Date of Award

Spring 5-10-2025

Level of Access Assigned by Author

Open-Access Thesis

Degree Name

Master's of Science in Teaching (MST)

First Committee Advisor

Mitchell Bruce

Second Committee Member

Alice Bruce

Third Committee Member

MacKenzie Stetzer

Additional Committee Members

Francois Amar

Abstract

A major goal of education at all levels is to develop students’ reasoning skills. In the chemistry discipline, reasoning involves the use of data to make connections between macroscopic observations and atomic-scale phenomena. Typical chemistry assessments (i.e. multiple-choice and open-ended questions) may not adequately capture this skill, as many can be answered correctly through the rote application of memorized content. To address the lack of valid, easily-administered assessments of students’ chemical reasoning, we sought to develop a tool to gain insight into how students engage with data to construct atomic-scale explanations of chemical phenomena. In service of this goal, we explored various modifications to the previously-reported Online Reasoning Chain Construction Assessment (ORCCA) format, which presents students with a multiple-choice question and a set of reasoning elements, i.e. true statements, and prompts students to assemble the elements into an argument that answers the question. Our explorations led us to a prototypical format, which we refer to as the Data Engagement and Analogical Reasoning ORCCA (DEAR ORCCA), which appears to have utility for uncovering students’ chemical reasoning abilities. Here, we present and discuss data collected using the DEAR ORCCA format, which suggest two reasoning approaches employed by students for interpreting experimental results. We provide a frame for thinking about these pathways and discuss the educational implications of this finding.

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