Online Education Student Cognitive State Recognition Based on Improved Multi-task Convolutional Neural Network

Abstract

With the widespread application and development of internet technology in public education scenarios in China, the application of deep learning technology on online learning platforms is becoming increasingly widespread. This study aims to address the difficulty in determining students’ cognitive state under the current network learning mode, and proposes a face recognition algorithm for student cognitive state detection using multitask convolutional neural network image recognition technology. At the same time, research is conducted on extracting two-dimensional feature points of facial color images through cascading regression tree localization methods. In the practical application experiments of the method, the research method can effectively detect images with facial offset angles greater than 15° for students, and the cognitive state of online learning can be analyzed from the frequency detection of students’ blinking and yawning. From the results of image simulation experiments, it can be seen that this study proposes a cascaded regression tree localization optimization multitask convolutional neural network face recognition method, which has the highest image recognition accuracy of 86%, a recall rate of 0.85, and an f1 value of 0.855. The experimental results show that online learning state recognition based on image analysis can effectively monitor abnormal states during students’ learning process, improve students’ online learning efficiency, and provide necessary student state information support for teachers, promoting the improvement of online teaching quality.