Evaluating Student Engagement with Deep Learning

[Objective] This paper constructs an expression data set of engagement degrees and designs a joint evaluation model for students’class engagement. It addresses the issues of lacking relevant expression data sets and the low accuracy of the existing models. [Methods] We collected data based on actual online classes and constructed an expression dataset suitable for engagement recognition. Then, we designed an improved VGG model to evaluate the dataset and recognize student engagement. Third, we combined the expression and face scores to establish a joint evaluation model for students’engagement and calculated the tested students’actual class engagement scores. [Results] We adjusted and verified the network structure through parameter tuning optimization for engagement expression recognition. The improved model VGG16+Dense+Dropout(lr=1e-5) had the highest accuracy among the four compared model architectures, reaching over 92%. The joint engagement score is more accurate for engagement evaluation than the single expression engagement score. [Limitations] We did not include more ablation studies in training the model; more research is needed to explore the deeper neural networks. [Conclusions] The dataset of W-AttLe is suitable for evaluating students’class engagement. The proposed joint engagement evaluation model outperforms the single index model. The proposed weighted test scheme combining knowledge point test and self-test of comprehension degree validates the joint engagement degree model. © 2023 Chinese Medical Association. All rights reserved.