistics: Applications and Challenges
Countryオーストラリア
CityCanberra, ACT
Period4/4/054/5/05

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

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