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Common and Assistive Technology to Support People with Specific Learning Disabilities to Access Healthcare
Published in Christopher M. Hayre, Dave J. Muller, Marcia J. Scherer, Everyday Technologies in Healthcare, 2019
Dianne Chambers, Sharon Campbell
Dyscalculia consists of a wide variety of difficulties in the area of mathematics. The Dyslexia SPELD Foundation (2014) describe a person with dyscalculia as having difficulty with learning number concepts, manipulating numbers, learning facts and identifying mathematical patterns. Butterworth, Varma and Laurillard (2011) describe dyscalculia as being the ‘poor cousin’ of dyslexia. Although it has a similar prevalence rate of 5%–7%, there has been less research and interest than in dyslexia.
Designing accessible MOOCs to expand educational opportunities for persons with cognitive impairments
Published in Behaviour & Information Technology, 2021
Pierre-Antoine Cinquin, Pascal Guitton, Hélène Sauzéon
This issue is even more important for individuals with cognitive impairments who experience the lowest employment rates (Thornicroft, Rose, and Mehta, 2010) and are most often to be employed in a segregated environment (Verdonschot et al., 2009). Furthermore, an increasing number of students report Attention Deficit/Hyperactivity Disorder (ADHD), specific learning disorder (e.g. Dyslexia, Dyscalculia, etc.) or Mental health disorders (e.g. Schizophrenia, depression or bipolar disorder, etc.) (Schelly, Davies, and Spooner, 2011). Although they represent distinct disorders with specific causes, symptoms and consequences, they exhibit a frequent comorbidity in several cognitive impairments. For example, deficits in reading ability is observed not only in persons with dyslexia but also in those with schizophrenia (Revheim et al., 2014). Moreover, such impairments can also be found in acquired neuropsychological disorders (e.g. traumatic brain injury, stroke, tumor, etc.) and can appear when aging (Craik and Salthouse, 2011). The overlapping of symptoms suggest to consider these clinical categories in light of their association with specific and global cognitive function impairments despite different medical conditions. In this paper we rely on a functional view of impairments, following the biopsychosocial framework proposed by the International Classification of Functioning, Disability and Health, promoted by the WHO and widely used by disability experts (World Health Organization, 2001; Gillespie, Best, and Oneill, 2014).
Monitoring the nomological network of number sense studies
Published in International Journal of Mathematical Education in Science and Technology, 2021
In the keyword analysis, the terms, such as mathematics, numerical cognition, approximate number system, kindergarten, arithmetic, working memory, early numeracy, dyscalculia, intervention and estimation, were found to be the top ten keywords of the number sense-related studies. Within these terms, numerical cognition, approximate number system, arithmetic, early numeracy, working memory and dyscalculia are closely related to number sense. Apart from these keywords, mathematics has broader structure covering number sense, kindergarten shows the grade level of related studies and intervention gives clues about the methodology of the studies. It is noticed that especially in older studies, the researchers focused on understanding the development of number sense in arithmetic perspective. However, recent studies investigated the closely related subjects such as approximate number sense and working memory. The number of publications has an increasing trend in almost all of the highly occurred keywords except kindergarten, arithmetic and working memory. Although there is a slight decrease in the occasion for these terms, these keywords keep their popularity in number sense-related articles.
Machine Learning Techniques in Adaptive and Personalized Systems for Health and Wellness
Published in International Journal of Human–Computer Interaction, 2023
Oladapo Oyebode, Jonathon Fowles, Darren Steeves, Rita Orji
For sickle cell patients, Khalaf et al. (2016) developed a web-based system that leverages an MLP model (MAPE = 0.1345) to predict the correct amount of medication (Hydroxycarbamide drugs/liquid) or dose with the aim of providing accurate personalized therapeutic recommendations. Abd et al. (2017) attempted to classify patients into those with sickle cell trait and those without the trait using the best performing model: LogitBoost (acc = 99.6%). For patients with sickle cell trait, the system further analyzed their clinical data (such as blood tests) to determine if the situation is critical or not; if critical, the patient receives personalized recommendations and treatment directly. Otherwise, the system contacts the physician directly to suggest the proper action that the patient should follow. Kariyawasam et al.’s Pubudu system aimed to enhance reading, writing and mathematical skills of children with dyslexia, dysgraphia, and dyscalculia conditions by providing tailored interventions (Kariyawasam et al., 2019). For dyslexia, the system applied CNN model to determine whether letters are pronounced correctly or not (acc = 65%) and KNN to predict whether a child has dyslexia or not. For dysgraphia, CNN model determines whether letters are written correctly or not (acc = 85%) while RF classifier (acc = 90%) was used to check the correctness of hand-written numbers. In addition, SVM classifier was used to determine if a child has the disease or not. For dyscalculia, an SVM model was used to detect the existence of the disease with high accuracy of 90%. Appropriate interventions are automatically triggered by the system to support the child. For example, if a child was predicted to be letter dysgraphic, he/she is trained on how to write letters in proceeding path with animations.