Using Multimodal Data to Assess Students’ Cognitive Load and Task Performance in Technology-Rich Environments (Poster 49)

• In Event: Sunday Poster Session 3:05 pm
  In Poster Session: Technology Innovations for Learning

Abstract

This study integrated self-reported and physiological data to examine how different cognitive load components predicted performance. Specifically, 88 medical students solved two diagnostics tasks of varying complexity. We recorded their electrodermal activities (EDA) and heart rate variability (HRV) during problem-solving. Students answered questionnaires to report perceived mental load and effort after each task. The findings revealed that students reported higher mental load in the complex task, whereas differences in self-reported mental effort were nonsignificant. Moreover, task complexity affected EDA (e.g., skin conductance response; SCR) and HRV (e.g., very-low-frequency band). Furthermore, the frequency of SCR peaks and low-frequency HRV activities could negatively predict performance, while self-reported mental effort and the high-frequency HRV band were positive predictors of performance.

Authors

• Tingting Wang, Renmin University of China

• Juan Zheng, Lehigh University

• Shan Li, Lehigh University

• Susanne P. Lajoie, McGill University