Machine Learning for Early Detection of Learning Disabilities in Primary School Students

This paper introduces a novel framework leveraging machine learning methodologies for the early detection of learning disabilities in primary school students. The significance of early identification in mitigating the impact of learning difficulties on academic performance and overall well-being cannot be overstated. Our approach integrates traditional cognitive assessments with advanced machine learning algorithms, creating a robust model that harnesses the power of diverse data sources. The system incorporates academic records, behavioral observations, and cognitive test results, ensuring a comprehensive evaluation of students’ learning capabilities. A thorough literature survey highlights the limitations of existing approaches and justifies the need for our proposed model. Our methodology encompasses meticulous data collection, preprocessing techniques, and feature selection, followed by the implementation of machine learning models. The chosen models are carefully selected based on their suitability for early detection tasks, emphasizing transparency and interpretability. The implementation model details the programming languages, frameworks, and tools used, providing a comprehensive guide for replication and validation. Results from our experiments demonstrate promising accuracy rates, precision, and recall, affirming the efficacy of our approach in identifying potential learning disabilities at an early stage. In conclusion, this research contributes a valuable tool for educators and administrators to intervene proactively, fostering an inclusive and supportive learning environment for primary school students. The paper concludes by discussing the broader implications of the findings and suggesting avenues for future research in the field of early learning disability detection.