基于多维连续情感识别的在线学习风险预警OA

Online teaching teachers are unable to predict academic performance in a timely manner and intervene in advance like traditional face-to-face classrooms due to a lack of emotional interaction with students.To this end,it establishes an online teaching academic risk prediction method based on sentiment analysis.Firstly,by obtaining the multidimensional emotional parameters of Valence-Arousal-Dominance(VAD),more comprehensive and detailed emotional information can be obtained.Secondly,it uses orthogonal convolutional neural networks for multi-dimensional emotion parameter recognition.Finally,multiple classic regression models are selected for academic performance and academic risk prediction experiments,and the most suitable model for predicting academic risk is ultimately selected.The experimental results show that compared with the unconstrained model,the neural network with orthogonal convolutional constraints improves the accuracy of emotion parameter prediction.The introduction of VAD emotional parameters in predicting academic achievements significantly improves the accuracy of prediction compared to using only cognitive data.The ADA-RF-EXP model performs the best in predicting final grades and warning of failure risks.