### 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 language | English |
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Title of host publication | Contributions to Probability and Statistics: Applications and Challenges - Proceedings of the International Statistics Workshop |

Pages | 11-16 |

Number of pages | 6 |

Publication status | Published - 2006 |

Externally published | Yes |

Event | International Statistics Workshop on Contributions to Probability and Statistics: Applications and Challenges - Canberra, ACT, Australia Duration: Apr 4 2005 → Apr 5 2005 |

### Other

Other | International Statistics Workshop on Contributions to Probability and Statistics: Applications and Challenges |
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Country | Australia |

City | Canberra, ACT |

Period | 4/4/05 → 4/5/05 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Statistics and Probability

### Cite this

*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.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*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.

}

TY - GEN

T1 - Two classification methods of individuals for educational data and an application

AU - Hayashi, Atsuhiro

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84891275618&partnerID=8YFLogxK

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M3 - Conference contribution

AN - SCOPUS:84891275618

SN - 9812703918

SN - 9789812703910

SP - 11

EP - 16

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

ER -