EEG Transformer for Classifying Students’ Epistemic Cognition States in Educational Contexts
Epistemic cognition encompasses the processes by which individuals achieve knowledge and understanding and is pivotal in human learning, typically in educational settings. Despite its importance, epistemic cognition is often studied using traditional educational research methods such as questionnair...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10870110/ |
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| author | Rosanna Yuen-Yan Chan |
| author_facet | Rosanna Yuen-Yan Chan |
| author_sort | Rosanna Yuen-Yan Chan |
| collection | DOAJ |
| description | Epistemic cognition encompasses the processes by which individuals achieve knowledge and understanding and is pivotal in human learning, typically in educational settings. Despite its importance, epistemic cognition is often studied using traditional educational research methods such as questionnaires and interviews. However, these methods primarily rely on self-reporting and retrospective accounts, which may not effectively capture the dynamic, situational, and often implicit nature of epistemic cognition as it unfolds in real-time learning environments. This study addresses this limitation by employing transformers and publicly available electroencephalography (EEG) datasets to classify students’ epistemic cognition states. For the first time, we demonstrate that transformers, enhanced with educational domain-specific preprocessing and categorical embeddings, achieve state-of-the-art performance in classifying epistemic cognition across diverse contexts. Our model achieved an accuracy of 99.63% and 99.65% for distance learning and online understanding tasks, respectively, outperforming existing transformers in cognition and metacognition studies. Grounded in the Learning Sciences, which emphasize an interdisciplinary and scientific approach to understanding human learning, this applied research demonstrates the transformative potential of integrating artificial intelligence with education and neuroscience to generate actionable insights into educational practices. |
| format | Article |
| id | doaj-art-e10c1a1ecbc046b4afa3d9715bdc9b04 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e10c1a1ecbc046b4afa3d9715bdc9b042025-02-11T00:01:22ZengIEEEIEEE Access2169-35362025-01-0113239352394910.1109/ACCESS.2025.353803610870110EEG Transformer for Classifying Students’ Epistemic Cognition States in Educational ContextsRosanna Yuen-Yan Chan0https://orcid.org/0000-0002-5345-6832Centre for Perceptual and Interactive Intelligence, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAREpistemic cognition encompasses the processes by which individuals achieve knowledge and understanding and is pivotal in human learning, typically in educational settings. Despite its importance, epistemic cognition is often studied using traditional educational research methods such as questionnaires and interviews. However, these methods primarily rely on self-reporting and retrospective accounts, which may not effectively capture the dynamic, situational, and often implicit nature of epistemic cognition as it unfolds in real-time learning environments. This study addresses this limitation by employing transformers and publicly available electroencephalography (EEG) datasets to classify students’ epistemic cognition states. For the first time, we demonstrate that transformers, enhanced with educational domain-specific preprocessing and categorical embeddings, achieve state-of-the-art performance in classifying epistemic cognition across diverse contexts. Our model achieved an accuracy of 99.63% and 99.65% for distance learning and online understanding tasks, respectively, outperforming existing transformers in cognition and metacognition studies. Grounded in the Learning Sciences, which emphasize an interdisciplinary and scientific approach to understanding human learning, this applied research demonstrates the transformative potential of integrating artificial intelligence with education and neuroscience to generate actionable insights into educational practices.https://ieeexplore.ieee.org/document/10870110/Epistemic cognitionelectroencephalography (EEG)transformersartificial intelligence |
| spellingShingle | Rosanna Yuen-Yan Chan EEG Transformer for Classifying Students’ Epistemic Cognition States in Educational Contexts IEEE Access Epistemic cognition electroencephalography (EEG) transformers artificial intelligence |
| title | EEG Transformer for Classifying Students’ Epistemic Cognition States in Educational Contexts |
| title_full | EEG Transformer for Classifying Students’ Epistemic Cognition States in Educational Contexts |
| title_fullStr | EEG Transformer for Classifying Students’ Epistemic Cognition States in Educational Contexts |
| title_full_unstemmed | EEG Transformer for Classifying Students’ Epistemic Cognition States in Educational Contexts |
| title_short | EEG Transformer for Classifying Students’ Epistemic Cognition States in Educational Contexts |
| title_sort | eeg transformer for classifying students x2019 epistemic cognition states in educational contexts |
| topic | Epistemic cognition electroencephalography (EEG) transformers artificial intelligence |
| url | https://ieeexplore.ieee.org/document/10870110/ |
| work_keys_str_mv | AT rosannayuenyanchan eegtransformerforclassifyingstudentsx2019epistemiccognitionstatesineducationalcontexts |