An Efficient Machine Learning Approache for Enhancing Learning Outcomes of Disabled Students

Abstract

This paper discusses the transformative role of machine learning (ML) in predicting and enhancing educational outcomes for students with disabilities. Based on data-mining techniques on educational performance data for disabled persons, this study shows that adaptive and predictive frameworks can identify critical success factors. This analysis features Python libraries; it demonstrates how personal learning strategies and educational environments can improve academic results, especially when an individual takes full responsibility for their own regulation of learning.

The integration of these methods into a cloud architecture allows examination of trends and patterns on the fly, ensuring immediate actionable insights. This reaches a 90% predictability rate for results; proof that ML models can direct appropriate interventions. This research evidence goes to affirm the capabilities of artificial intelligence in solving some of the educational disparities and facilitating inclusive learning, especially through optimal learning methods appropriate for different students.

However, the study is encouraging but suggests further research in details about educational datasets by using more complex ML techniques like deep learning. Integration of psychological and environmental factors into the predictive models can be one step toward the more holistic understanding of academic achievements.

This study will call for increased innovation in achieving better access and equity in learning opportunities in educational systems. Scaling and AI-driven solutions embedded with the findings can well service diverse needs of learners. This development of education through inclusive practice can promote outcomes for education for students living with disabilities, as research advances the more significant narrative that will make education equitable and effective in the lives of students in all parts of the globe.