{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:34:11Z","timestamp":1775666051006,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T00:00:00Z","timestamp":1749168000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100019564","name":"National Institute of Education Research","doi-asserted-by":"publisher","award":["RS 1\/22 CWL"],"award-info":[{"award-number":["RS 1\/22 CWL"]}],"id":[{"id":"10.13039\/501100019564","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019564","name":"National Institute of Education Research","doi-asserted-by":"publisher","award":["IRB-2021-210"],"award-info":[{"award-number":["IRB-2021-210"]}],"id":[{"id":"10.13039\/501100019564","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Institute of Education (NIE), Nanyang Technological University (NTU)","award":["RS 1\/22 CWL"],"award-info":[{"award-number":["RS 1\/22 CWL"]}]},{"name":"National Institute of Education (NIE), Nanyang Technological University (NTU)","award":["IRB-2021-210"],"award-info":[{"award-number":["IRB-2021-210"]}]},{"name":"NTU IRB","award":["RS 1\/22 CWL"],"award-info":[{"award-number":["RS 1\/22 CWL"]}]},{"name":"NTU IRB","award":["IRB-2021-210"],"award-info":[{"award-number":["IRB-2021-210"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Effectively leveraging cognitive load predictions helps optimize collaborative learning design and implementation. This study explored the feasibility of predicting individual learners\u2019 cognitive load during collaborative learning using a combination of functional near-infrared spectroscopy (fNIRS) and eye-tracking data. A total of 188 valid collaborative events collected from 78 graduate students who engaged in three collaborative ideation tasks were analyzed using various machine learning algorithms applied to classify cognitive load levels. Nine features, derived from both fNIRS and eye-tracking data, were used as input for the models. Results demonstrated that machine learning models could accurately predict individual cognitive load, with the Random Forest model achieving the highest performance (F1 score = 0.84). Furthermore, the integration of fNIRS and eye-tracking data significantly enhanced predictive performance, with the multimodal model achieving an F1 score 0.87\u2014outperforming the eye-tracking-only model (F1 = 0.79) by 8% and the fNIRS-only model (F1 = 0.68) by 19%. Analysis of feature importance revealed that \u201cTotal Fixation Duration\u201d, \u201cAverage Inter-Fixation Degree\u201d, and prefrontal cortex activity were among the strongest predictors of learners\u2019 cognitive load. These findings have implications for understanding cognitive load dynamics and designing effective collaborative learning environments and human\u2013computer interfaces.<\/jats:p>","DOI":"10.3390\/make7020051","type":"journal-article","created":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T09:02:03Z","timestamp":1749200523000},"page":"51","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Machine Learning Models to Predict Individual Cognitive Load in Collaborative Learning: Combining fNIRS and Eye-Tracking Data"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3196-3400","authenticated-orcid":false,"given":"Wenli","family":"Chen","sequence":"first","affiliation":[{"name":"National Institute of Education, Nanyang Technological University, Singapore 637616, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zirou","family":"Lin","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence in Education, South China Normal University, Guangzhou 510631, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lishan","family":"Zheng","sequence":"additional","affiliation":[{"name":"National Institute of Education, Nanyang Technological University, Singapore 637616, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8818-2440","authenticated-orcid":false,"given":"Mei-Yee Mavis","family":"Ho","sequence":"additional","affiliation":[{"name":"National Institute of Education, Nanyang Technological University, Singapore 637616, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6773-893X","authenticated-orcid":false,"given":"Farhan","family":"Ali","sequence":"additional","affiliation":[{"name":"National Institute of Education, Nanyang Technological University, Singapore 637616, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3929-9778","authenticated-orcid":false,"given":"Wei Peng","family":"Teo","sequence":"additional","affiliation":[{"name":"National Institute of Education, Nanyang Technological University, Singapore 637616, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1207\/s15516709cog1202_4","article-title":"Cognitive Load During Problem Solving: Effects on Learning","volume":"12","author":"Sweller","year":"1988","journal-title":"Cogn. 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