DysVis: A User-Centred Data Visualization System for Dyslexia Pre-screening

要旨

Dyslexia is a common neurobiological learning disorder significantly impacting reading, writing, and spelling worldwide. Early identification and intervention are essential, but most pre-screening tools focus on Latin languages, leaving Chinese-speaking students underserved. To address this gap, we conduct semi-structured interviews with special education (special-ed) teachers to gather their needs for dyslexia pre-screening tailored to Chinese contexts. Us- ing their insights, we have developed DysVis, a user-centered data visualization system that combines handwriting analysis, body movement keypoint conversion, and a comprehensive visualization interface. DysVis provides teachers with multi-level visualizations, such as performance overviews, task analyses, handwriting observations, and behavioural insights, enabling them to identify the root causes of learning difficulties. Our evaluations, including case studies, a user study, and expert interviews, demonstrate that DysVis is user-friendly and effective in quickly identifying at-risk students, ultimately enhancing learning outcomes for Chinese-speaking students with dyslexia.

著者
Ka Yan Fung
Hong Kong University of Science and Technology, Hong Kong, China
Lik-Hang Lee
Hong Kong Polytechnic University, Hong Kong, Hong Kong
Lin-Ping Yuan
The Hong Kong University of Science and Technology, Hong Kong, China
Kwong Chiu Fung
Hong Kong University of Science and Technology, Hong Kong, China
Sin Kuen Fung
The Education University of Hong Kong, Hong Kong, China
Lui Tze Leung
The Education University of Hong Kong, Hong Kong, China
QU Huamin
Hong Kong University of Science and Technology, Hong Kong, China
Shenghui Song
Hong Kong University of Science and Technology, Hong Kong, China
DOI

10.1145/3706598.3713194

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713194

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Technology for People

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6 件の発表
2025-04-29 01:20:00
2025-04-29 02:50:00
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