Screening dyslexia for English using HCI measures and machine learning

More than 10% of the population has dyslexia, and most are diagnosed only after they fail in school. This work seeks to change this through early detection via machine learning models that predict dyslexia by observing how people interact with a linguistic computer-based game. We designed items of the game taking into account (i) the empirical linguistic analysis of the errors that people with dyslexia make, and (ii) specific cognitive skills related to dyslexia: Language Skills, Working Memory, Executive Functions, and Perceptual Processes. . Using measures derived from the game, we conducted an experiment with 267 children and adults in order to train a statistical model that predicts readers with and without dyslexia using measures derived from the game. The model was trained and evaluated in a 10-fold cross experiment, reaching 84.62% accuracy using the most informative features.Peer ReviewedPostprint (author's final draft

Identification of EEG signal patterns between adults with dyslexia and normal controls

Electroencephalography (EEG) is one of the most useful techniques used to represent behaviours of the brain and helps explore valuable insights through the measurement of brain electrical activity. Hence, it plays a vital role in detecting neurological disorders such as epilepsy. Dyslexia is a hidden learning disability with a neurological origin affecting a significant amount of the world population. Studies show unique brain structures and behaviours in individuals with dyslexia and these variations have become more evident with the use of techniques such as EEG, Functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG) and Positron Emission Tomography (PET). In this thesis, we are particularly interested in discussing the use of EEG to explore unique brain activities of adults with dyslexia. We attempt to discover unique EEG signal patterns between adults with dyslexia compared to normal controls while performing tasks that are more challenging for individuals with dyslexia. These tasks include real--‐word reading, nonsense--‐ word reading, passage reading, Rapid Automatized Naming (RAN), writing, typing, browsing the web, table interpretation and typing of random numbers. Each participant was instructed to perform these specific tasks while staying seated in front of a computer screen with the EEG headset setup on his or her head. The EEG signals captured during these tasks were examined using a machine learning classification framework, which includes signal preprocessing, frequency sub--‐band decomposition, feature extraction, classification and verification. Cubic Support Vector Machine (CSVM) classifiers were developed for separate brain regions of each specified task in order to determine the optimal brain regions and EEG sensors that produce the most unique EEG signal patterns between the two groups. The research revealed that adults with dyslexia generated unique EEG signal patterns compared to normal controls while performing the specific tasks. One of the vital discoveries of this research was that the nonsense--‐words classifiers produced higher Validation Accuracies (VA) compared to real--‐ words classifiers, confirming difficulties in phonological decoding skills seen in individuals with dyslexia are reflected in the EEG signal patterns, which was detected in the left parieto--‐occipital. It was also uncovered that all three reading tasks showed the same optimal brain region, and RAN which is known to have a relationship to reading also showed optimal performance in an overlapping region, demonstrating the likelihood that the association between reading and RAN reflects in the EEG signal patterns. Finally, we were able to discover brain regions that produced exclusive EEG signal patterns between the two groups that have not been reported before for writing, typing, web browsing, table interpretation and typing of random numbers

Lexiland: A Tablet-based Universal Screener for Reading Difficulties in the School Context

First published online January 27, 2022Massive and timely screening of the student population for early signs of reading difficulties is needed to implement timely effective remediation of these difficulties. However, traditional approaches are costly and hard to apply. Here, we present Lexiland, a tablet-based reading assessment tool for kindergarten and primary school children developed to be applied in school settings with minimal personnel intervention. Following a story line, players help a character of the game perform several tasks that measure different predictors of reading outcomes. Most of the tasks that usually involve a verbal response were switched to receptive tasks to demand a touch-screen response only. The tablet application was administered to a sample of N = 616 5-yo kindergarten children and to a sub-sample of these children twice during the following two years (First and Second Grades). Applying logistic regression and cross-validation, we selected a reduced subset of tasks that can predict with great sensitivity and specificity, whether a five-year-old child will have reading difficulties by the end of first grade (sensitivity 90% and specificity 76%) and two years later (sensitivity 90% and specificity 61%). Importantly, Lexiland is a scalable tool to implement universal screening, given the increasing availability of devices able to run android and iOS applications.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was funded by ANII FSED_2_2015_1_120741 and ANII FSED_2_2016_1_131230 grants to Juan Valle-Lisboa and Manuel Carreiras. Camila Zugarramurdi received a PhD Scholarship from Fundación Carolin

Current Understanding, Support Systems, and Technology-led Interventions for Specific Learning Difficulties

In January 2019, the Government Office for Science commissioned a series of 4 rapid evidence reviews to explore how technology and research can help improve educational outcomes for learners with Specific Learning Difficulties (SpLDs). This review examined: 1) current understanding of the causes and identification of SpLDs, 2)the support system for learners with SpLDs, 3)technology-based interventions for SpLDs 4) a case study approach focusing on dyscalculia to explore all 3 theme

Screening Young Children at Risk for Reading Failure

Reading and reading difficulties are some of the most researched topics in the literature in regard to psychology and education. Additionally, some specific subjects such as prediction and prevention attract research interest as well. These issues are discussed in the present chapter that focused on the screening measures and their characteristics towards significance and effectiveness. More specifically, discrimination accuracy, sensitivity, and specificity as well as validity and reliability were taken into consideration. Some well-known studies were examined revealing a range of methodological issues, which affected the effectiveness of using measures in the extant research. Although the findings were consistent with literature, they continued to be scant and not widely accepted, affected by several limitations regarding the sampling and the experimental design

Inclusive Intelligent Learning Management System Framework

Machado, D. S-M., & Santos, V. (2023). Inclusive Intelligent Learning Management System Framework. International Journal of Automation and Smart Technology, 13(1), [2423]. https://doi.org/10.5875/ausmt.v13i1.2423The article finds context and the current state of the art in a systematic literature review on intelligent systems employing PRISMA Methodology which is complemented with narrative literature review on disabilities, digital accessibility and legal and standards context. The main conclusion from this review was the existing gap between the available knowledge, standards, and law and what is put into practice in higher education institutions in Portugal. Design Science Research Methodology was applied to output an Inclusive Intelligent Learning Management System Framework aiming to help higher education professors to share accessible pedagogic content and deliver on-line and presential classes with a high level of accessibility for students with different types of disabilities, assessing the uploaded content with Web content Accessibility Guidelines 3.0, clustering students according to their profile, conscient feedback and emotional assessment during content consumption, applying predictive models and signaling students at risk of failing classes according to study habits and finally applying a recommender system. The framework was validated by a focus group to which experts in digital accessibility, information systems and a disabled PhD graduate.publishersversionpublishe

Multisensory learning in adaptive interactive systems

The main purpose of my work is to investigate multisensory perceptual learning and sensory integration in the design and development of adaptive user interfaces for educational purposes. To this aim, starting from renewed understanding from neuroscience and cognitive science on multisensory perceptual learning and sensory integration, I developed a theoretical computational model for designing multimodal learning technologies that take into account these results. Main theoretical foundations of my research are multisensory perceptual learning theories and the research on sensory processing and integration, embodied cognition theories, computational models of non-verbal and emotion communication in full-body movement, and human-computer interaction models. Finally, a computational model was applied in two case studies, based on two EU ICT-H2020 Projects, "weDRAW" and "TELMI", on which I worked during the PhD