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Decision-based Learning for a Sophomore Level Thermodynamics Course

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Conference

2015 ASEE Annual Conference & Exposition

Location

Seattle, Washington

Publication Date

June 14, 2015

Start Date

June 14, 2015

End Date

June 17, 2015

ISBN

978-0-692-50180-1

ISSN

2153-5965

Conference Session

Teaching and Learning Strategies I

Tagged Division

Educational Research and Methods

Page Count

17

Page Numbers

26.440.1 - 26.440.17

DOI

10.18260/p.23779

Permanent URL

https://peer.asee.org/23779

Download Count

127

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Paper Authors

biography

Matthew Hagge Iowa State University

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Matt Hagge is a Senior Lecturer at Iowa State University. He has spent his career talking to students to figure out how students think and learn. The result of these talks has been the development of a course-wide decision framework for a thermodynamics course that allows students to solve previously unseen problems while building their expertise. This pedagogy is called Decision Based Learning, and has received tremendous student feedback and results. Students are able to solve complex problems through understanding rather than memorization and copying. Learning how to think, how to self reflect, how to take personal responsibility for learning, and the development of expert problem solving skills are all reasons why this style of teaching is life changing for many students.

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Mostafa Amin-Naseri Iowa State University

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Mostafa Amin-Naseri, is a masters student in industrial engineering in Iowa State University. He is interested in data mining and statistical analysis. He applies data analysis to educational data, building learner models and reporting tools for instructors, in order to evaluate and enhance educational systems and methods.

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Stephen B Gilbert Iowa State University

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Stephen B. Gilbert received a BSE from Princeton in 1992 and PhD from MIT in 1997. He has worked in commercial software development and run his own company. He is currently an assistant professor in the Industrial and Manufacturing Systems Engineering department at Iowa State University, as well as Associate Director of ISU's Virtual Reality Application Center and its Graduate Program in Human Computer Interaction. His research focuses on technology to advance cognition, including interface design, intelligent tutoring systems, and cognitive engineering.

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John Jackman Iowa State University

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John Jackman is an associate professor of industrial and manufacturing systems engineering at Iowa State University. His research interests include engineering problem solving, computer simulation, web-based immersive learning environments, and data acquisition and control.

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Enruo Guo

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Enruo Guo is a Ph.D. candidate in computer science, co-majoring in human computer interaction at Iowa State University. Before that, she got her M.S. degree in computer science and chemistry. Her research interests include AI in education, educational data mining and human computer interaction.

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Gloria Starns Iowa State University

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Senior Lecturer, Dept. of Mechanical Engineering, Iowa State University
Ph.D., Iowa State University, 1996

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LeAnn E Faidley Wartburg College

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Dr. LeAnn Faidley is an Assistant Professor of Engineering Science at Wartburg College in Waverly, IA. She teaches in the areas of Freshmen Engineering, Mechanics, Materials, and Design. Her pedagogical research areas include methods for improving student engagement with the material, service learning, and electronic tutors. Her technical research is in the area of magnetically activated smart materials.

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Abstract

Decision based learning for a sophomore level thermodynamics courseAn electronic tutor was designed and used to study the effectiveness of a new pedagogy calledDecision Based Learning. Decision based learning (DBL) has similarities to existing activelearning methods, but differs in the following important ways: 1) learning consists of developinga general to specific decision set that students can use to solve novel problems 2) students practicemaking the decisions in the instructor decision set, and are given help with understanding. Thegoal of this method is to improve student understanding through the process of decision makingso that they have a better chance of solving novel or complex problems.Students were asked to draw a T-v phase diagram for a refrigeration cycle they had never seenbefore. At any point in the drawing process, students could submit their work to receive feedback.Upon submission, the tutor evaluated the student drawing to determine the most general/importantunderstanding for which the student needed help. Rather than showing the correct answer, the tutorasked additional thought questions designed with the attempt to improve the student’sunderstanding to the point where he or she could make a decision. The decision making would, inturn, advance the student toward a correct solution. Students were asked to work at least 40minutes on the activity.Students completed a pre-tutor-test and a post-tutor-test for the purpose of determining the impactof the tutor in furthering students’ understanding regarding (P,T,v) property relationships forthermodynamic components. A significant amount of learning was demonstrated using DBL assuggested by a Cohen’s d=1.77 for 88 students, where d>0.8 shows a large effect. The pre-testresults indicate that on average only 25% of students were able to identify all three relations forcomponents, before the activity. Liquid gas separator, evaporator and condenser were thecomponents that were most misunderstood by students. The post activity test showed significantlearning for component relations. As an example, 58% of the students had a misconception aboutpressure for heat exchangers. This was reduced to 18% using a single activity. More detailedanalysis investigated how students learned using this activity.

Hagge, M., & Amin-Naseri, M., & Gilbert, S. B., & Jackman, J., & Guo, E., & Starns, G., & Faidley, L. E. (2015, June), Decision-based Learning for a Sophomore Level Thermodynamics Course Paper presented at 2015 ASEE Annual Conference & Exposition, Seattle, Washington. 10.18260/p.23779

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