-------- Forwarded Message -------- Subject: [AISWorld] SI CFP: Teaching Data Analytics and Statistics Date: Thu, 31 Dec 2020 18:14:05 +0000 From: George, Jordana jgeorge@mays.tamu.edu To: aisworld (aisworld@lists.aisnet.org) aisworld@lists.aisnet.org

Special Issue Call for Papers: Teaching Data Analytics and Statistics

Decision Sciences Journal of Innovative Education Practitioners today need business analytics-based tools to help make data driven decisions (Hazen et al., 2016). At the undergraduate and MBA levels, broadly speaking, this means learning to interpret data analysis and predictive modeling output, understanding what large datasets bring to the table, and respecting the pitfalls of overfitting among other potential dangers. The Decision Sciences Journal on Innovative Education (DSJIE) is seeking submissions for a special issue dedicated to teaching data analytics and statistics. DSJIE is interested in novel online, hybrid, or face-to-face classroom methods and exercises that show evidence-based AACSB learning outcome improvements in data visualization, statistics, and predictive analytics. Pilot studies/in-class experiments that have shown promise are especially sought after. DSJIE is especially interested in teaching briefs for this special issue but will accept longer conceptual and empirical papers (please reference https://onlinelibrary.wiley.com/page/journal/15404609/homepage/forauthors.ht...https://urldefense.com/v3/__https:/onlinelibrary.wiley.com/page/journal/15404609/homepage/forauthors.html__;!!KwNVnqRv!T8W159a8VP1rHu1Xzcx8N32ST_WgosWHu7WKAPDrdjapKgdmRi4Gq7piaS701Qo-xw$ for definitions of article formats). All submissions must include a literature review and teaching briefs should provide appropriate evidentiary-based results from classroom testing. We welcome submissions that delineate novel methods and/or approaches to undergraduate or MBA-level business statistics and data analytics. We are especially interested in submissions that demonstrate “moving the needle” on AACSB/ABET core-learning outcomes. The breadth of topics for the special issue include but are not limited to:

• Evidence-based improvements on learning outcomes for statistics and data analytics courses;

• Pilot studies and experiments with in-class teaching methods, and;

• In-class projects or exercises that lead to higher-order skills on Bloom’s taxonomy. Review Process and Publication Timeline: Manuscript submissions: 30 June 2021 Initial first-round decisions: 31 August 2021 Revised paper resubmissions: 31 September 2021 Final acceptance decisions: 30 November 2021 Publication: January 2022 Special Issue Guest Editors: Michelle Hutnik (mzh17@psu.edu) joined Penn State’s Office of the Senior Vice President for Research in 2015. As Director of Research Analytics and Communications, her primary responsibilities include formalizing and strengthening Penn State’s ability to identify and showcase the University’s research strengths and performance. Dr. Hutnik is an alumnus of Penn State, where she received her Bachelor of Science. She completed a Doctorate of Science in Materials Science and Engineering at the Massachusetts Institute of Technology (MIT). She also completed a postdoctoral fellowship in the department of Chemical Engineering at MIT. Trevor S. Hale (trevor.hale@tamu.edu) is a clinical full Professor of Business Analytics in the Mays Business School at Texas A&M University where he teaches undergraduate business statistics and graduate-level data analytics. Previously, he was a faculty member at University of Houston-Downtown, Ohio University, and Colorado State University-Pueblo. He is the managing co-author of Pearson’s number one textbook in business analytics, Quantitative Analysis for Managementhttps://www.pearson.com/us/higher-education/program/Render-Quantitative-Analysis-for-Management-13th-Edition/PGM335022.html, now in its 13th edition. Dr. Hale is an alumnus of Penn State (B.S.), Northeastern (M.S.), and Texas A&M University where he earned a Ph.D in industrial engineering with an emphasis in operations research. References: Hazen, B. T., Skipper, J. B., Ezell, J. D., and Boone, C. A. (2016) Big data and predictive analytics for supply chain sustainability: A theory-driven research agenda, Computers & Industrial Engineering, 101(592-598), https://doi.org/10.1016/j.cie.2016.06.030.

For more information please contact Trevor S. Hale (trevor.hale@tamu.edu) or Michelle Hutnik (mzh17@psu.edu).

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