Exploring the Feasibility of Employing a Hybrid Machine Learning Method to Unpack Student Reasoning Patterns in Physics Essays
Abstract
We propose a novel clustering pipeline that combines two classic clustering algorithms to better understand student problem-solving strategies. This unsupervised machine learning method helps uncover patterns in reasoning without pre-defined labels. We applied it to essays written for an online multiple-choice quiz, the resulting clusters showed strong statistical alignment with students' selected answers. We also report on the resulting clusters of the hybrid pipeline compared to that of K-Means (MacQueen, 1967) and Hierarchal Density-Based Spatial Clustering of Application with Noise (HDBSCAN) (McInnes, Healy, and Astels, 2017) by analyzing the Scatter Plots, Silhouette Scores (Rousseeuw, 1987), and Davies Bouldin Index (Davies and Bouldin, 1979).
Cite
@article{arxiv.2504.08904,
title = {Exploring the Feasibility of Employing a Hybrid Machine Learning Method to Unpack Student Reasoning Patterns in Physics Essays},
author = {Winter Allen and N. Sanjay Rebello},
journal= {arXiv preprint arXiv:2504.08904},
year = {2025}
}