•  
  •  
 

MAPPING REGIONAL AND TEMPORAL SHIFTS IN SCIENTIFIC TERMINOLOGY WITH PYTHON AND SENTENCE TRANSFORMERS

Abstract

Science terms can change across regions, academic levels, and time periods. These shifts can create confusion when students see different words used for the same idea or when older terms appear in some materials but not others. This project examines how chemistry terminology varies across learning environments by collecting data from ScienceDirect, Stack Exchange, YouTube transcripts, Google Ngrams, and PDFs. Python scripts were written to gather and organize this information so that term frequency and usage patterns could be compared. The project uses the SPECTER sentence transformer model to generate numerical representations of word meaning. These representations make it possible to measure how closely related different chemistry terms are. Using this method revealed clear patterns. Some terms appear more in certain academic settings. Some are used more often in specific regions. Some older terms are slowly fading from common use. These findings highlight areas where students may encounter inconsistent wording while learning chemistry. The work also points to several directions for future development. One possible next step is designing a browser add-on that scans scientific text and highlights terms that tend to vary across time or region. Another direction is improving the Python scripts so that data collection is faster and more versatile. These potential tools could make it easier for students and educators to understand terminology changes in chemistry. Overall, this study shows how computational methods can support clearer communication and help students better understand chemistry language.

Acknowledgements

Georgia Gwinnett College STEC Research Funds

This document is currently not available here.

Share

COinS