Operationalizing Optimization in a Middle School Virtual Engineering Internship

• Published in: Journal 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
• Language: English
• 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
• DOI: 10.1007/s10956-020-09826-8
• ISSN: 1059-0145

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.