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.

Downloads

Download data is not yet available.

References

O. Barukab, A. Ahmad, T. Khan, and M. R. Thayyil Kunhu muhammed, “Analysis of parkinsonrsquo;s disease using an imbalanced-speech dataset by employing decision tree ensemble methods,” Diagnostics, vol. 12, no. 12, 2022.

Rayner and Keith, “Eye movements in reading and information processing. psychological bulletin,” 1978.

A. Al-Edaily, A. Al-Wabil, and Y. Al-Ohali, “Dyslexia explorer: A screening system for learning difficulties in the arabic language using eye tracking,” in Human Factors in Computing and Informatics, pp. 831–834, Springer Berlin Heidelberg, 2013.

R. Tibshirani, “Regression shrinkage and selection via the lasso,” Journal of the Royal Statistical Society. Series B (Methodological), vol. 58, no. 1, pp. 267–288, 1996.

O. R. K. Hyo¨na¨, J., “Eye fixation patterns among dyslexic and normal readers: Effects of word length and word frequency.,” 1995.

R. Muthukrishnan and R. Rohini, “Lasso: A feature selection technique in predictive modeling for machine learning,” in 2016 IEEE International Conference on Advances in Computer Applications (ICACA), pp. 18–20, 2016.

R. Muthukrishnan and R. Rohini, “Lasso: A feature selection tech- nique in predictive modeling for machine learning,” in 2016 IEEE International Conference on Advances in Computer Applications (ICACA), pp. 18–20, 2016.

P. Bonifacci, V. Tobia, A. Sansavini, and A. Guarini, “Eye-movements in a text reading task: A comparison of preterm children, children with dyslexia and typical readers,” Brain Sciences, vol. 13, no. 3, 2023.

I. Smyrnakis, V. Andreadakis, V. Selimis, M. Kalaitzakis, T. Ba- chourou, G. Kaloutsakis, G. D. Kymionis, S. Smirnakis, and I. M. Aslanides, “Radar: A novel fast-screening method for reading difficulties with special focus on dyslexia,” PLOS ONE, vol. 12, pp. 1–26, 08 2017.

M. Nilsson Benfatto, G. O¨ qvist Seimyr, J. Ygge, T. Pansell, A. Ry- dberg, and C. Jacobson, “Screening for dyslexia using eye tracking during reading,” PLOS ONE, vol. 11, pp. 1–16, 12 2016.

A. B. Kuerten, M. B. Mota, and K. Segaert, “Developmental dyslexia: a condensed review of literature,” Ilha do Desterro, vol. 72, p. 249–270, Sep 2019.

L. Rello and M. Ballesteros, “Detecting readers with dyslexia using machine learning with eye tracking measures,” in Proceedings of the 12th International Web for All Conference, W4A ’15, (New York, NY, USA), Association for Computing Machinery, 2015.

G. Eden, J. Stein, H. Wood, and F. Wood, “Differences in eye movements and reading problems in dyslexic and normal children,” Vision Research, vol. 34, no. 10, pp. 1345–1358, 1994.

T. Asvestopoulou, V. Manousaki, A. Psistakis, I. Smyrnakis, V. An- dreadakis, I. M. Aslanides, and M. Papadopouli, “Eye movements in reading and information processing: 20 years of research.,” 1998.

Rayner and Keith, “Eye movements in reading and information processing. psychological bulletin,” 1978.

G. Eden, J. Stein, H. Wood, and F. Wood, “Differences in eye movements and reading problems in dyslexic and normal children,” Vision Research, vol. 34, no. 10, pp. 1345–1358, 1994.

R. Elterman, L. Abel, R. Daroff, L. Dell’Osso, and J. Bornstein, “Eye movement patterns in dyslexic children,” Journal of Learning Disabilities, vol. 13, no. 1, pp. 16–21, 1980. PMID: 7373140.

K. Rayner, “Eye movements in reading and information processing: 20 years of research.,” 1998.

Downloads

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://www.ijisae.org/index.php/IJISAE/article/view/4325

Issue

Vol. 12 No. 9s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.

Most read articles by the same author(s)