Fengfeng Ke, Ruohan Liu, Zlatko Sokolikj, Ibrahim Dahlstrom-Hakki, Maya Israel
{"title":"Using eye-tracking in education: review of empirical research and technology","authors":"Fengfeng Ke, Ruohan Liu, Zlatko Sokolikj, Ibrahim Dahlstrom-Hakki, Maya Israel","doi":"10.1007/s11423-024-10342-4","DOIUrl":null,"url":null,"abstract":"<p>This study aims to provide a systematic review of recent eye-tracking studies conducted with children and adolescents in learning settings, as well as a scoping review of the technologies and machine learning approaches used for eye-tracking. To this end, 68 empirical studies containing 78 experiments were analyzed. Eye-tracking devices as well as the ever-evolving mechanisms of gaze prediction endorsed in the prior and current research were identified. The review results indicated a set of salient patterns governing the employment of eye-tracking measures and the inferred cognitive constructs in learning, along with the common practices in analyzing and presenting the eye-tracking data. Eye-tracking has been used to track engagement, learning interactions, and learning-relevant cognitive activities mainly in a research lab or a highly-controlled learning setting. The mechanisms of gaze capturing and prediction with learners in a dynamic and authentic learning environment are evolving.</p>","PeriodicalId":501584,"journal":{"name":"Educational Technology Research and Development","volume":"168 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Educational Technology Research and Development","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s11423-024-10342-4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This study aims to provide a systematic review of recent eye-tracking studies conducted with children and adolescents in learning settings, as well as a scoping review of the technologies and machine learning approaches used for eye-tracking. To this end, 68 empirical studies containing 78 experiments were analyzed. Eye-tracking devices as well as the ever-evolving mechanisms of gaze prediction endorsed in the prior and current research were identified. The review results indicated a set of salient patterns governing the employment of eye-tracking measures and the inferred cognitive constructs in learning, along with the common practices in analyzing and presenting the eye-tracking data. Eye-tracking has been used to track engagement, learning interactions, and learning-relevant cognitive activities mainly in a research lab or a highly-controlled learning setting. The mechanisms of gaze capturing and prediction with learners in a dynamic and authentic learning environment are evolving.