Intelligent approaches for early prediction of learning disabilities in children using learning patterns: A survey and discussion
Abstract
Learning disabilities in children occur in early childhood age. These disabilities include dyslexia, dysgraphia, dyscalculia, ADHD, etc. These children face difficulty in academic progress in life. Difficulties include reading, writing, and spelling words, despite these students possessing normal or above-average intelligence. The learning gap between these students and others increases with time. As a result, these students become less motivated, find it difficult to progress in life, and struggle with employment opportunities. Children with these symptoms often have emotional consequences, including frustration and low self-esteem. These disabilities range around 10 to 15% of the total population, which is considerably high. There is an immense need for early diagnosis to provide them with remedial education and special care. Researchers have proposed a diverse range of approaches to detect learning disorders like dyslexia, one of the most common learning disorders. These approaches include the detection of LD using eye tracking, electroencephalography (EEG) scan, detection using handwritten text, the use of a gaming approach, audiovisual approaches, etc. This paper critically analyses recent contributions of intelligent technique-based dyslexia prediction and provides a comparison. Among the mentioned techniques, it is found that detection using eye tracking, EEG, and MRI are costly, complex, and non-scalable. In contrast, detection using handwritten text and a gaming approach is scalable and cost-effective. A character-based approach is presented as word formation is difficult for children for whom English is a second language. Also, in early childhood, children make fewer mistakes in character writing. An experimental setup for handwritten text-based detection is done using the CNN model, and future opportunities for learning disabilities detection are discussed in this paper.
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1. Singh S, Sawani V, Deokate M, et al. Specific learning disability: a 5 year study from India. International Journal of Contemporary Pediatrics. 2017, 4(3): 863. doi: 10.18203/2349-3291.ijcp20171687
2. Learning Disabilities Association of America. Available online: https://ldaamerica.org/types-of-learningdisabilities/dyslexia/ (accessed on 12 Nov 2023).
3. Karande S, Sholapurwala R, Kulkarni M. Managing specific learning disability in schools in India. Indian Pediatrics. 2011, 48(7): 515-520. doi: 10.1007/s13312-011-0090-1
4. Protopapas A, Parrila R. Is Dyslexia a Brain Disorder? Brain Sciences. 2018, 8(4): 61. doi: 10.3390/brainsci8040061
5. Vellaiappan M. Current Legislations for Learning Disabilities in India and Future Prospects. Jindal Journal of Public Policy. 2017, 3(1): 83-96. doi: 10.54945/jjpp.v3i1.116
6. Cortiella C, Horowitz SH. The State of Learning Disabilities: Facts, Trends and Emerging Issues. National Center for Learning Disabilities, 2014.
7. Håkansson J. English Word Formation Processes: The use of affixations and implications for second language learning: A Case Study of Swedish Secondary Schools Grades 7-9 [PhD thesis]. The University of Galve. 2021.
8. Muktamath V, Hegde P, Chand S. Types of Specific Learning Disability. Learning Disabilities - Neurobiology, Assessment, Clinical Features and Treatments 2021. doi: 10.5772/intechopen.100809
9. Samyak Lalit. Disabled Population in India: Data and Facts. Available online: https://wecapable.com/disabled-population-india-data (accessed on 25 May 2018).
10. Germano GD, Giaconi C, Capellini SA. Characterization of Brazilians Students with Dyslexia in Handwriting Proficiency Screening Questionnaire and Handwriting Scale. Psychology Research, 2016. 6: 590–597. doi: 10.17265/2159-5542/2016.10.004
11. Jothi Prabha A, Bhargavi R. Detection of developmental dyslexia with machine learning using eye movement data. Array. 2021, 12: 100087. doi: 10.1016/j.array.2021.100087
12. Asvestopoulou T, Manousaki V, Psistakis A, et al. Aslanides and Maria Papadopouli, DysLexML: Screening Tool for Dyslexia Using Machine Learning. arXiv 2019; arXiv:1903.06274. doi: 10.48550/arXiv.1903.06274
13. Wong I. An Exploratory Study to Investigate Eye Movement Performance and Visual Perceptual Skills in Children with Dyslexia. Asia Pacific Journal of Developmental Differences. 2020, 7(1): 27-60. doi: 10.3850/s2345734120000039
14. Barela JA, Tesima N, Amaral V da S, et al. Visually guided eye movements reduce postural sway in dyslexic children. Neuroscience Letters. 2020, 725: 134890. doi: 10.1016/j.neulet.2020.134890
15. Chakraborty V, Sundaram M. Machine learning algorithms for prediction of dyslexia using eye movement. Journal of Physics: Conference Series. 2020, 1427(1): 012012. doi: 10.1088/1742-6596/1427/1/012012
16. Saluja KS, Dv J, Arjun S, et al. Analyzing Eye Gaze of Users with Learning Disability. Proceedings of the 2019 3rd International Conference on Graphics and Signal Processing. Published online June 2019. doi: 10.1145/3338472.3338481
17. Zhang Y, Zhou W. Research on Dyslexia Detection based on Eye Tracking. 2022 IEEE 2nd International Conference on Electronic Technology, Communication and Information (ICETCI). Changchun, China. 2022. pp. 36-39.
18. Spoon K, Crandall D, Siek K, Fillmore M. Can We (and Should We) Use AI to Detect Dyslexia in Children’s Handwriting? AI for Social Good workshop at NeurIPS, 2019, pp. 1-6.
