Virtual Conference
July 26, 2021
July 26, 2021
July 19, 2022
Electrical and Computer
16
10.18260/1-2--37175
https://peer.asee.org/37175
630
Caroline Crockett is a graduate student at University of Michigan, working towards a PhD in electrical engineering. Her interests include image processing and engineering education research.
Dr. Cynthia Finelli is Professor of Electrical Engineering and Computer Science, Professor of Education, and Director and Graduate Chair for Engineering Education Research Programs at University of Michigan (U-M). Dr. Finelli is a fellow in the American Society of Engineering Education, a Deputy Editor of the Journal for Engineering Education, an Associate Editor of the IEEE Transactions on Education, and past chair of the Educational Research and Methods Division of ASEE. She founded the Center for Research on Learning and Teaching in Engineering at U-M in 2003 and served as its Director for 12 years. Prior to joining U-M, Dr. Finelli was the Richard L. Terrell Professor of Excellence in Teaching, founding director of the Center for Excellence in Teaching and Learning, and Associate Professor of Electrical Engineering at Kettering University.
Dr. Finelli's current research interests include student resistance to active learning, faculty adoption of evidence-based teaching practices, and the use of technology and innovative pedagogies on student learning and success. She also led a project to develop a taxonomy for the field of engineering education research, and she was part of a team that studied ethical decision-making in engineering students.
Previous studies show that many engineering undergraduates lack conceptual understanding of signals and systems. Although there is evidence that teaching style impacts conceptual understanding, there are few studies that investigate other reasons that some students understand the concepts while others do not. This paper tests how well a subset of factors from the Model of Educational Productivity (student ability and motivation, instructional quality and quantity, and home, peer, and classroom environment) explain the variance in signals and systems conceptual understanding at the end of an introductory undergraduate course. We present results from a linear regression model on data collected from surveys and concept inventories (n=124) that show the hypothesized factors explained 28% of variance in post-test conceptual understanding. Further, two of the factors were significantly predictive of conceptual understanding: ability (p<0.01) and motivation (p<0.10).
Crockett, C., & Finelli, C. J. (2021, July), Factors Influencing Conceptual Understanding in a Signals and Systems Course Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. 10.18260/1-2--37175
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