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Large-scale Research on Engineering Design in Secondary Classrooms: Big Learner Data Using Energy3D Computer-Aided Design

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2015 ASEE Annual Conference & Exposition


Seattle, Washington

Publication Date

June 14, 2015

Start Date

June 14, 2015

End Date

June 17, 2015





Conference Session

NSF Grantees’ Poster Session

Tagged Topic

NSF Grantees Poster Session

Page Count


Page Numbers

26.1054.1 - 26.1054.4



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Paper Authors


Senay Purzer Purdue University, West Lafayette Orcid 16x16

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Ṣenay Purzer is an Assistant Professor in the School of Engineering Education. She is the recipient of a 2012 NSF CAREER award, which examines how engineering students approach innovation. She serves on the editorial boards of Science Education and the Journal of Pre-College Engineering Education (JPEER). She received a B.S.E with distinction in Engineering in 2009 and a B.S. degree in Physics Education in 1999. Her M.A. and Ph.D. degrees are in Science Education from Arizona State University earned in 2002 and 2008, respectively.

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Robin Adams Purdue University, West Lafayette


Molly H Goldstein Purdue University, West Lafayette Orcid 16x16

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Molly Goldstein is a Ph.D. student in the School of Engineering Education at Purdue University, West Lafayette. She previously worked as an environmental engineer specializing in air quality influencing her focus in engineering design with environmental concerns. Her research interests include how students approach decision making in an engineering design context. She obtained her B.S. in General Engineering and M.S. in Systems and Entrepreneurial Engineering from the University of Illinois.

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K. Anna Douglas Purdue University, West Lafayette

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Anna earned her M.S. Ed in School Counseling and PhD in Educational Psychology from Purdue University. Her research interests are related to measurement and assessment in engineering education.

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Large-scale Research on Engineering Design in Secondary Classrooms: Big Learner Data Using Energy3D Computer-Aided Design Through a five-year collaborative project, the Concord Consortium and PurdueUniversity are applying a data-intensive approach to study one of the most fundamental researchtopics in learning sciences: “How do secondary students learn and apply science concepts inengineering design processes?” We have collected more 2GB of structured data from secondaryschool students in Indiana and Massachusetts through automatic, unobtrusive logging of studentdesign processes enabled by a unique CAD tool that supports the design of energy-efficientbuildings using Earth science and physical science concepts. Data includes fine-grainedinformation of student actions, experimentation results, electronic notes, and design artifacts.These process data are used to reconstruct the entire learning trajectory of each individualstudent with high resolution. Our research evaluates how these learning analytics applied to theseprocess data can be the computational counterparts of traditional performance assessmentmethods. Combining these process data with pre/post-tests and demographic data, we haveinvestigated the common patterns of student design behaviors and how they are associated withlearning outcomes with a specific focus on how students deepen their understanding of scienceconcepts involved in engineering design projects and how often and deeply students usescientific experimentation to make a design choice. So far we completed two small-scale studiesin Massachusetts and one study in Indiana using classroom observations and expert evaluations.We are collecting data with student interviews to validate metrics. Some key findings are…evidence that suggests that for science learning to occur, design projects used in classroomsshould (1) allow and emphasize trade-off analysis and include time and resources forexperimenting and data gathering; (2) provide instructional scaffolding and formative feedbackto guide student design.

Purzer, S., & Adams, R., & Goldstein, M. H., & Douglas, K. A. (2015, June), Large-scale Research on Engineering Design in Secondary Classrooms: Big Learner Data Using Energy3D Computer-Aided Design Paper presented at 2015 ASEE Annual Conference & Exposition, Seattle, Washington. 10.18260/p.24391

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