A Closer Look at the Use of Augmented Reality to Teach Organic Chemistry : Can We Do Better?

Abstract

This cumulative dissertation investigates the role of augmented reality learning environments (AR-LEs) in enhancing representational competence in organic chemistry education at the secondary level. Representational competence—the ability to understand, interpret, and switch between macroscopic, symbolic, and particulate levels of representation—is a critical part of chemical reasoning, yet remains a significant challenge for learners. Leveraging the unique capabilities of AR to visualize and interact with abstract molecular processes (reaction mechanisms) and complex spatial relationships (enantiomers and meso compounds), the four studies presented here explore the impact of AR-LEs on student learning outcomes, cognitive load, technology acceptance, and the quality of connections students make between the particulate and symbolic levels of representation during instruction. The dissertation includes three foundational quantitative studies and a capstone qualitative study. Each foundational study focuses on a distinct topic within organic chemistry—electrophilic aromatic substitution, radical substitution, and chirality—and employs a pre-post design to measure student learning gains, cognitive load (using a validated cognitive load survey), and acceptance of the AR technology (using adapted Technology Acceptance Model instruments). Across all three studies, statistically significant learning gains were observed. Students reported low extraneous and high germane cognitive load, and demonstrated high acceptance of the AR tools, citing both usefulness and ease of use. The capstone study builds on the first foundational study and adopts a qualitative classroom ethnography approach to analyze video recordings of students using the AR-LE to lean about an electrophilic aromatic substitution. Using a coding scheme informed by Kozma and Russell’s framework for representational competence, this study offers insight into how students interact with AR to make connections between symbolic and particulate representations. Over 80% of student connections were rated as intermediate or advanced in quality, with peer interaction playing a key role in fostering understanding. The study found that students varied in their approach to and depth of representational reasoning, influenced by factors such as prior knowledge and use of scaffolding. Together, the four studies provide a comprehensive perspective on the integration of AR into chemistry classrooms. Quantitative data highlight the effectiveness of AR-LEs in supporting conceptual understanding and minimizing cognitive load, while qualitative data shed light on the nuanced ways in which students engage with the technology. This dissertation affirms that AR can be a powerful pedagogical tool for addressing representational challenges in chemistry, provided it is thoughtfully embedded in well-designed instructional settings. It also underscores the need for ongoing research that examines not only learning outcomes but also learning processes, as well as the instructional contexts in which AR is most effective. The findings contribute to the growing body of literature on educational technologies in STEM and offer practical guidance for researchers, educators, and developers seeking to harness AR’s potential in chemistry education.