The Language of Organic Chemistry: Is Fluency the Key to Success?

Abstract

People around the world use language to exchange ideas and connect with each other. We use the language of organic chemistry similarly, but in addition to words, we use symbols to represent chemical phenomena. One such example of symbols used in chemistry is the electron-pushing formalism (EPF), which depicts electron movement during chemical reactions. Students spend a large amount of time decoding and often make no sense of chemistry's symbolic language. This expenditure increases students' working memory load, limiting their abilities to learn new concepts, as they can only process as much information as their working memory allows. As such, students often rely on rote memorization, employ heuristics, and adopt a product-oriented thinking approach, resulting in limited reasoning abilities that hinder their learning progress. In this study, we explored how fluency in the electron-pushing formalism (EPF) relates to second-year organic chemistry students' cognitive load, reasoning ability and chemistry argumentation skills. Additionally, we looked to reveal students' misinterpretations after learning about chemical factors affecting stability. We hypothesized that participants more fluent in the EPF would exhibit lower pupil measures due to decreased cognitive load. We also hypothesized that participants more fluent in the EPF would demonstrate a greater reasoning ability since they would have a greater cognitive capacity to engage in chemical reasoning. We employed a pre/post-experimental design separated by a learning phase with a treatment group focusing on EPF mastery and a control group focusing on acid–base chemistry mastery. The pre- and post-tests assessed participants' ability to solve problems related to the EPF and evaluated their scientific reasoning ability. To measure cognitive load, we used eye-tracking technology to capture changes in participants' average and maximum pupil diameters. Eye-tracking data was analyzed using a custom-made Python program, and participants' reasoning ability was analyzed by categorizing their arguments based on complexity and examining connections between chemical factors.