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Automated Analytic Dataset Generation and Assessment for Engineering Analytics Education

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Conference

2023 ASEE Annual Conference & Exposition

Location

Baltimore , Maryland

Publication Date

June 25, 2023

Start Date

June 25, 2023

End Date

June 28, 2023

Conference Session

Industrial Engineering Division (IND) Technical Session 4

Tagged Division

Industrial Engineering Division (IND)

Page Count

11

DOI

10.18260/1-2--42350

Permanent URL

https://peer.asee.org/42350

Download Count

233

Paper Authors

biography

Bruce Wilcox University of Southern California

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Dr. Wilcox is a senior analytics consultant with over 30 years years experience with top-tier consulting firms providing management and information systems consulting services to large corporate and government clients. From 2013 until 2021, he was employed full-time by the SAS Institute, a premier provider of advanced analytics software and consulting services, responsible for consulting with major SAS government clients in California on the use of advanced analytics tools and solutions.

With BS and MS degrees in Electrical Engineering from Carnegie-Mellon University and an MBA from UCLA, Bruce earned his PhD in Engineering and Applied Mathematics from Claremont Graduate University in 2018 with thesis work in time series clustering for fixed income portfolio diversification using model-based clustering and state space analysis techniques. Since January 2020 until taking a full-time faculty position in the 2021 fall semester, Bruce was a part-time lecturer at USC Vitterbi College of Engineering in the MS Analytics program. Prior to that, he taught for twelve semesters as a lecturer at California State University, Long Beach and as a visiting international professor at the National Economics University, Hanoi, Vietnam, teaching two short courses in quantitative analysis to advanced finance students.

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Yufan Fei

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Jihao LI University of Southern California

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Junqiang Wang

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Junmeng Xu University of Southern California

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Abstract

In recent years, there has been significant growth in analytics programs at the undergraduate and masters’ levels in Industrial and Systems Engineering (ISE) departments at universities across the country. These programs create new challenges for ISE faculty because they involve the teaching of an interdisciplinary blend of software engineering, statistics, simulation, optimization, and business analysis disciplines. Analytics courses frequently involve significant amounts of programming assignments to generate, assess, and refine predictive models and extensive use of sample datasets for both teaching and assessment purposes. When teaching analytics techniques, especially predictive analytics, instructors are always looking for datasets that contain statistical characteristics that we want to discuss including multi-collinearity, interaction effects between variables, skewed distributions, and nonlinear relationships between predictor and response variables. Instructors generally must either search for existing datasets that have these attributes or create them “manually” using programmatic techniques. This paper describes work done to develop an academic toolset to permit instructors to specify the statistical properties desired in an analytic dataset (using a newly defined high-level dataset specification language), to generate multiple, randomized versions of this dataset (using a newly developed Python library), to provide automation for creating individualized datasets for each student (to avoid inappropriate collaboration on assignments and take-home exams among students), and to provide for automated grading support for assignments and examinations.

Wilcox, B., & Fei, Y., & LI, J., & Wang, J., & Xu, J. (2023, June), Automated Analytic Dataset Generation and Assessment for Engineering Analytics Education Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--42350

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