DYSL-X: Design of a tablet game for early risk detection of dyslexia in preschoolers

Audenaeren, Lieven Van den; Celis, Véronique; Abeele, Vero Vanden; Geurts, Luc; Husson, Jelle; Ghesquière, Pol; Wouters, Jan; Loyez, Leen; Goeleven, Ann

doi:10.1007/978-3-658-02897-8_20

Cited by 28 publications

(15 citation statements)

References 12 publications

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“…First, Gaggi et al [12] tested a game with 24 pre-schoolers in Italy, that aimed at eye-hand coordination, visual spatial attention, rapid speechsound identification and discrimination as well as visual-tospeech sound. Second, Van den Audenaeren et al [32] performed a user study with 20 pre-schoolers in Flanders and are currently developing the game DYSL-X for early risk detection of dyslexia, which includes letter and end-phoneme recognition as well as psycho-acoustical tests.…”

Section: Related Workmentioning

confidence: 99%

Dytective: Diagnosing Risk of Dyslexia with a Game

Rello

Ballesteros

Ali

et al. 2016

Proceedings of the 10th EAI International Conference on Pervasive Computing Technologies for Healthcare

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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 scalable early detection via machine learning models that predict reading and writing difficulties by watching how people interact with a linguistic web-based game: Dytective. The design of Dytective is based on (i) the empirical linguistic analysis of the errors that people with dyslexia make, (ii) principles of language acquisition, and (iii) specific linguistic skills related to dyslexia. Experiments with 243 children and adults (95 with diagnosed dyslexia) revealed differences in how people with dyslexia read and write. We trained a machine learning model that was able to predict dyslexia with 83% accuracy in a held-out test set with 100 participants. Currently, we are working with schools to put our approach into practice at scale to reduce school failure as a primary way dyslexia is diagnosed.

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Section: Related Workmentioning

confidence: 99%

Dytective: Diagnosing Risk of Dyslexia with a Game

Rello

Ballesteros

Ali

et al. 2016

Proceedings of the 10th EAI International Conference on Pervasive Computing Technologies for Healthcare

View full text Add to dashboard Cite

show abstract

“…The greatest accuracy was 100%, implementing Bayes Net as the classifier and CFS as a feature selection algorithm. Nevertheless, there was much scope for additional research looking at further features of this tool by inspecting students' learning profiles [19].…”

Section: Dyslexia Screening At Preschool Stagementioning

confidence: 99%

ICTs as a Distinct Detection Approach for Dyslexia Screening: A Contemporary View

Drigas

Politi-Georgousi

2019

Int. J. Onl. Eng.

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<span>In contemporary times, various studies have been conducted around the world, researching the use of Information and Communication Technologies (ICTs) on dyslexia screening in all ages. Screening procedure constitutes the first vital step to investigate the kind of these experiencing difficulties, which sets the foundations for a well-designed intervention. Thus, ICTs is becoming an emerging unique supplementary strategy, using cost-effective and easy to access advanced tests for gaining in-depth knowledge about students’ learning profiles. The purpose of this literature review is to present an indicative number of digital assessment tools (e.g. computerized systems, software, applications etc.) for detecting dyslexia, that have been designed or have been implemented on dyslexic population during the last thirteen years (2005-2018). The classification of proposed papers has been carried out according to the life stage, each of whom analyzed.</span>

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“…According to Geurts et. in their research, a normal computer-based learning with interactive user interface and added animations is still difficult to keep the child's attention at a high level throughout the learning [3]. Therefore, by incorporating fun and challenging application such as a game-based learning would increase dyslexic students' motivation to continue learning.…”

Section: Related Workmentioning

confidence: 99%

User Interface Guidelines for Dyslexic Game-Based Learning on Selected Usability Test Method

Sulaiman¹

2019

IJATCSE

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Multimedia technology is constantly growing and benefitted every facet of our lives and has its impact felt to modern education. Despite many products that utilize multimedia technology for education such as e-Learning and learning courseware, there is a lack of development in the designs of educational products for students with Dyslexia. User Centred Design guidelines on developing software for Dyslexic user is still under investigation and most published guidelines are focusing more on web accessibility and not specific for game-based learning. This paper presents a set of practical interface design guidelines to improve learning process of a game-based learning for the dyslexic students. The guidelines are developed based on survey conducted and proposed elements in Human Computer Interaction (HCI) that is essential in improving learning process. Within the paper, we showed that using of the proposed guidelines can improved the learning process for Dyslexia students.

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DYSL-X: Design of a tablet game for early risk detection of dyslexia in preschoolers

Cited by 28 publications

References 12 publications

Dytective: Diagnosing Risk of Dyslexia with a Game

Dytective: Diagnosing Risk of Dyslexia with a Game

ICTs as a Distinct Detection Approach for Dyslexia Screening: A Contemporary View

User Interface Guidelines for Dyslexic Game-Based Learning on Selected Usability Test Method

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