Learning disabilities like dysgraphia, dyslexia, dyspraxia, etc. interfere with academic achievements yet have likewise long terms consequences beyond academic time. It is widely admitted that between 5% to 10% of the total population is subject to this kind of disability. For assessing such disabilities in early childhood, children have to solve a battery of tests. Human experts score these tests and decide whether the children require specific education strategies based on their marks. The assessment can be lengthy, exorbitant, and emotionally difficult. Dyslexia is a learning disorder characterized by a lack of reading and/or composing skills, trouble in fast word naming and likewise poor in spelling. Dyslexic people have great trouble to read and interpret words or letters. Research work is carried out to order dyslexic from non-dyslexics by different approaches, for example, machine learning, image processing, understanding the cerebrum behaviour through brain science, contemplating the differences in life systems of mind. In recent years, e-learning systems have played an increasingly significant role in higher education and, specifically, in enhancing learning experiences for people who have learning difficulties. However, huge numbers of the people involved in the development and implementation of e-learning instruments overlook the needs of dyslexic students. In this paper, a detailed literature survey is carried on the machine techniques for the prediction of dyslexia students and e-learning for learning and cognitive disabilities.

Machine Learning, dysgraphia, e-learning, brain science, cognitive

[1] Asteriadis, Stylianos, et al. "Estimation of behavioral user state based on eye gaze and head pose—application in an e-learning environment." Multimedia Tools and Applications 41.3, 469-493, 2009.
[2] Srivastava, Bhavana, and Md Tanwir Uddin Haider. "Personalized assessment model for alphabets learning with learning objects in e-learning environment for dyslexia." Journal of King Saud University-Computer and Information Sciences, 2017.
[3] David, Julie M., and Kannan Balakrishnan. "Machine learning approach for prediction of learning disabilities in school-age children." International Journal of Computer Applications 9.11, 7-12, 2010.
[4] Kroese, Biza Senfert. "Cognitive-behavioural therapy for people with learning disabilities." Behavioural and Cognitive Psychotherapy 26.4, 315-322, 1998.
[5] Polatajko, Helene J., and Noemi Cantin. "Developmental coordination disorder (dyspraxia): an overview of the state of the art." Seminars in pediatric neurology. Vol. 12. No. 4. WB Saunders, 2005.
[6] Cui, Zaixu, et al. "Disrupted white matter connectivity underlying developmental dyslexia: a machine learning approach." Human brain mapping 37.4, 1443-1458, 2016.
[7] Ba?tanlar, Yalin, and Mustafa Özuysal. "Introduction to machine learning." miRNomics: MicroRNA Biology and Computational Analysis. Humana Press, Totowa, NJ, 105-128, 2014.
[8] Smola, Alex, and S. V. N. Vishwanathan. "Introduction to machine learning." Cambridge University, UK 32.34 (2008): 2008.
[9] Malav, Anuraj, and Neelu J. Ahuja. "Machine Learning Techniques for Effective Facilitation of Teaching and Learning: A Narrative Review." i-Manager`s Journal on Computer Science 6.2, 42, 2018.
[10] Khan, Rehman Ullah, Julia Lee Ai Cheng, and Oon Yin Bee. "Machine learning and Dyslexia: Diagnostic and classification system (DCS) for kids with learning disabilities." International Journal of Engineering & Technology 7.3.18, 97-100, 2018.
[11] El Hammoumi, Oussama, et al. "Emotion Recognition in E-learning Systems." 2018 6th International Conference on Multimedia Computing and Systems (ICMCS). IEEE, 2018.
[12] Chitra, K., and R. Umamaheswari. "Semantically Enchanced Personalised Adaptive E- Learning for General and Dyslexia Learners: An Ontology Based Approach." International Journal of Advanced Networking and Applications 10.1, 3717-3723, 2018.
[13] Rajapakse, Sampath, et al. "ALEXZA: A Mobile Application For Dyslexics Utilizing Artificial Intelligence And Machine Learning Concepts." 2018 3rd International Conference on Information Technology Research (ICITR). IEEE, 2018.
[14] Hamid, Siti Suhaila Abdul, et al. "Dyslexia adaptive learning model: student engagement prediction using machine learning approach." International Conference on Soft Computing and Data Mining. Springer, Cham, 2018.
