Classifying engagement in E-learning through GRU-TCN model using photoplethysmography signals

Abstract

Learning engagement is regarded as an important metric for assessing E-learning since it is related to the quality of online education and student performance. Previous studies have identified a correlation between heart rate variability (HRV) and learning engagement. However, a comprehensive and systematic investigation of the underlying mechanisms remains lacking. In this study, a GRU-TCN model was developed to classify learner engagement levels in E-learning based on photoplethysmography (PPG). PPG signals were collected from 40 participants as they learned the video course under task conditions of no-interference, music & bullet chat stimuli, and mental arithmetic. Twenty-eight HR/HRV metrics (including time-domain, frequency-domain, and Non-linear metrics) were examined, among which 10 metrics were sensitive to changes in learner engagement. To adjust for individual differences in heart response in learning tasks and to enhance the data, a three-step processing procedure was conducted to evaluate the PPG signals of each participant: (1) defined three levels of learning engagement: full attention, external, and internal divided attention, (2) normalized HRV metrics for different engagement levels by using the average metric of participants' baseline states, (3) Enhanced data with sliding time windows. The GRU-TCN classification model achieved F1-scores of 95% in the training set and 85% in the testing set, respectively, enabling effective automatic real-time tracking of learner engagement in E-learning.