Comparing Design Team Self Reports With Actual Performance: Cross Validating Assessment Instruments
Author(s) -
Robin Adams,
Pimpida Punnakanta,
Craig D. Lewis,
Cynthia J. Atman
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.18260/1-2--10043
Subject(s) - rubric , session (web analytics) , engineering design process , engineering education , computer science , test (biology) , engine department , process (computing) , task (project management) , reliability (semiconductor) , engineering management , engineering , mathematics education , psychology , systems engineering , mechanical engineering , paleontology , power (physics) , physics , quantum mechanics , world wide web , biology , operating system
Assessing student learning of the engineering design process is challenging. Students’ ability to answer test questions about the design process or record their design activities may differ significantly from their actual performance in solving “messy” open-ended problems. In the Pacific Northwest, multi-university participants in a National Science Foundation supported project (Transferable Integrated Design Engineering Education, TIDEE) have implemented and disseminated a Mid-Program Assessment instrument for assessing engineering student design competency. One part of the instrument requires student teams to document (e.g., self-report) their design decisions and processes while engaged in a design task. These written self-reports are scored using a rubric that has demonstrated a high inter-rater reliability. We are interested in comparing the scores derived from these selfreports with measures of actual design performance. Our research method for analyzing design performance is verbal protocol analysis. In this study, eighteen teams of students (2-6 students per team) from four different institutions were videotaped as they completed the TIDEE Mid-Program Assessment. In this paper we provide 1) a description of the assessment instrument, 2) our research methods for assessing the validity of the instrument, 3) examples of comparing self-reports to performance, and 4) a summary of our findings. We conclude with a discussion of the strengths and weaknesses of this study, as well as implications for teaching and assessing engineering student design competency.
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