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

\bibitem{asee_peer_42350}
  Wilcox, B., \& Fei, Y., \& LI, J., \& Wang, J., \& Xu, J. (2023, June), \emph{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

Bruce Wilcox, Yufan Fei, Jihao LI, Junqiang Wang, and Junmeng Xu.  "Automated Analytic Dataset Generation and Assessment for Engineering Analytics Education".  2023 ASEE Annual Conference & Exposition, Baltimore , Maryland, 2023, June.  ASEE Conferences, 2023.  https://strategy.asee.org/42350 Internet.  13 May, 2024

\bibitem{asee_peer_42350}
  Bruce Wilcox, Yufan Fei, Jihao LI, Junqiang Wang, and Junmeng Xu.
  "Automated Analytic Dataset Generation and Assessment for Engineering Analytics Education".
  \emph{2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland, 2023, June}.
  ASEE Conferences, 2023.
  https://strategy.asee.org/42350 Internet.  13 May, 2024

@INPROCEEDINGS{asee_peer_42350
author = "Bruce Wilcox, Yufan Fei, Jihao LI, Junqiang Wang, and Junmeng Xu"
title = "Automated Analytic Dataset Generation and Assessment for Engineering Analytics Education"
booktitle = "2023 ASEE Annual Conference \& Exposition"
year = "2023"
month = "June"
address = "Baltimore , Maryland"
publisher = "ASEE Conferences"
note = {https://strategy.asee.org/42350}
number = {10.18260/1-2--42350}
}

TY  - CPAPER
AB  - 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.  

AU  - Bruce Wilcox
AU  - Yufan Fei
AU  - Jihao LI
AU  - Junqiang Wang
AU  - Junmeng Xu
CY  - Baltimore , Maryland
DA  - 2023/06/25
PB  - ASEE Conferences
TI  - Automated Analytic Dataset Generation and Assessment for Engineering Analytics Education
UR  - https://strategy.asee.org/42350
DO  - 10.18260/1-2--42350
ER  -