Breaking the Working Memory Barrier: Introducing a Novel Digital Tool to Improve Understanding of Mathematical Texts

Abstract

With the digitalization of academic material, there exists opportunity to utilize external tools to improve understanding of notation-heavy text. Prior research has shown a connection between working memory limits and conceptual learning. This study examines the creation and efficacy of a digital tool that acts as a crutch for working memory, through saving and resurfacing variable definitions across texts. 46 Harvard students in concentrating in STEM and Economics were asked to read two articles by the same author, one using the tool and one without, before answering questions about the excerpts about main arguments, detailed equations, and connections to related topics. These responses were then graded on a 5-point scale using Amazon Mechanical Turk. Significant results were found in the recall of specific equations as well as participant confidence in understanding the text, especially for participants with no experience reading similar-caliber text, but insignificance in grasping larger conceptual ideas. Further research is suggested in sample size and experimental design in order to analyze the longer-term effects of this tool on learning and memory.