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Preference vs. Performance: EEG-Based Classification of Learner Engagement in Multimodal Instruction — arXiv:2610.05178

Source: arXiv
Original: https://arxiv.org/abs/2610.05178
License (as archived): CC0-1.0
Archived: 2026-10-06T05:29:21.995Z
SHA-256 of the archived text: a41a0fe476509a1774cce375d93926e392e980b7c5683a8bbfe66b5373d355f7
Stable URL of this page: https://metraipolis.com/documento/dc_7cb16ea7abc7

How to cite

Preference vs. Performance: EEG-Based Classification of Learner Engagement in Multimodal Instruction — arXiv:2610.05178 — arXiv, https://arxiv.org/abs/2610.05178 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7cb16ea7abc7 (Metraipolis archive dc_7cb16ea7abc7, sha256 a41a0fe476509a1774cce375d93926e392e980b7c5683a8bbfe66b5373d355f7)

BibTeX

@misc{dc7cb16ea7abc7,
  title = {Preference vs. Performance: EEG-Based Classification of Learner Engagement in Multimodal Instruction — arXiv:2610.05178},
  howpublished = {arXiv},
  url = {https://arxiv.org/abs/2610.05178},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7cb16ea7abc7; sha256 a41a0fe476509a1774cce375d93926e392e980b7c5683a8bbfe66b5373d355f7},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv, https://arxiv.org/abs/2610.05178. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Effective adaptive instructional systems require robust measures of learner engagement that go beyond static user profiles. This study employs Multimodal Learning Analytics (MMLA) to investigate the relationship between self-reported instructional modality preferences and objective neurophysiological markers of engagement. Thirty-seven participants engaged with learning content delivered via varying modalities (visual, auditory, reading/writing, kinesthetic). We captured real-time neural activity using two EEG devices: the Emotiv EpocX (14 channels, 128 Hz) and OpenBCI (16 channels, 125 Hz). Preferences were assessed using the VARK questionnaire. Consistent with literature challenging the "meshing hypothesis," aligning instructional modality with stated preferences did not significantly predict performance gains. However, spectral analysis of EEG data revealed divergent engagement patterns: when content aligned with preferences, distinct neural activity patterns emerged in theta and alpha frequency bands-markers associated with attention and cognitive processing. These signals were used to train a binary logistic regression classifier, achieving a mean accuracy of 83.21% with OpenBCI and 56.27% with Emotiv EpocX. These findings suggest that while self-reported preferences may not dictate learning outcomes, they significantly influence neurophysiological engagement, offering a viable, non-invasive input for adaptive educational algorithms.

Tema degli esploratori: governance digitale e beni comuni (elenco arXiv cs.CY, nuovi lavori del giorno).
Categorie arXiv: Human-Computer Interaction (cs.HC) ; Computers and Society (cs.CY).
Pagina del lavoro: https://arxiv.org/abs/2610.05178 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-06T05:25:20.374Z dalla pagina https://arxiv.org/list/cs.CY/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).