1,937 research outputs found

    A Computer-Based Method to Improve the Spelling of Children with Dyslexia

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    In this paper we present a method which aims to improve the spelling of children with dyslexia through playful and targeted exercises. In contrast to previous approaches, our method does not use correct words or positive examples to follow, but presents the child a misspelled word as an exercise to solve. We created these training exercises on the basis of the linguistic knowledge extracted from the errors found in texts written by children with dyslexia. To test the effectiveness of this method in Spanish, we integrated the exercises in a game for iPad, DysEggxia (Piruletras in Spanish), and carried out a within-subject experiment. During eight weeks, 48 children played either DysEggxia or Word Search, which is another word game. We conducted tests and questionnaires at the beginning of the study, after four weeks when the games were switched, and at the end of the study. The children who played DysEggxia for four weeks in a row had significantly less writing errors in the tests that after playing Word Search for the same time. This provides evidence that error-based exercises presented in a tablet help children with dyslexia improve their spelling skills.Comment: 8 pages, ASSETS'14, October 20-22, 2014, Rochester, NY, US

    Screening dyslexia for English using HCI measures and machine learning

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    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

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    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

    Evaluating New Approaches of Intervention in Reading Difficulties in Students with Dyslexia: The ilearnRW Software Application

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    The aim of this paper is to increase knowledge and understanding on how the implementation of language content through specialized software, such as the “Integrated Intelligent Learning Environment for Reading and Writing-iLearnRW”, can enhance learning during intervention procedures to enhance reading skills for children with dyslexia.The iLearnRW software is a newly designed tool that makes use of innovative technology and provides individualized intervention through games that incorporate learning activities, addressing those language areas that are most challenging for children with dyslexia in a highly entertaining and motivating way. Individualized intervention is provided through an underlying user profile, which incorporates these language features and is constantly updated as the child uses the software playing games, presenting language material selected based on his difficulties and recording his progress. A group of 78 students (52 male, 26 female) diagnosed with dyslexia, aged between 9 and 11 years old, was assessed for phonological, morphological and vocabulary skills. The students logged in the iLearnRW software on a mean of 14.18 days over a six-month intervention. After the 6-month intervention, the students were assessed once again on the same skills so as to establish the tool’s effectiveness.The results’ analysis revealed the following: (i) there was a strong constructional linkage between the profile entries of the sample, the language content of the tasks of the screening test as well of the games and its effectiveness in the students’ performance; (ii) the students who received specific guidance by their teachers, obtained higher success rates in most of the games than the students without any guidance, and (iii) the quantity of the language content and the time playing were not correlated with the students’ performance in the software’s games. Keywords: Digital technology, assistive computer software, dyslexia, learning environmen

    Can children's instructional gameplay activity be used as a predictive indicator of reading skills?

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    For children who may face reading difficulties, early intervention is a societal priority. However, early intervention requires early detection. While much research has approached the issue of identification through measuring component skills at single timepoints, an alternative is the utilisation of dynamic assessment. To this point, few initiatives have explored the potential for identification through progress data from play in digital literacy games. This study explored how well growth curves from progress data in a digital intervention can predict reading performance after gameplay compared to measuring component skills at a single timepoint (school entry). 137 six-year-old students played the digital Graphogame for 25 weeks. Latent growth curve analyses showed that variation in trajectories explained variation in literacy performance to a greater extent than risk status at school entry. Findings point to a potential for non-intrusive reading assessment in the application of a serious digital game in first grade.publishedVersio

    Can children's instructional gameplay activity be used as a predictive indicator of reading skills?

    Get PDF
    For children who may face reading difficulties, early intervention is a societal priority. However, early intervention requires early detection. While much research has approached the issue of identification through measuring component skills at single timepoints, an alternative is the utilisation of dynamic assessment. To this point, few initiatives have explored the potential for identification through progress data from play in digital literacy games. This study explored how well growth curves from progress data in a digital intervention can predict reading performance after gameplay compared to measuring component skills at a single timepoint (school entry). 137 six-year-old students played the digital Graphogame for 25 weeks. Latent growth curve analyses showed that variation in trajectories explained variation in literacy performance to a greater extent than risk status at school entry. Findings point to a potential for non-intrusive reading assessment in the application of a serious digital game in first grade

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

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    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 FundaciĂłn Carolin

    Proceedings of the Salford Postgraduate Annual Research Conference (SPARC) 2011

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    These proceedings bring together a selection of papers from the 2011 Salford Postgraduate Annual Research Conference(SPARC). It includes papers from PhD students in the arts and social sciences, business, computing, science and engineering, education, environment, built environment and health sciences. Contributions from Salford researchers are published here alongside papers from students at the Universities of Anglia Ruskin, Birmingham City, Chester,De Montfort, Exeter, Leeds, Liverpool, Liverpool John Moores and Manchester

    Multisensory learning in adaptive interactive systems

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    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
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