Modeling Cognitive Load and Affect to Support Adaptive Online Learning

Online learning has been spreading with increasing availability and diversity of digital resources. Understanding how students' cognitive load and affect changes when using learning technologies will help us decipher the learning process and understand student needs. In this research, we focus on modeling learner's cognitive load and affect using real-time physiological reactions. We explore what affect modeling contributes to the modeling of cognitive load, and how real-time cognitive load changes alongside learning activities. We want to further investigate if cognitive load modeling helps diagnose learner knowledge and facilitates improvement. We have designed two case studies: one where students are learning python with an e-learning system and another where they are practicing literacy skills with a web-based learning game. To collect learner data, we have implemented a sensing prototype consisting of an eye tracker and a wireless wristband.