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Mere Renovation is Too Little Too Late: We Need to Rethink our Undergraduate Curriculum from the Ground Up

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  • George Cobb

Abstract

The last half-dozen years have seen The American Statistician publish well-argued and provocative calls to change our thinking about statistics and how we teach it, among them Brown and Kass, Nolan and Temple-Lang, and Legler et al. Within this past year, the ASA has issued a new and comprehensive set of guidelines for undergraduate programs (ASA, Curriculum Guidelines for Undergraduate Programs in Statistical Science ). Accepting (and applauding) all this as background, the current article argues the need to rethink our curriculum from the ground up, and offers five principles and two caveats intended to help us along the path toward a new synthesis. These principles and caveats rest on my sense of three parallel evolutions: the convergence of trends in the roles of mathematics, computation, and context within statistics education. These ongoing changes, together with the articles cited above and the seminal provocation by Leo Breiman call for a deep rethinking of what we teach to undergraduates. In particular, following Brown and Kass, we should put priority on two goals, to make “fundamental concepts accessible” and to “minimize prerequisites to research.”[Received December 2014. Revised July 2015]

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  • George Cobb, 2015. "Mere Renovation is Too Little Too Late: We Need to Rethink our Undergraduate Curriculum from the Ground Up," The American Statistician, Taylor & Francis Journals, vol. 69(4), pages 266-282, November.
    • Handle: RePEc:taf:amstat:v:69:y:2015:i:4:p:266-282
    DOI: 10.1080/00031305.2015.1093029
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    Cited by:

    1. Robert A. Stine, 2017. "Explaining Normal Quantile-Quantile Plots Through Animation: The Water-Filling Analogy," The American Statistician, Taylor & Francis Journals, vol. 71(2), pages 145-147, April.
    2. Roger W. Hoerl & Ronald D. Snee, 2017. "Statistical Engineering: An Idea Whose Time Has Come?," The American Statistician, Taylor & Francis Journals, vol. 71(3), pages 209-219, July.
    3. Jeff Witmer, 2017. "Bayes and MCMC for Undergraduates," The American Statistician, Taylor & Francis Journals, vol. 71(3), pages 259-264, July.
    4. Manuela Svoboda, 2022. "Evaluation of Motivation, Expectation, and Present Situation in 3rd Year Undergraduate Students of German Language and Literature at the University of Rijeka, Croatia," European Journal of Education Articles, Revistia Research and Publishing, vol. 5, ejed_v5_i.
    5. Ryan Sterling McCulloch, 2017. "Learning Outcomes in a Laboratory Environment vs. Classroom for Statistics Instruction: An Alternative Approach Using Statistical Software," International Journal of Higher Education, Sciedu Press, vol. 6(5), pages 131-131, October.
    6. Nicholas Jon Horton, 2016. "Discussion: Making Progress in a Crowded Market," International Statistical Review, International Statistical Institute, vol. 84(2), pages 179-181, August.
    7. Chris J. Wild, 2016. "Discussion: Locating Statistics in the World of Finding Out," International Statistical Review, International Statistical Institute, vol. 84(2), pages 194-202, August.
    8. Amy L. Phelps & Kathryn A. Szabat, 2017. "The Current Landscape of Teaching Analytics to Business Students at Institutions of Higher Education: Who is Teaching What?," The American Statistician, Taylor & Francis Journals, vol. 71(2), pages 155-161, April.
    9. Daniel Kaplan, 2018. "Teaching Stats for Data Science," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 89-96, January.

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