NSF Award
2502778
A key learning outcome in undergraduate chemistry courses is the ability to understand and predict the behavior of atoms and molecules. The types, amounts, and other characteristics of atoms or molecules determine whether and how they interact. With support from NSF’s EHR Core Research (ECR) program, this project seeks to serve the national interest by examining the development of students’ ability to interpret and use graphical representations about atoms and molecules and their interactions. The research will involve two studies: (1) Analysis of individual student’s graphical reasoning and ideas about atoms and molecules, as the student progresses through a general chemistry course sequence; and (2) How students reason with and about graphs in a collaborative classroom environment. This research is expected to increase understanding about how students’ abilities to use graphs for making inferences change over time. This evidence about student learning can help education researchers and will better support development of students’ knowledge about key concepts in chemistry. Since all science, technology, engineering, and mathematics (STEM) fields involve graphical reasoning, the project findings may be applicable to other STEM fields. Thus, translation of research findings from this research into practice ultimately has the potential to improve teaching and learning in STEM courses and benefit society by increasing the number of students who are successful in these areas of study.<br/><br/>This project will explore how students learn about varied population schema, the idea that there are a range of states for individual particles within a system at the particulate level, which has been hypothesized to be a threshold concept for chemistry learning. In addition, the team will study how students interpret and make inferences using graphical representations related to varied population schema. Despite the prevalence and importance of graphical models that relate to particulate-level systems, there are relatively few theory-based qualitative accounts of how general chemistry students interpret and make inferences from such models. Models that relate to particulate-level variability, such as distribution graphs and reaction coordinate diagrams, are particularly important. The research aims of the project are: (1) to describe the impact of a general chemistry course sequence on individual student’s conceptual structures pertaining to variability at the particulate level, including their ability to interpret and make inferences from graphical representations; and (2) to describe in-the-moment dynamics of students’ joint sense-making during a collaborative learning activity. To address aim 1, we will conduct a study with multiple semi-structured interviews regarding graphical models related to the varied population schema throughout the two-semester undergraduate general chemistry sequence. This will involve the use of eye-tracking technology to analyze features in representations that students attend to supplement their verbal explanations. Moreover, interviews will examine the ways in which students connect and use knowledge elements to make inferences about chemical properties such as diffusion, reaction rate, and equilibrium. For aim 2, the project team will investigate the discourse and dynamics of groups of students as they engage in a graphical-reasoning focused collaborative learning activity designed to emphasize the variability in a system. Analysis for both aims will be informed by coordination class theory and the microgenetic learning analysis approach. The success of the project will be assessed by an advisory panel comprised of disciplinary experts in chemistry and experts in educational research. Research findings will be shared via scholarly publications and conference presentations. Learning materials developed will be disseminated via faculty workshops and on the Chemical Education Xchange website. Thus, the project will advance knowledge of how undergraduate students develop understandings related to interpreting graphs that involve the varied population schema. In addition, it has potential to inform the design and implementation of collaborative learning activities for STEM education, and to propagate methodologies adapted from other fields for broader use in discipline-based education research. This project is funded by the EHR Core Research Program, which supports work that advances fundamental research on STEM learning and learning environments, broadening participation in STEM, and STEM workforce development.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
