Reducing Cognitive Load as a Means of Enhancing Physics Learning and Comprehension

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Physics and Astronomy

Date of Award

9-1-2026

Abstract

Online educational videos have become an increasingly prominent tool for instruction, serving both as standalone resources and as supplements to traditional classroom teaching. The design and presentation of these videos can significantly influence students’ ability to process and retain complex information. This study applies Cognitive Load Theory (CLT) and established principles of multimedia learning to evaluate the effectiveness of research-based video lessons in the context of physics education. Specifically, this work focuses on electricity and magnetism (E\&M)—a core area of introductory physics in which many students consistently struggle, particularly with visualizing abstract and dynamic vector-based phenomena. To address these challenges, a series of animated educational videos were developed using Python-based visualization tools. These videos are designed to reduce extraneous cognitive load by incorporating research-backed strategies such as signaling, segmenting, and dual-channel processing. The goal is to make the content more accessible and engaging while promoting deeper conceptual understanding. To evaluate the impact of these videos, feedback was collected through post-surveys administered to undergraduate students enrolled in introductory physics courses. These surveys measure the perceived cognitive load experienced while watching the videos as well as perceived cognitive load following in-class activities related to video content. The collected data were analyzed to determine the effectiveness of the multimedia design choices, especially in how they support the visualization of the electric field. The results of this study indicate that there were no significant differences in cognitive load experienced between the treatment and control videos shown to students. This outcome may be attributed to the limited amount of matched student data (N=13). However, Bayesian analysis suggests that the treatment videos could decrease intrinsic load, which provides motivation for further research.

Advisor

Robynne Lock

Subject Categories

Physical Sciences and Mathematics | Physics

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