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DATA-DRIVEN ANALYSIS OF CHEMISTRY TERMINOLOGY ACROSS EDUCATIONAL AND SCIENTIFIC SOURCES

Abstract

The language used in chemistry plays an important role in how students understand and communicate scientific ideas, and even small differences in terminology can create confusion as students move between textbooks, online resources, and classroom instruction. This project examines how chemistry terms vary across different sources by collecting text from ScienceDirect, Stack Exchange, YouTube, Google Ngrams, and instructional PDF files. Python programs were used to gather and organize these terms so that patterns in frequency and usage could be compared. The expanded dataset shows that some chemistry terms are preferred in academic writing, while others appear more often in online discussions or in older teaching materials. Some terms are becoming less common over time, while others are used more frequently depending on the region or the audience. These differences matter because students may encounter several words that describe the same idea, which can make learning more difficult. The results of this study can help instructors choose clearer and more consistent terminology and can guide the creation of classroom tools that point out alternate word choices. The project also suggests future directions, such as improving data gathering methods and building educational tools that help students explore how chemistry language changes across time and different learning environments.

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