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

TL;DR

This paper introduces a recurrent neural network-based clustering method tailored for binary educational data, effectively classifying large datasets into smaller, meaningful groups by leveraging network dynamics and fixed points.

Contribution

The paper presents a novel clustering approach using RNN dynamics for binary data, specifically addressing the challenge of large S-P charts in education.

Findings

The method successfully classifies large binary datasets into smaller clusters.

The average caution index effectively characterizes student answer pattern singularity.

Experimental results confirm the effectiveness of the proposed clustering technique.

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.