发表机构
Indiana University; Michigan State University（印第安纳大学; 密歇根州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过脑电数据发现，学习者自报模态偏好虽不预测学习表现，但显著影响神经生理参与度，并据此训练分类器（OpenBCI准确率83.21%）为自适应教育提供非侵入性输入。
AI 中文摘要
有效的自适应教学系统需要超越静态用户画像的稳健学习者参与度测量。本研究采用多模态学习分析（MMLA）来探究自我报告的教学模态偏好与客观的神经生理参与度标记之间的关系。三十七名参与者通过不同模态（视觉、听觉、阅读/写作、动觉）呈现的学习内容进行学习。我们使用两种脑电设备捕获实时神经活动：Emotiv EpocX（14通道，128赫兹）和OpenBCI（16通道，125赫兹）。偏好通过VARK问卷进行评估。与挑战“网格假说”的文献一致，将教学模态与陈述偏好对齐并未显著预测表现提升。然而，脑电数据的频谱分析揭示了不同的参与模式：当内容与偏好对齐时，在θ和α频带中出现了不同的神经活动模式——这些是与注意力和认知处理相关的标记。这些信号被用于训练二元逻辑回归分类器，在OpenBCI上实现了83.21%的平均准确率，在Emotiv EpocX上实现了56.27%的平均准确率。这些发现表明，虽然自我报告的偏好可能不决定学习结果，但它们显著影响神经生理参与度，为自适应教育算法提供了一种可行的、非侵入性的输入。
英文摘要
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.
CommentsPresented at The 26th IEEE International Conference on Advanced Learning Technologies (ICALT) 2026, July 6-9, 2026 at Hung Yen, Vietnam
