Operationalizing Optimization in a Middle School Virtual Engineering Internship

New national science standards have elevated attention to student performance with a core set of science and engineering practices, yet guidance about how to assess these practices is only just emerging in the literature. This is particularly true for the set of engineering design–focused concepts a...

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Bibliographic Details
Published inJournal of science education and technology Vol. 29; no. 3; pp. 409 - 420
Main Authors Montgomery, Ryan, Greenwald, Eric, Crane, Samuel, Krakowski, Ari, Barber, Jacqueline
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 01.06.2020
Springer
Springer Nature B.V
Subjects
Academic Achievement
Analysis
Cognition
Cognitive Processes
Design
Design engineering
Design optimization
Education
Education parks
Educational Technology
Engineering
Engineering education
Evaluation Methods
Exploration
Exploratory behavior
Internship Programs
Middle School Students
Middle schools
Online Courses
Optimization
Product information
School facilities
Science Education
Student Evaluation
Students
Engineering design
Learning analytics
Virtual internships
Middle school
NGSS
Optimization
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Summary:New national science standards have elevated attention to student performance with a core set of science and engineering practices, yet guidance about how to assess these practices is only just emerging in the literature. This is particularly true for the set of engineering design–focused concepts and practices articulated in the Next Generation Science Standards’ (NGSS) Engineering, Technology, and Application of Science (ETS) standards. In this work, we present a model of student cognition for assessing student facility with the engineering design practice of optimization. We operationalize this model of cognition within a set of engineering-focused units for middle school, framed as Virtual Engineering Internships (VEIs). To operationalize the engineering design practice of optimization within our VEIs, we first broke optimization down into two more specific sub-behaviors: exploration and systematicity. We then designed metrics that provide evidence of those behaviors and would be observable given student clickstream data from a digital design tool. We normalized these metrics based on the obtained distributions from a research trial. We discuss the existing correlations between these behaviors and metrics.
ISSN:1059-0145
1573-1839
DOI:10.1007/s10956-020-09826-8