Minimizing Cognitive Load in Cyber Learning Materials – An Eye Tracking Study
Leon Bernard, Sagar Raina, Blair Taylor, Siddharth Kaza · ACM Symposium on Eye Tracking Research and Applications · 2021
Cybersecurity education is critical in addressing the global cyber crisis. However, cybersecurity is inherently complex and teaching cyber can lead to cognitive overload among students. Cognitive load includes: 1) intrinsic load (IL- due to inherent difficulty of the topic), 2) extraneous (EL- due to presentation of material), and 3) germane (GL- due to extra effort put in for learning). The challenge is to minimize IL and EL and maximize GL. We propose a model to develop cybersecurity learning materials that incorporate both the Bloom's taxonomy cognitive framework and the design principles of content segmentation and interactivity. We conducted a randomized control/treatment group study to test the proposed model by measuring cognitive load using two eye-tracking metrics (fixation duration and pupil size) between two cybersecurity learning modalities – 1) segmented and interactive modules, and 2) traditional-without segmentation and interactivity (control). Nineteen computer science majors in a large comprehensive university participated in the study and completed a learning module focused on integer overflow in a popular programming language.