Skeletal keypoint-based pipeline as a computer vision-based approaches

Abstract

Classroom engagement analysis plays an important role in understanding students’ learning behaviors in a non-intrusive manner. However, many existing computer vision-based approaches rely on complex deep learning architectures and time-consuming dataset construction, which limit their applicability in practical classroom settings. This study proposes a skeletal keypoint-based pipeline for classroom engagement analysis that combines person detection and single-person pose estimation. The extracted keypoints are represented in a low-dimensional form and used as input features for lightweight engagement classification models. Two classifiers, namely Ridge Classifier and Gradient Boosting, are evaluated to assess the effectiveness of the proposed pipeline. Experimental results on a test set of 432 samples show that the Ridge Classifier achieves an accuracy of 0.90 with a macro-average recall of 0.94. In contrast, Gradient Boosting achieves an accuracy of 0.96 and a macro-average precision of 0.98. In addition, the proposed pipeline significantly improves data collection efficiency, achieving approximately a sixteen-fold reduction in pose data acquisition time compared to conventional approaches. These results demonstrate that the proposed pipeline provides a practical solution for individual-level classroom engagement analysis under controlled classroom.