Exploring Machine Learning Algorithms for Analysing Students' Attitudes Towards Distance Mathematics Learning

Abstract

This study investigates the application of machine learning algorithms to analyse students' attitudes towards distance mathematics education, focusing on perceived effectiveness and students' ability to successfully learn and adopt mathematical content in an online setting. Data were collected from 1154 students at various educational levels using a 28- item Likert scale questionnaire on distance mathematics learning. An ML pipeline incorporating multiple data preparation techniques and machine learning algorithms was applied to two key prediction questions. Unlike predominantly descriptive or single model studies in this area, this approach evaluates both predictive performance and the stability of selected survey items across many model configurations, providing more robust and interpretable pedagogical insights. The results show that Recursive Feature Elimination was the most effective feature selection method, while Random Forest, Ridge Classifier and Categorical Naive Bayes achieved the strongest overall predictive performance across the two questions. These findings confirm the value of combining feature selection techniques and machine learning algorithms to derive robust and interpretable insights from educational survey data, while also highlighting the importance of well- structured distance learning strategies in mathematics education. The methodology is readily adaptable to other academic disciplines, providing educators with a data- driven framework for improving the design and effectiveness of online learning.