Salt Lake City, Utah
June 23, 2018
June 23, 2018
July 27, 2018
Educational Research and Methods
8
10.18260/1-2--31252
https://peer.asee.org/31252
394
Nicole received her B.S. in Engineering Physics at the Colorado School of Mines (CSM) in May 2013. She is currently working towards a PhD in Materials Science and Engineering at the University of Illinois at Urbana-Champaign (UIUC) under Professor Angus Rockett and Geoffrey Herman. Her research is a mixture between understanding defect behavior in solar cells and student learning in Materials Science. Outside of research she helps plan the Girls Learning About Materials (GLAM) summer camp for high school girls at UIUC.
Dong San Choi is a PhD Candidate in the Department of Electrical and Computer Engineering at University of Illinois at Urbana-Champaign; choi88@illinois.edu.
Dr. Geoffrey L. Herman is a teaching assistant professor with the Deprartment of Computer Science at the University of Illinois at Urbana-Champaign. He also has a courtesy appointment as a research assistant professor with the Department of Curriculum & Instruction. He earned his Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign as a Mavis Future Faculty Fellow and conducted postdoctoral research with Ruth Streveler in the School of Engineering Education at Purdue University. His research interests include creating systems for sustainable improvement in engineering education, conceptual change and development in engineering students, and change in faculty beliefs about teaching and learning. He serves as the Publications Chair for the ASEE Educational Research and Methods Division.
This work-in-progress paper describes the preliminary synthesis of two data sets that explore how students solve engineering problems and access information from visual representations. This synthesis is a first step toward building theory about how students use visual representations across engineering disciplines.
By graduation, engineering students are expected to solve problems and communicate engineering ideas with several types of visual representations (e.g., free-body diagrams, graphs, or schematics). This skill requires students to identify conceptually-relevant features from visual representations and translate them into other useful forms to solve problems. Results from the cognitive science and physics education research literatures suggest that certain features of visual representations and students’ level of domain knowledge determine how effectively students access information from these representations. These two bodies of literature model knowledge acquisition and problem solving as generalized functions that are independent of the discipline. However, engineering concepts are visually represented differently depending on conventions within a discipline. These differences suggest that problem solving could also be discipline dependent. Thus, we should consider conventions of a discipline when redesigning visual representations for pedagogical interventions.
Our goal through this study is to develop theory that explores similarities and differences in students’ knowledge acquisition and problem-solving strategies using visual representations across different engineering disciplines. Our research question for this work in progress is how do certain features in a visual representation affect the types of mistakes students make during problem solving?
Our initial theory is built from the synthesis of results obtained from two previous qualitative studies that analyzed engineering students’ problem-solving behavior in Statics and Digital Logic using think-aloud interviews. We use the Constant Comparative Method to synthesize these data sets to determine whether previously identified themes and patterns are discipline specific or whether they transcend disciplinary boundaries.
In this work-in-progress paper, we present one emergent theme, “Informationally incomplete representations leads to coordinating multiple representations.” This theme describes how the presence of representations that do not contain enough information to translate to another representation result in students coordinating multiple representations during their problem solving. Results from our study can be used to better inform classroom interventions within the discipline-based education research community.
Johnson-Glauch, N., & Choi, D. S., & Herman, G. L. (2018, June), WIP: How Do Visual Representations Affect How Engineering Students Learn and Solve Problems Within and Across Disciplines? Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2--31252
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