  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.
