{"title":"Adaptive E-Learning and Dyslexia: an Empirical Evaluation and Recommendations for Future Work","authors":"W. Alghabban, R. Hendley","doi":"10.1093/iwc/iwad036","DOIUrl":null,"url":null,"abstract":"\n Adaptive e-learning is becoming increasingly popular as a tool to help learners with dyslexia. It provides more customized learning experiences based on the learners’ characteristics. Each learner with dyslexia has unique characteristics for which material should ideally be suitably tailored. However, adaptation to the characteristics of learners with dyslexia—in particular, their dyslexia type and reading skill level—is limited. By examining the learning effectiveness of adaptation of learning material based on the learner’s type of dyslexia and reading skill, this study fills a knowledge vacuum in this under-researched area. An empirical evaluation through a controlled experiment with 47 Arabic subjects has been undertaken and assessed using the following metrics: learning gain and learner satisfaction. The findings reveal that adapting learning material to the combination of dyslexia type and reading skill level yields significantly better short- and long-term learning gains and improves the learners’ satisfaction compared to non-adapted material. There is evidence that this benefit also extends to how well learners read unseen material. This paper also discusses implications and important avenues for future research and practice related to how adaptation influences learners with dyslexia.","PeriodicalId":50354,"journal":{"name":"Interacting with Computers","volume":" ","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Interacting with Computers","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1093/iwc/iwad036","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}
引用次数: 0
Abstract
Adaptive e-learning is becoming increasingly popular as a tool to help learners with dyslexia. It provides more customized learning experiences based on the learners’ characteristics. Each learner with dyslexia has unique characteristics for which material should ideally be suitably tailored. However, adaptation to the characteristics of learners with dyslexia—in particular, their dyslexia type and reading skill level—is limited. By examining the learning effectiveness of adaptation of learning material based on the learner’s type of dyslexia and reading skill, this study fills a knowledge vacuum in this under-researched area. An empirical evaluation through a controlled experiment with 47 Arabic subjects has been undertaken and assessed using the following metrics: learning gain and learner satisfaction. The findings reveal that adapting learning material to the combination of dyslexia type and reading skill level yields significantly better short- and long-term learning gains and improves the learners’ satisfaction compared to non-adapted material. There is evidence that this benefit also extends to how well learners read unseen material. This paper also discusses implications and important avenues for future research and practice related to how adaptation influences learners with dyslexia.
期刊介绍:
Interacting with Computers: The Interdisciplinary Journal of Human-Computer Interaction, is an official publication of BCS, The Chartered Institute for IT and the Interaction Specialist Group .
Interacting with Computers (IwC) was launched in 1987 by interaction to provide access to the results of research in the field of Human-Computer Interaction (HCI) - an increasingly crucial discipline within the Computer, Information, and Design Sciences. Now one of the most highly rated journals in the field, IwC has a strong and growing Impact Factor, and a high ranking and excellent indices (h-index, SNIP, SJR).