情感学习中基于检测眨眼频率和贝叶斯网络的情感分类算法

Emotion Classification Algorithm Based on Blink Frequency Detection and Bayesian Network in Affective Learning

针对情感学习中常用情感状态识别方法的局限性,研究了一种通过检测学习者眨眼频率来对学习过程中产生的情感状态进行分类的方法。该方法首先根据学习者的眨眼频率将其情感状态分为正向情感或者负向情感,然后再通过贝叶斯网络根据学生信息及教学活动的上下文信息将负向情感进一步解析为具体的负向情感状态。将该分类算法应用到一个以教学视频为主体学习资源的电子学习平台,以验证其有效性。

This paper explored an emotional state classification algorithm through detecting the learner＇s blink frequen- cy during a learning process. This algorithm could classify the learner＇s emotional states into positive emotional state and negative emotional state. And the negative emotional categories could be inferred by a Bayesian network using the parameters about the student＇s personal information and the learning context information. An e-learning system using instructional videos as the core learning material was developed to validate this classification algorithm.