Understanding the role of study strategies and learning disabilities on student academic performance to enhance educational approaches: A proposal using artificial intelligence

Abstract

Statement of problem: The students’ academic performance is influenced by a complex interplay among several factors. Traditional educational approaches often struggle to accommodate the diverse needs of students, leading to suboptimal learning outcomes. Purpose: This article aims to comprehensively understand the role of study strategies and learning disabilities in shaping academic performance. Through the integration of artificial intelligence (AI) tools, the purpose is to propose a decision support system (DSS) for recommendations to improve the educational approach. Method: To identify features with higher explanatory power based on empirical data, we employed an artificial neural network (ANN) to recognize patterns of association between study strategies, learning disabilities, and academic performance. Using the pondered features, a Fuzzy-based AI was built for offering recommendations into effective educational interventions. Conclusions: The findings underscore the significance of study strategies in mitigating the negative impact of learning disabilities on academic performance. By leveraging the proposed AI tools framework, educators can make informed decisions to tailor educational approaches, catering to the unique cognitive profiles of students. Personalized interventions based on identified patterns can lead to improved academic outcomes and greater inclusivity in the learning environment. Practical implications: Educators and policymakers can adopt the proposed data-driven strategies to enhance teaching methodologies, thereby accommodating the varying needs of students with learning disabilities. This approach fosters a more inclusive and equitable educational landscape, promoting academic success for all learners.