Analysis of Design Process Knowledge Task Responses: Statistical Approaches to Uncover Patterns (Research)

Monica E Cardella; Ming-Chien Hsu; George D Ricco

Download Paper | Permalink

Conference: 2014 ASEE Annual Conference & Exposition
Location: Indianapolis, Indiana
Publication Date: June 15, 2014
Start Date: June 15, 2014
End Date: June 18, 2014
ISSN: 2153-5965
Conference Session: Principles of K-12 Engineering Education and Practice
Tagged Division: K-12 & Pre-College Engineering
Page Count: 18
Page Numbers: 24.180.1 - 24.180.18
DOI: 10.18260/1-2--20071
Permanent URL: https://peer.asee.org/20071
Download Count: 402

Request a correction

Paper Authors

biography

Monica E Cardella Purdue University, West Lafayette orcid.org/0000-0002-4229-6183

visit author page

Monica E. Cardella is an Associate Professor of Engineering Education at Purdue University and the Director of Informal Learning Environments Research for INSPIRE (the Institute for P-12 Engineering Research and Learning). She has a BSc in Mathematics from the University of Puget Sound and an MS and PhD in Industrial Engineering from the University of Washington. Her research focuses on: parents' roles in engineering education; engineering learning in informal environments; engineering design education; and mathematical thinking in engineering.

visit author page

biography

Ming-Chien Hsu Purdue University, West Lafayette

visit author page

Ming-Chien Hsu is a doctoral candidate of Engineering Education at Purdue University. She was an electrical engineer working on semiconductor devices before stepping into the realm of education research. Her current research explores and characterizes learning experiences in engineering such as design and interdisciplinary learning.

visit author page

biography

George D Ricco Purdue University, West Lafayette

visit author page

George Dante Ricco is a recent graduate of the School of Engineering Education at Purdue University and is on the job market. His work focuses on applying various research methods to address long standing anecdotal questions, ranging from ethnographic studies to hierarchical linear models.
He was born in Kent, Ohio. He attended Walsh Jesuit High School, and instead of becoming a Jesuit, he decided to go to Case Western Reserve University in Cleveland to obtain his BSE in engineering physics (2002). He then spent a number of years on the beach at the University of California at Santa Cruz, receiving master's degrees in physics (2007) and earth and planetary sciences (2008) until emancipated by Prof. Matthew Ohland at Purdue University. He enjoys cycling, weightlifting, running, photography, volunteering at a number of organizations, and the untold intellectual pleasures provided by the study of Lagomorph physiology. He resides in Lafayette, Indiana, and in-between job interviews spends time with his Leporidae life partner, Rochelle Huffington Nibblesworth.

visit author page

Download Paper | Permalink

Abstract

Analysis of Design Process Knowledge Task Responses: Statistical Approaches to Uncover Patterns (Research-to-Practice)While the Next Generation Science Standards have very recently solidified and are just nowbeginning to become adopted, engineering has already been taught in K-12 classrooms in someschools for many years. As engineering has been taught, different motivations have driven theinclusion of engineering: for purposes of increasing students’ interest in pursuing engineeringmajors and careers; for providing a context so that students’ might better learn mathematics andscience (and at times language arts, social studies and art skills too); and for students to learnengineering concepts and skills. One of the core engineering concepts and skills that students canlearn is the engineering design process—they can gain knowledge about what the design processis and what different activities make up the design process, and they can gain skills as they learnto employ the engineering design process.This work presents findings from five academic years worth of data collected from multipleschools, where teachers initially began to learn about engineering and how to teach engineeringin 2007. Throughout the history of the project, teachers and students were asked to complete anassessment task to measure the change in their understanding of the design process, eitherbefore-and-after a week-long teacher professional development workshop or at the beginning-vs.-end of the academic school year. Analysis of the data (which includes 1,171 responses from2nd, 3rd and 4th graders) has already established that there are changes in both teachers andstudents’ understanding of the design process (as measured by the task).At this point, analysis focuses on identifying patterns in the data beyond the simple comparisonsalready conducted to determine that there were pre-/post-test differences. First, we employ anormative cluster technique to identify groups of responses that are more strongly coupled thanothers to determine how to lay the groundwork for the construction of a structural equationmodel in a larger study. Second, we employ classic and novel techniques in list analysis forsimple ranking of the two lists (students and teachers), treating the teacher responses as a semi-homogeneous “expert” group of respondents. While analysis is currently underway, the analysiswill be completed prior to submission of the full conference paper.The results of this analysis (as well as dissemination of the task itself) can provide valuableinsights as we see a dramatic increase in the number of elementary schools adopting NGSS andtherefore including engineering design process instruction. Additionally, we believe that theresults will be able to inform efforts aimed at preparing teachers for engineering designeducation.

Citation
Format

Cardella, M. E., & Hsu, M., & Ricco, G. D. (2014, June), Analysis of Design Process Knowledge Task Responses: Statistical Approaches to Uncover Patterns (Research) Paper presented at 2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana. 10.18260/1-2--20071

TY  - CPAPER
AB  - Analysis of Design Process Knowledge Task Responses: Statistical Approaches to Uncover                            Patterns (Research-to-Practice)While the Next Generation Science Standards have very recently solidified and are just nowbeginning to become adopted, engineering has already been taught in K-12 classrooms in someschools for many years. As engineering has been taught, different motivations have driven theinclusion of engineering: for purposes of increasing students’ interest in pursuing engineeringmajors and careers; for providing a context so that students’ might better learn mathematics andscience (and at times language arts, social studies and art skills too); and for students to learnengineering concepts and skills. One of the core engineering concepts and skills that students canlearn is the engineering design process—they can gain knowledge about what the design processis and what different activities make up the design process, and they can gain skills as they learnto employ the engineering design process.This work presents findings from five academic years worth of data collected from multipleschools, where teachers initially began to learn about engineering and how to teach engineeringin 2007. Throughout the history of the project, teachers and students were asked to complete anassessment task to measure the change in their understanding of the design process, eitherbefore-and-after a week-long teacher professional development workshop or at the beginning-vs.-end of the academic school year. Analysis of the data (which includes 1,171 responses from2nd, 3rd and 4th graders) has already established that there are changes in both teachers andstudents’ understanding of the design process (as measured by the task).At this point, analysis focuses on identifying patterns in the data beyond the simple comparisonsalready conducted to determine that there were pre-/post-test differences. First, we employ anormative cluster technique to identify groups of responses that are more strongly coupled thanothers to determine how to lay the groundwork for the construction of a structural equationmodel in a larger study. Second, we employ classic and novel techniques in list analysis forsimple ranking of the two lists (students and teachers), treating the teacher responses as a semi-homogeneous “expert” group of respondents. While analysis is currently underway, the analysiswill be completed prior to submission of the full conference paper.The results of this analysis (as well as dissemination of the task itself) can provide valuableinsights as we see a dramatic increase in the number of elementary schools adopting NGSS andtherefore including engineering design process instruction. Additionally, we believe that theresults will be able to inform efforts aimed at preparing teachers for engineering designeducation.
AU  - Monica E Cardella
AU  - Ming-Chien Hsu
AU  - George D Ricco
CY  - Indianapolis, Indiana
DA  - 2014/06/15
PB  - ASEE Conferences
TI  - Analysis of Design Process Knowledge Task Responses: Statistical Approaches to Uncover Patterns (Research)
UR  - https://peer.asee.org/20071
DO  - 10.18260/1-2--20071
ER  -