19. Isa IS, Syazwani Rahimi WN, Ramlan SA, et al. Automated Detection of Dyslexia Symptom Based on Handwriting Image for Primary School Children. Procedia Computer Science. 2019, 163: 440-449. doi: 10.1016/j.procs.2019.12.127
20. Moetesum M, Siddiqi I, Ehsan S, et al. Deformation modeling and classification using deep convolutional neural networks for computerized analysis of neuropsychological drawings. Neural Computing and Applications. 2020, 32(16): 12909-12933. doi: 10.1007/s00521-020-04735-8
21. Jasira KT, Laila VP. DyslexiScan: A Dyslexia Detection Method from Handwriting Using CNN LSTM Model. In: 2023 International Conference on Innovations in Engineering and Technology (ICIET). Muvattupuzha, India. 13 July 2023. pp. 1-6.
22. Kunhoth J, Al Maadeed S, Saleh M, et al. Machine Learning Methods for Dysgraphia Screening with Online Handwriting Features. 2022 International Conference on Computer and Applications (ICCA). Cairo, Egypt. 20 December 2022. pp. 1-6.
23. Skunda J, Nerusil B, Polec J. Method for dysgraphia disorder detection using convolutional neural network. Computer Science Research Notes WSCG 2022 Proceedings, 2022. pp. 152-157.
24. Rello L, Romero E, Rauschenberger M, et al. Screening Dyslexia for English Using HCI Measures and Machine Learning. Association for Computing Machinery, 2018. 80-84. doi: 10.1145/3194658.3194675
25. Gaggi O, Galiazzo G, Palazzi C, et al. A Serious Game for Predicting the Risk of Developmental Dyslexia in Pre-Readers Children. In: 2012 21st International Conference on Computer Communications and Networks (ICCCN). Munich, Germany. July 2012. doi: 10.1109/ICCCN.2012.6289249
26. Ali A, Rello L, Baeza-Yates R, et al. Predicting risk of dyslexia with an online gamified test. arXiv 2019. arXiv:1906.03168v2. doi: 10.48550/arXiv.1906.03168
27. Solenthaler B, Klingler S, K ̈aser T, Gross M. Ten Years of Research on Intelligent Educational Games for Learning Spelling and Mathematics. arXiv 2018. arXiv:1806.03257v1. doi: 10.48550/arXiv.1806.03257
28. Holz H, Ninaus M, Meurers D, et al. Validity and Player Experience of a Mobile Game for German Dyslexic Children. In: Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play Companion Extended Abstracts (CHI PLAY ‘18 Extended Abstracts). Association for Computing Machinery. 2018. New York, NY, USA. pp. 469–478. doi: 10.1145/3270316.3271523
29. Rauschenberger M, Baeza-Yates R, Rello L. Screening risk of dyslexia through a web-game using language-independent content and machine learning. In Proceedings of the 17th International Web for All Conference (W4A ‘20). Association for Computing Machinery, 2020. New York, NY, USA. Article 13, 1–12.
30. Loveline Zeema J, Francis Xavier Christopher D. Evolving Optimized Neutrosophic C means clustering using Behavioral Inspiration of Artificial Bacterial Foraging (ONCMC-ABF) in the Prediction of Dyslexia. Journal of King Saud University - Computer and Information Sciences. 2022, 34(5): 1748-1754. doi: 10.1016/j.jksuci.2019.09.008
31. Zhao J, Liu H, Li J, et al. Improving sentence reading performance in Chinese children with developmental dyslexia by training based on visual attention span. Scientific Reports. 2019, 9(1). doi: 10.1038/s41598-019-55624-7
32. Fiveash A, Schön D, Canette LH, et al. A stimulus-brain coupling analysis of regular and irregular rhythms in adults with dyslexia and controls. Brain and Cognition. 2020, 140: 105531. doi: 10.1016/j.bandc.2020.105531
33. Płoński P, Gradkowski W, Altarelli I, et al. Multi-parameter machine learning approach to the neuroanatomical basis of developmental dyslexia. Human Brain Mapping. 2016, 38(2): 900-908. doi: 10.1002/hbm.23426
34. Perera H, Shiratuddin MF, Wong KW, et al. EEG signal analysis of passage reading and rapid automatized naming between adults with dyslexia and normal controls. In: 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS). Beijing, China. November 2017. pp. 104-108.
35. Sobnath D, Isiaq SO, Rehman IU, et al. Using Machine Learning Advances to Unravel Patterns in Subject Areas and Performances of University Students with Special Educational Needs and Disabilities (MALSEND): A Conceptual Approach. Fourth International Congress on Information and Communication Technology. 2020: 509-517. doi: 10.1007/978-981-32-9343-4_41
36. Rauschenberger M, Baeza–Yates R, Rello L. Technologies for Dyslexia. Web Accessibility. 2019: 603-627. doi: 10.1007/978-1-4471-7440-0_31
37. Abdul Hamid SS, Admodisastro N, Abd AA, et al. Cognitive-Behaviour Intervention in Developing an Adaptive Learning Model for Students with Dyslexia. International Journal of Engineering & Technology (Open Access), pp. 175-181.
DOI: https://doi.org/10.32629/jai.v7i5.1329
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