Doctoral Consortium 2 - Modeling Cognitive Load and Affect in Interactive Game-based Learning Using Physiological Features

ABSTRACT

Media use in educational environments has been rapidly developing with the increasing availability and diversity of interactive elements. By understanding how student cognitive load changes when interacting with learning technologies, we can make sense of their learning process and how to provide appropriate, personalized media design to enhance the learning experience. Recent developments in sensing technologies makes it possible to capture learner’s dynamic physiological reactions. In this thesis research, we will identify learner’s cognitive load when interacting with educational media. We will explore how affective reactions contribute to the modeling of cognitive load and how real-time cognitive load changes alongside learning activities. We focus on modeling such information using physiological reactions that include pupillary, cardiovascular, and electrodermal responses. We are conducting this work in a game-based learning (GBL) environment for reading comprehension. We have implemented a sensing pipeline that will enable the modelling of learner affect and cognitive load. The modeling and analysis from this project could enable the design of interactive learning media that provides real-time adaptation to support learning processes.

HOW TO CITE (APA)

Cai, M. & Epp C.D. (2022). Modeling Cognitive Load and Affect in Interactive Game-based Learning Using Physiological Features. In Doctoral Consortium - ACM International Conference on Interactive Media Experiences: IMX 2022 (pp. 9–14).  Aveiro, Portugal.