Authors: Aditiya Sawant, Devansh Sojitra, Rohan Maheta
Abstract: In modern educational environments, a significant number of students face learning difficulties due to slower cognitive processing, limited conceptual understanding, low confidence, and the inability of traditional teaching systems to address individual learning needs. These students, commonly referred to as slow learners, often require personalized academic support and innovative teaching strategies to achieve educational success. This paper presents Supporting Slow Learners with Remedial Innovation, an intelligent and inclusive educational support platform designed to enhance the academic performance and confidence of slow learners through customized remedial solutions. The proposed system integrates personalized learning plans, adaptive teaching methodologies, educator guidance, progress monitoring, and technology-assisted learning resources to create a supportive learning ecosystem. The platform allows educators to identify student weaknesses, assign customized study materials, track academic progress, and provide targeted interventions based on individual performance. Students can access simplified learning content, practice modules, motivational feedback, and continuous assessments according to their pace of learning. The system is developed using modern web technologies with a scalable frontend-backend architecture to ensure accessibility, usability, and performance. It supports multiple user roles including students, educators, and administrators for efficient management and monitoring. Experimental outcomes indicate improvements in student engagement, learning consistency, conceptual understanding, and confidence levels when compared with conventional classroom-only teaching approaches. This project demonstrates how remedial innovation combined with digital technologies can transform the educational journey of slow learners by promoting equal learning opportunities, reducing academic gaps, and creating an inclusive learning environment. The proposed framework can be extended in future with Artificial Intelligence, predictive analytics, and multilingual learning support for wider educational impact.
DOI: http://doi.org/
