Multimodal Concentration Evaluation in Online Learning
This paper presents a multi-modal concentration evaluation model CE-HPE for the challenge of evaluating the status of online learning effectively.CE-HPE estimates concentration of learners with two modal features such as head posture and facial expression.It combines the 6DRepNet algorithm to predict the yaw angle and the pitch angle of head,and classifies facial expressions with the improved DAN algorithm RES-DAN.CE-HPE quantifies two modal features and calculates the concentration score with their weights.Experiments based on the RAF-DB data set show that the accuracy of RES-DAN is superior to that of comparative models.Furthermore,ablation studies also verify the effectiveness of each module in RES-DAN.We develop an online learning concentration evaluating system which can calculate the quantification score of concentration in online learning for single player mode and multiplayer mode in real time or a period.The results show that our system can effectively detect and evaluate the concentration status in online learning.