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arXiv · 2508.13224

A Recurrent Neural Network based Clustering Method for Binary Data Sets in Education

Mizuki Ohira·Toshimichi Saito
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Abstract

This paper studies an application of a recurrent neural network to clustering method for the S-P chart: a binary data set used widely in education. As the number of students increases, the S-P chart becomes hard to handle. In order to classify the large chart into smaller charts, we present a simple clustering method based on the network dynamics. In the method, the network has multiple fixed points and basins of attraction give clusters corresponding to small S-P charts. In order to evaluate the clustering performance, we present an important feature quantity: average caution index that characterizes singularity of students answer oatterns. Performing fundamental experiments, effectiveness of the method is confirmed.

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Mizuki Ohira, Toshimichi Saito. 2025-08-17. A Recurrent Neural Network based Clustering Method for Binary Data Sets in Education. https://arxiv.org/abs/2508.13224

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