System for Detection of Specific Learning Disabilities Based on Assessment

Authors

  • Aparna Joshi Assistant Professor, Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune
  • Rupali Bagate Assistant Professor, Department of Information Technology, Army Institute of Technology, Pune
  • Yogita Hambir Assistant Professor, Department of Computer Engineering, Army Institute of Technology, Pune
  • Ashwini Sapkal Associate Professor, Department of Information Technology, Army Institute of Technology, Pune
  • Nilesh P. Sable Associate Professor, Department of Computer Science & Engineering, (Artificial Intelligence), Vishwakarma Institute of Information Technology, Pune.
  • Mahesh Lonare Assistant Professor, Department of Computer Engineering, Army Institute of Technology, Pune

Keywords:

Computer Vision, Natural Language Processing, Medical Automation, Specific Learning Disabilities, Transformer Models

Abstract

Learning disabilities are neurological processing problems brought on by faulty cortical circuitry. According to the RPwD Act of 2016,” Specific learning disabilities (SLD)” refers to a wide range of conditions where there is a deficit in processing language, whether spoken or written. These conditions include perceptual difficulties, dyslexia, dyscalculia, dysgraphia, dyspraxia, and developmental aphasia. They can manifest as difficulty in understanding, speaking, reading, writing, spelling, or performing mathematical calculations. The paper aims to create a hybrid mobile application for analyzing and profiling different learning capacities in students to detect specific learning disabilities and generate error reports using a variety of examinations/tests that include eyeball tracking, handwriting analysis, logical test, and speech evaluation. Remedies and solutions for different learning incapability are aggregated over the internet through Google Search and YouTube API from authentic sources. Based on the child error report, Doctors/Psychiatrists specializing in their field will be recommended. The reviewed papers cover a variety of approaches to detect the SLDs using automated systems, including manual screening methods, Q/A and eyeball tracking-based approaches, and deep-learning techniques. The performance of these approaches has been evaluated using various metrics. The research paper also addresses a range of challenges in SLD detection, such as handling complex and multiple SLDs, generating diverse and coherent questions for detection, and improving the quality and relevance of reports generated.

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Published

27.12.2023

How to Cite

Joshi, A. ., Bagate, R. ., Hambir, Y. ., Sapkal, A. ., Sable, N. P. ., & Lonare, M. . (2023). System for Detection of Specific Learning Disabilities Based on Assessment. International Journal of Intelligent Systems and Applications in Engineering, 12(9s), 362–368. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4325

Issue

Vol. 12 No. 9s (2024)

Section

Research Article

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