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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.html<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
Management
<https://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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