The Design and Implementation of the Full-time Mentality Detection System in Distance Learning

• Main Authors: Pei-Xun Tsai, 蔡沛勳
• Other Authors: Kuo-An Hwang
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/2rc5k6

碩士 === 朝陽科技大學 === 網路與通訊研究所 === 94 === Distance learning is one of the teaching modes in some universities. The advantage is that students could study in anytime and anywhere. Students could get the points under the form, but there are some drawbacks. The learning performance is determined by oneself. Especially the different learning environments maybe result to the negative learning effects. (e.g., The appearances of the drowsiness and inattention.) The motivation of the research is to provide a full-time detection system for monitoring the learning process. We will combine the image processing with recognition technology for the system core. The features of the facial expression and user operating behavior are the important evidences. We will collect those evidences and build a Bayesian Networks Model for inferring whether the student is concentrated. In our experiment, the detecting rates of the features of expression and behavior are well. To inferring the mentality according to Bayesian Networks the results will near the actual personal mentality.