Exploring the Optimal Time Window for Predicting Cognitive Load Using Physiological Sensor Data

TL;DR

This study investigates the optimal time window length for analyzing physiological sensor data to predict cognitive load, finding longer windows (>90 seconds) improve prediction accuracy and challenge traditional immediate-response assumptions.

Contribution

It provides empirical evidence on the effectiveness of longer time windows for physiological data analysis in cognitive load prediction, guiding future educational technology development.

Findings

Longer time windows (>90 seconds) yield better cognitive load prediction.

Optimal window length varies across classifiers, indicating complexity.

Broader temporal analysis offers a more comprehensive understanding of cognitive processes.

Abstract

Learning analytics has begun to use physiological signals because these have been linked with learners' cognitive and affective states. These signals, when interpreted through machine learning techniques, offer a nuanced understanding of the temporal dynamics of student learning experiences and processes. However, there is a lack of clear guidance on the optimal time window to use for analyzing physiological signals within predictive models. We conducted an empirical investigation of different time windows (ranging from 60 to 210 seconds) when analysing multichannel physiological sensor data for predicting cognitive load. Our results demonstrate a preference for longer time windows, with optimal window length typically exceeding 90 seconds. These findings challenge the conventional focus on immediate physiological responses, suggesting that a broader temporal scope could provide a more comprehensive understanding of cognitive processes. In addition, the variation in which time windows best supported prediction across classifiers underscores the complexity of integrating physiological measures. Our findings provide new insights for developing educational technologies that more accurately reflect and respond to the dynamic nature of learner cognitive load in complex learning environments.