{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T05:08:52Z","timestamp":1667538532712},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643683386","type":"print"},{"value":"9781643683393","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T00:00:00Z","timestamp":1667174400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,31]]},"abstract":"The unprecedented long-term online learning caused by COVID-19 has increased stress symptoms among students. The e-learning system reduces communications between teachers and students, making it difficult to observe student\u2019s mental issues and learning performance. This study aims to develop a non-intrusive method that can simultaneously monitor stress states and cognitive performance of student in the scenario of online education. Forty-three participants were recruited to perform a computer-based reading task under stressful and non-stressful conditions, and their eye-movement data were recorded. A tree ensemble machine learning model, named LightGBM (Light Gradient Boosting Machine), was utilized to predict stress states and reading performance of students with an accuracy of 0.825 and 0.793, respectively. An interpretable model, SHAP (SHapley Additive exPlanation), was used to identify the most important eye-movement indicators and their effects on stress and reading performance. The proposed model can serve as a foundation for further development of user-centred services in e-learning system.<\/jats:p>","DOI":"10.3233\/atde220697","type":"book-chapter","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T09:35:45Z","timestamp":1667468145000},"source":"Crossref","is-referenced-by-count":0,"title":["Mental States and Cognitive Performance Monitoring for User-Centered e-Learning System: A Case Study"],"prefix":"10.3233","author":[{"given":"Ziqing","family":"Xia","sequence":"first","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore"}]},{"given":"Cherng En","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore"}]},{"given":"Chun-Hsien","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore"}]},{"given":"Jo-Yu","family":"Kuo","sequence":"additional","affiliation":[{"name":"Department of Industrial Design, National Taipei University of Technology, Taipei City, 10608 Taiwan"}]},{"given":"Kendrik Yan Hong","family":"Lim","sequence":"additional","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore"}]}],"member":"7437","container-title":["Advances in Transdisciplinary Engineering","Transdisciplinarity and the Future of Engineering"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/ATDE220697","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T09:35:46Z","timestamp":1667468146000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/ATDE220697"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,31]]},"ISBN":["9781643683386","9781643683393"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/atde220697","relation":{},"ISSN":["2352-751X","2352-7528"],"issn-type":[{"value":"2352-751X","type":"print"},{"value":"2352-7528","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,31]]}}}