[15] Rello, Luz, et al. "Screening dyslexia for English using HCI measures and machine learning." Proceedings of the 2018 international conference on digital health. 2018.
[16] Chu, Hui-Chuan, et al. "Facial emotion recognition with transition detection for students with high-functioning autism in adaptive e-learning." Soft Computing 22.9 : 2973-2999, 2018
[17] Cinquin, Pierre-Antoine, Pascal Guitton, and Hélène Sauzéon. "Online e-learning and cognitive disabilities: A systematic review." Computers & Education 130: 152-167, 2019.
[18] Elhammoumi, Oussama, et al. "The Use of NN to Detect Learning Styles of Children with Learning Disabilities in E-Learning System." International Conference on Advanced Intelligent Systems for Sustainable Development. Springer, Cham, 2019.
[19] Atkar, Geeta Bhimrao, and J. Priyadarshini. "Enhancing Readability of Dyslexic Children by Machine Learning Techniques—A Survey." Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems. Springer, Singapore, 2019.
[20] Jothi Prabha, A., and R. Bhargavi. "Prediction of dyslexia from eye movements using machine learning." IETE Journal of Research (): 1-10, 2019
[21] Chakraborty, Ms Vani. "A SURVEY PAPER ON LEARNING DISABILITY PREDICTION USING MACHINE LEARNING.", International Journal of Information and Computing Science, Volume 6, Issue 5, pp. 481-485. May 2019,
[22] Dcruz, Francis, Vijitashw Tiwari, and Mayur Soni. "Using Machine Learning to Help Students with Learning Disabilities Learn." International Conference on Sustainable Communication Networks and Application. Springer, Cham, 2019.
[23] Hamid, Siti Suhaila Abdul, et al. "Engagement Prediction in the Adaptive Learning Model for Students with Dyslexia." Proceedings of the 4th International Conference on Human-Computer Interaction and User Experience in Indonesia, CHIuXiD`18. 2018.
[24] Rezvani, Z., et al. "Machine learning Classification of Dyslexic Children based on EEG Local Network Features." bioRxiv : 569996, 2019.
[25] Mehigan, T., and I. Pitt. "ENGAGING LEARNERS THROUGH EMOTION IN ARTIFICIALLY INTELLIGENT ENVIRONMENTS." Proceedings of EDULEARN19 Conference 1st-3rd Palma, Mallorca, Spain, pp. 5661-5668. July 2019,
[26] Kaisar, Shahriar. "Developmental dyslexia detection using machine learning techniques: A survey." ICT Express, 2020.
[27] Chakraborty, Ms Vani, and Meentachi Sundaram. "Machine learning algorithms for prediction of dyslexia using eye movement." Journal of Physics:
Conference Series. Vol. 1427. No. 1. IOP Publishing, 2020.
[28] Knoop-van Campen, Carolien AN, Eliane Segers, and Ludo Verhoeven. "Effects of audio support on multimedia learning processes and outcomes in students with dyslexia." Computers & Education 150: 103858, 2020
[29] Trivedi, Mr Viraj, et al. "Detecting the Severity and the Type of Learning Disability with Pattern Extraction Using Machine Learning." dyslexia 16: 18.
[30] Low, Spencer. "Applying Machine Learning to Neuroimaging Data to Identify Predictive Models of Reading Disorder (RD)." 2020.
[31] Usman, Opeyemi Lateef, and Ravie Chandren Muniyandi. "CryptoDL: Predicting Dyslexia Biomarkers from Encrypted Neuroimaging Dataset Using Energy-Efficient Residue Number System and Deep Convolutional Neural Network." Symmetry 12.5: 836, 2020.
[32] Fellman, Daniel, et al. "Predicting visuospatial and verbal working memory by individual differences in e-learning activities." Frontiers in Education. Vol. 5. Frontiers, 2020.
[33] Martinez-Murcia, Francisco J., et al. "EEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia." International Journal of Neural Systems : 2050037, 2020
[34] Razzaque, Anjum, and Allam Hamdan. "Internet of things for Learning Styles and Learning Outcomes Improve e-Learning: A Review of Literature." Joint European-US Workshop on Applications of Invariance in Computer Vision. Springer, Cham, 2020
[35] Vinutha K.N and K.S.Sampada “Survey of Automated Recommender System for Web Application”,s International Journal of Computer Science and Engineering Vol.4 , Issue 3, pp. 36-39 , 2016.