Evaluating the Impact of Machine Learning-Based Approach for Elementary Students in Detecting Learning Disabilities
Through Game Interactions
Keywords:
Machine Learning, Learning Disabilities, Educational Games, Early Detection, Student Behavior.Abstract
This study explores the use of machine learning (ML) to assist in identifying potential learning disabilities in elementary students by analyzing their interactions with educational games. While traditional approaches such as teacher observations and standardized assessments are valuable, they may take time and occasionally overlook early conditions like dyslexia, ADHD, or dyscalculia. By examining gameplay data including response time, accuracy, and error patterns, the study applies supervised ML models like Decision Trees and Support Vector Machines to classify students’ learning needs. Findings suggest that ML tools could offer meaningful support to educators in recognizing students who may benefit from early intervention.
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