Ka-Yan Fung;Kit-Yi Tang;Tze Leung Rick Lui;Kuen-Fung Sin;Lik-Hang Lee;Huamin Qu;Shenghui Song
{"title":"ADPS - 有阅读障碍学生学习繁体中文的预检工具","authors":"Ka-Yan Fung;Kit-Yi Tang;Tze Leung Rick Lui;Kuen-Fung Sin;Lik-Hang Lee;Huamin Qu;Shenghui Song","doi":"10.1109/TLT.2024.3384290","DOIUrl":null,"url":null,"abstract":"Prescreening children for specific learning disabilities, e.g., dyslexia, is essential for effective intervention. With a quick and reliable prescreening result, special education coordinators (SENCOs) can provide students with early intervention and relieve their learning pressure. Unfortunately, due to the limited resources, many students in Hong Kong receive dyslexia assessments beyond the golden period, i.e., under the age of six. To this end, information technology could establish automatic prescreening tools to address this issue. However, dyslexia prescreening for children learning Chinese is challenging due to the lack of sound–script correlation in Chinese. In this article, an automatic dyslexia prescreening system (ADPS) is developed to provide a quick test to identify at-risk children. Through a two-stage approach, we first develop a gamified tool based on linguistic characteristics and then evaluate the result by a comparison study. Results from a pilot test on 30 students with dyslexia and 32 students without dyslexia indicate that the ADPS can effectively distinguish between two groups of students. Furthermore, the interactive design elements can motivate students to conduct the prescreening independently.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1454-1470"},"PeriodicalIF":2.9000,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ADPS—A Prescreening Tool for Students With Dyslexia in Learning Traditional Chinese\",\"authors\":\"Ka-Yan Fung;Kit-Yi Tang;Tze Leung Rick Lui;Kuen-Fung Sin;Lik-Hang Lee;Huamin Qu;Shenghui Song\",\"doi\":\"10.1109/TLT.2024.3384290\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Prescreening children for specific learning disabilities, e.g., dyslexia, is essential for effective intervention. With a quick and reliable prescreening result, special education coordinators (SENCOs) can provide students with early intervention and relieve their learning pressure. Unfortunately, due to the limited resources, many students in Hong Kong receive dyslexia assessments beyond the golden period, i.e., under the age of six. To this end, information technology could establish automatic prescreening tools to address this issue. However, dyslexia prescreening for children learning Chinese is challenging due to the lack of sound–script correlation in Chinese. In this article, an automatic dyslexia prescreening system (ADPS) is developed to provide a quick test to identify at-risk children. Through a two-stage approach, we first develop a gamified tool based on linguistic characteristics and then evaluate the result by a comparison study. Results from a pilot test on 30 students with dyslexia and 32 students without dyslexia indicate that the ADPS can effectively distinguish between two groups of students. Furthermore, the interactive design elements can motivate students to conduct the prescreening independently.\",\"PeriodicalId\":49191,\"journal\":{\"name\":\"IEEE Transactions on Learning Technologies\",\"volume\":\"17 \",\"pages\":\"1454-1470\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-04-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Learning Technologies\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10488748/\",\"RegionNum\":3,\"RegionCategory\":\"教育学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Learning Technologies","FirstCategoryId":"95","ListUrlMain":"https://ieeexplore.ieee.org/document/10488748/","RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
ADPS—A Prescreening Tool for Students With Dyslexia in Learning Traditional Chinese
Prescreening children for specific learning disabilities, e.g., dyslexia, is essential for effective intervention. With a quick and reliable prescreening result, special education coordinators (SENCOs) can provide students with early intervention and relieve their learning pressure. Unfortunately, due to the limited resources, many students in Hong Kong receive dyslexia assessments beyond the golden period, i.e., under the age of six. To this end, information technology could establish automatic prescreening tools to address this issue. However, dyslexia prescreening for children learning Chinese is challenging due to the lack of sound–script correlation in Chinese. In this article, an automatic dyslexia prescreening system (ADPS) is developed to provide a quick test to identify at-risk children. Through a two-stage approach, we first develop a gamified tool based on linguistic characteristics and then evaluate the result by a comparison study. Results from a pilot test on 30 students with dyslexia and 32 students without dyslexia indicate that the ADPS can effectively distinguish between two groups of students. Furthermore, the interactive design elements can motivate students to conduct the prescreening independently.
期刊介绍:
The IEEE Transactions on Learning Technologies covers all advances in learning technologies and their applications, including but not limited to the following topics: innovative online learning systems; intelligent tutors; educational games; simulation systems for education and training; collaborative learning tools; learning with mobile devices; wearable devices and interfaces for learning; personalized and adaptive learning systems; tools for formative and summative assessment; tools for learning analytics and educational data mining; ontologies for learning systems; standards and web services that support learning; authoring tools for learning materials; computer support for peer tutoring; learning via computer-mediated inquiry, field, and lab work; social learning techniques; social networks and infrastructures for learning and knowledge sharing; and creation and management of learning objects.