Auditory Graphs for Stochastic Processes: A Case Study on Mathematical Accessibility
Author(s)
Nagami, Haru
Yanashima, Shun
Hori, Teturo
Kobayashi, Makiko
Ono, Kyosuke
Tanaka, Kennosuke
Yamakawa, Ryota
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Abstract
The demand for data scientists has been rapidly increasing in recent years. However, visually impaired individuals face significant challenges in interpreting stochastic process data, which are frequently used in various fields such as finance. Addressing these barriers through auditory graph techniques could help expand career opportunities for visually impaired individuals. In this study, we evaluate whether auditory graphs, which map the values of functions to variations in audio pitch, enable five visually impaired students from grades 5, 9, and 12 to differentiate between graphs of Brownian motion. Experimental results suggest that auditory graphs effectively convey the distinguishing features of Brownian motion, indicating their potential for supporting mathematical accessibility in stochastic contexts. Due to the small number of participants, this study should be regarded as a pilot study, and further research with larger sample sizes is required to confirm generalizability.
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2025-06
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Text
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Proceedings
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Creative Commons Attribution Non-Commercial 4.0 International (CC BY-NC 4.0)