  Educational games are being increasingly used to support self-paced learning.
However, educators and system designers often face challenges in monitoring
student affect and cognitive load. Existing assessments in game-based learning
environments (GBLEs) tend to focus more on outcomes rather than processes,
potentially overlooking key aspects of the learning journey that include
learner affect and cognitive load. To address this issue, we collected data and
trained a model to track learner cognitive load while they used an online
literacy game for English. We collected affect-related physiological data and
pupil data during gameplay to enable the development of models that identify
these latent characteristics of learner processes. Our model indicates the
feasibility of using these data to track cognitive load in GBLEs. Our
multimodal model distinguished different levels of cognitive load, achieving
the highest Kappa (.417) and accuracy (70%). Our model reveals the importance
of including affect-related features (i.e., EDA and heart rate) when predicting
cognitive load and extends recent findings suggesting the benefit of using
multiple channels when modeling latent aspects of learner processes. Findings
also suggest that cognitive load tracking could now be used to facilitate the
creation of personalized learning experiences.
