An Agent-Based Modeling for a Reader’s Cognitive Load and Performance

Abstract

Reading performance and cognitive load play an important role to facilitate learners to learn, memorize, and understand a novel piece of information.The cognitive demands of the reading tasks for solving complicated problems, when using poor learning materials, or in a distraction condition such as noise or interruptions, can have a significant impact on reading performance. These conditions can impair the reading performance, and may thus deter effective learning experience. In this paper, an agent computational model is proposed. Different types of relations between extraneous, intrinsic, and germane loads were used to analyze the reader’s performance over time. Simulation experiments under different conditions and parameters setting showed that the model is able to produce realistic behaviour when tested on different types of personalities and conditions. Through mathematical analysis, the equilibria of the model was determined.