Comparative case study of biochemistry students’ understanding of static and dynamic augmented reality models of hemoglobin | Poster Board #443

Biochemistry and related disciplines require understanding of the spatial relationships, structural components, and functions of entities that are invisible to the naked eye. Consequently, biochemistry education utilizes various models to visualize such processes and structures. Previous research has demonstrated the usefulness of visualizations for student learning; however, studies of common visualization technologies (e.g., Pymol) typically focused on evaluating student performance and attitude, with little attention towards the interplay between students’ linguistic expressions and visual representations. This study centers around BiochemAR, an augmented reality (AR) tool that presents students with five different AR models of hemoglobin. The first three models were static (i.e., non-animated) visualizations of the hemoglobin structure, and the last two models contained dynamic animations. In this mixed-methods study, we will present a comparison of students’ language patterns and the potential interplay between task environment, AR visualization models, and students’ language patterns. Audio and video recordings of semi-structured interviews with six undergraduate biochemistry students from two different institutions were collected and analyzed. We conducted thematic analysis to qualitatively examine students' language patterns that emerged while engaging with each AR model. Using these emergent themes, we then quantified student language to examine patterns of language use. Our finding showed that students’ use of dynamic language, which often occurred when describing mechanistic models of hemoglobin, increased as students were exposed to dynamic AR models (or those showing dynamic processes). Task environment may also exert influence on students’ use of dynamic language. In addition to highlighting common trends in students’ language patterns, our findings also illustrate different students’ unique individual language patterns when engaging with the same AR models. Insights from this work can inform the integration of different visualization tools by illustrating the interplays between the features of external representation, learning environment, and linguistic expressions within which different biochemistry meanings emerge.