Comparative Analysis of Adaptive Learning and Fast for Word Programs for ASD Students in Learning English, and Mathematics, and Predicting Future Academic Performance Using Machine Learning Algorithms

Abstract

This study investigates the efficacy of adaptive learning methods in teaching English and Mathematics to students diagnosed with autism spectrum disorder (ASD), compared to the Fast ForWord program. Utilizing a randomized controlled trial design, students aged 6-7 were assigned to either the adaptive learning group or the Fast ForWord group. Pre- and post-tests in English and Mathematics, along with engagement and behavior checklists, were used to assess outcomes. We employed machine learning techniques, including Support Vector Machine (SVM), K Nearest Neighbor (KNN), Gaussian Process Regressor (GPR), and Logistic Regression (LR), to predict student scores and analyze the effectiveness of these educational interventions. Results indicate that the Gaussian Process Regressor (GPR) is the best for predicting students’ future grades, and adaptive learning methods significantly improved academic performance and engagement compared to the Fast ForWord program, suggesting a need for personalized educational strategies in ASD. These findings have significant implications for educators and policymakers seeking to enhance educational outcomes for students with ASD.

Keywords: Autism Spectrum Disorder (ASD), Fast For Word Program, Support Vector Machine (SVM), K Nearest Neighbor (KNN), Gaussian Process Regressor (GPR).

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