基于神经网络的学习状态检测

Detection of the Learning State Based on Neural Network

对在线学习者注意力状态检测的方法大多基于眼睛闭合频率、头部偏转等特征,此类方法能够应对大多数情况,但针对学习者正视屏幕且视线落点处于屏幕上时出现的发呆、分神状态无法作出检测。针对此问题,提出了一种基于RNN的眼动分析算法RNN-EMA(RNN-EyeMovementAnalysis),该算法通过对序列眼动向量分析,预测学生学习行为,完成当前学习状态检测。实验表明,RNN-EMA算法能够对学习状态作出有效检测,且对比同类方法效果有所提升。

Online learners’ attention states are mostly detected through eye closure frequency, head rotation and other action features. These methods can cope with most situations, but cannot detect the absent-minded and distracted state when the learner is facing the screen and the sight point is on the screen. To solve this problem, the paper proposes an RNN-EMA(RNN-Eye Movement Analysis) algorithm based on RNN. The algorithm predicts the learning behavior of students through sequential eye movement vector analysis, and conducts the current learning state detection. Experiments show that the RNNEMA algorithm can effectively detect the learning state, and the accuracy is improved compared with other methods of the same kind.