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 News & Events

American Society of Engineering Education Northeast 2017

May 30, 2017

TCR’s Center for Advanced Research on Educational Assessment Technologies presented an invited paper on “What Does it Take to Jump-start Diversity Pipelines in Engineering Disciplines? Case Study: A Diversity Survey for Root Cause Analysis”

Abstract– This paper examines data on the participation of women and underrepresented minorities in science and engineering majors and careers, and what this portends for the future. The role and limitations of college diversity surveys as a tool to identify root cause factors — which hinder and promote participation in science and engineering majors — is explored. A case study, based on a survey of undergraduate students attending a Connecticut-based engineering university, is presented. Crafting a survey requires a judicious balance of practical and theoretical considerations, including: costs; ethical and privacy concerns; survey design; reliability and validity testing; sample selection; handling of missing data; and statistical significance and hypothesis testing. This paper offers a blueprint to address these considerations through various phases of the survey process: i) design of survey instrument; ii) dissemination of survey; iii) collection of survey data; iv) analysis of survey data; v) and visualization and reporting of survey data and results. Root cause analysis builds on survey findings, and provides insights on what is needed on a societal and individual level to engage students in science and engineering studies. Many surveys of varying origin, scope, and quality are being used to collect data on students’ pathways through school and careers. It is a challenge to leverage these various data sources to inform evidence-based interventions and policies that will help students with different needs achieve their potential. In large measure, this reflects the lack of a holistic, systems perspective. Innovations in industrial engineering have helped corporations achieve vast improvements in quality and productivity using scientific methods and data-driven optimization. These approaches have broad applicability in education and in the workplace.

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