Thermodynamic Concepts in a Model-Eliciting Activity

Paul Nicholas van Bloemen Waanders; Andrew Kean; Brian P. Self

Download Paper | Permalink

Conference: 2011 ASEE Annual Conference & Exposition
Location: Vancouver, BC
Publication Date: June 26, 2011
Start Date: June 26, 2011
End Date: June 29, 2011
ISSN: 2153-5965
Conference Session: Thermodynamics, Fluids, and Heat Transfer II
Tagged Division: Mechanical Engineering
Page Count: 33
Page Numbers: 22.1519.1 - 22.1519.33
DOI: 10.18260/1-2--18868
Permanent URL: https://peer.asee.org/18868
Download Count: 463

Paper Authors

biography

Paul Nicholas van Bloemen Waanders California Polytechnic State University, San Luis Obispo, Mechanical Engineering

visit author page

I am a Mechanical Engineering Masters Student studying at Cal Poly, San Luis Obispo.

visit author page

author page

Andrew Kean California Polytechnic State University

biography

Brian P. Self California Polytechnic State University, San Luis Obispo

visit author page

Brian Self is a Professor in the Mechanical Engineering Department at California Polytechnic State University, San Luis Obispo. Prior to joining the faculty at Cal Poly in 2006, he taught for seven years at the United States Air Force Academy and worked for four years in the Air Force Research Laboratories. Research interests include active learning and engineering education, spatial disorientation, rehabilitation engineering, sports biomechanics, and aerospace physiology. He worked on a team that developed the Dynamics Concept Inventory and is currently collaborating on a grant to develop and assess Model Eliciting Activities in engineering. Brian was the 2008 - 2010 ASEE Zone IV Chair and serves as Cal Poly’s ASEE Campus Representative.

visit author page

Download Paper | Permalink

Abstract

Thermodynamic Concepts in a Model-Eliciting ActivityAbstractModel-Eliciting Activities (MEAs) are reality-based group exercises that require open-endedproblem solving. These MEAs are currently being developed and tested to confirm that theyhelp engineering students acquire key learning objectives. This paper focuses in depth on theperformance and value of individual MEA which we have now implemented several times in anintroductory thermodynamics course. In addition, we present the results an in-depth reflectiontool to determine the students’ learned concepts and opinions. In the context of a supermileagevehicle (from a 2000+ mpg student competition), the premise for this MEA surrounds thethermal efficiency of an engine cycle. Basically, we provide a real p-V diagram of a cycle, andstudents have to pick up to six processes and states to match the complex shape of the cycle. TheMEA was tested in six separate classes with students grouped into fours. The students used onehour of class time in their groups, and had an additional week to finish and turn in a memo thatdescribed their analysis of the engine cycle. We present an analysis of common successes anderrors contained in the student deliverables. Also, we are just beginning to notice certain trendsin the data including that groups that modeled their cycle with at least one isothermal processcalculated a work output for the cycle within 25% of the measured value, while the remaininggroups average a 36% error. A similar trend is seen with those groups that include anatmospheric condition state in their model, with a 15% error in those that do and a 41% errorwith those that do not. This provides insight into the successful methods of solving this MEAand what possible concepts the students are missing. Another method of assessing the MEA is apost-exercise reflection tool that allows the students to reflect on what they learned and recordthe troubles and successes that they experienced. Across the two academic quarters of ourinvestigation, the students indicated in the reflection that they learned very similar concepts; forinstance 63% of the students indicated that they learned about modeling a polytropic processfrom the MEA. Because the original goal was to help students understand the difficult conceptsin modeling processes, this reflection has shown us that 63% of students believe they reachedthat goal. On the other hand, this consistent response indicates that ~37% of the class does notfeel they are learning one of the key objectives of this MEA. Even with the drawbacks of usingstudent surveys to assess learning, this method of analyzing MEAs will help us modify andrefine the MEA to be more effective in the future.

Citation
Format

van Bloemen Waanders, P. N., & Kean, A., & Self, B. P. (2011, June), Thermodynamic Concepts in a Model-Eliciting Activity Paper presented at 2011 ASEE Annual Conference & Exposition, Vancouver, BC. 10.18260/1-2--18868

TY - CPAPER
AB - Thermodynamic Concepts in a Model-Eliciting ActivityAbstractModel-Eliciting Activities (MEAs) are reality-based group exercises that require open-endedproblem solving. These MEAs are currently being developed and tested to confirm that theyhelp engineering students acquire key learning objectives. This paper focuses in depth on theperformance and value of individual MEA which we have now implemented several times in anintroductory thermodynamics course. In addition, we present the results an in-depth reflectiontool to determine the students’ learned concepts and opinions. In the context of a supermileagevehicle (from a 2000+ mpg student competition), the premise for this MEA surrounds thethermal efficiency of an engine cycle. Basically, we provide a real p-V diagram of a cycle, andstudents have to pick up to six processes and states to match the complex shape of the cycle. TheMEA was tested in six separate classes with students grouped into fours. The students used onehour of class time in their groups, and had an additional week to finish and turn in a memo thatdescribed their analysis of the engine cycle. We present an analysis of common successes anderrors contained in the student deliverables. Also, we are just beginning to notice certain trendsin the data including that groups that modeled their cycle with at least one isothermal processcalculated a work output for the cycle within 25% of the measured value, while the remaininggroups average a 36% error. A similar trend is seen with those groups that include anatmospheric condition state in their model, with a 15% error in those that do and a 41% errorwith those that do not. This provides insight into the successful methods of solving this MEAand what possible concepts the students are missing. Another method of assessing the MEA is apost-exercise reflection tool that allows the students to reflect on what they learned and recordthe troubles and successes that they experienced. Across the two academic quarters of ourinvestigation, the students indicated in the reflection that they learned very similar concepts; forinstance 63% of the students indicated that they learned about modeling a polytropic processfrom the MEA. Because the original goal was to help students understand the difficult conceptsin modeling processes, this reflection has shown us that 63% of students believe they reachedthat goal. On the other hand, this consistent response indicates that ~37% of the class does notfeel they are learning one of the key objectives of this MEA. Even with the drawbacks of usingstudent surveys to assess learning, this method of analyzing MEAs will help us modify andrefine the MEA to be more effective in the future.
AU - Paul Nicholas van Bloemen Waanders
AU - Andrew Kean
AU - Brian P. Self
CY - Vancouver, BC
DA - 2011/06/26
PB - ASEE Conferences
TI - Thermodynamic Concepts in a Model-Eliciting Activity
UR - https://peer.asee.org/18868
DO - 10.18260/1-2--18868
ER -