用脑电数据预测学习努力度，助力个性化教学优化
Hybrid Deep Learning Model to Estimate Cognitive Effort from fNIRS Signals
- 融合CNN与GRU的深度模型分析脑血氧信号
- 模型准确率达73%，可有效捕捉认知努力趋势
- 适合教育科技、脑机接口领域研究者参考
本研究基于功能近红外光谱（fNIRS）数据和答题表现分数，构建混合深度神经网络模型以估计认知努力。实验中，16名参与者在基于Unity的教育问答游戏中回答16道题，每题限时30秒，采集其氧合血红蛋白信号。通过深度网络模型从氧合血红蛋白预测答题得分，并对比传统机器学习与深度学习模型的预测性能。结果表明，所提出的CNN-GRU模型在得分预测上表现最优，准确率达73%。基于预测得分与氧合血红蛋白数据，进一步计算相对神经效率与参与度以评估认知努力。结果显示，尽管预测准确率中等，但认知努力趋势与真实情况高度吻合。该方法可为学习环境设计与教学材料优化提供重要参考。
原文摘要 · Abstract (English)
This study estimates cognitive effort based on functional near-infrared spectroscopy data and performance scores using a hybrid DeepNet model. The estimation of cognitive effort enables educators to modify material to enhance learning effectiveness and student engagement. In this study, we collected oxygenated hemoglobin using functional near-infrared spectroscopy during an educational quiz game. Participants (n=16) responded to 16 questions in a Unity-based educational game, each within a 30-second response time limit. We used DeepNet models to predict the performance score from the oxygenated hemoglobin, and compared traditional machine learning and DeepNet models to determine which approach provides better accuracy in predicting performance scores. The result shows that the proposed CNN-GRU gives better performance with 73% than other models. After the prediction, we used the predicted score and the oxygenated hemoglobin to observe cognitive effort by calculating relative neural efficiency and involvement in our test cases. Our result shows that even with moderate accuracy, the predicted cognitive effort closely follow the actual trends. This findings can be helpful in designing and improving learning environments and provide valuable insights into learning materials.
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