Real-Time Prediction of Science Student Learning Outcomes Using Machine Learning Classification of Hemodynamics During Virtual Reality and Online Learning Sessions
| dc.contributor.author | Lamb, Richard | |
| dc.contributor.author | Knut Neumann, Knut | |
| dc.contributor.author | Linder, Kayleigh A. | |
| dc.date.accessioned | 2022-11-15T17:58:11Z | |
| dc.date.available | 2022-11-15T17:58:11Z | |
| dc.date.issued | 2022 | |
| dc.identifier.doi | 10.1016/j.caeai.2022.100078 | |
| dc.identifier.issn | 2666-920X | |
| dc.identifier.uri | http://hdl.handle.net/10342/11744 | |
| dc.language.iso | en_US | en_US |
| dc.subject | Science student learning | en_US |
| dc.subject | Functional near infrared spectrometry (fNIRS) | en_US |
| dc.subject | Student prediction | en_US |
| dc.title | Real-Time Prediction of Science Student Learning Outcomes Using Machine Learning Classification of Hemodynamics During Virtual Reality and Online Learning Sessions | en_US |
| dc.type | Article | en_US |
| ecu.journal.name | Computers and Education: Artificial Intelligence | en_US |
| ecu.journal.volume | 3 | en_US |
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