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This document discusses the draft guidelines for statistics education, highlighting the importance of statistics as a process, the use of graphical displays and numerical summaries, and the connections between statistics, mathematics, and computer science. It also raises questions about the inclusion of the modern Bayesian approach and the impact of these guidelines on introductory college-level courses. The dissemination plan includes targeting ASA members, K-12 teachers and administrators, non-ASA members teaching introductory college courses, and college math education professors.
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Comments on Draft Guidelines for Assessment and Instruction in Statistics Education (GAISE) Jim Landwehr Avaya Labs August 10, 2004
My perspective: statistician in industry with longstanding interests in statistical education, especially K-12. Overall, document emphasizes: • Statistics as a process to answer questions (not as a big bag of formulas) • Use of relatively limited set of graphical displays and numerical summaries and algorithms • Continued use of techniques learned in earlier grades on more sophisticated problems in later grades • Limited role of probability, focusing on aspects of it that are important for inference My general reaction: agree entirely with the principal points and most of the details.
Some related comments on topics not (yet?) addressed in the Guidelines Discuss connections between statistics and mathematics and computer science in the world today, and how these should impact statistics education. What about modern Bayesian approach to problems? • Often computationally intensive, topics of much current research interest. • What, if any role through introductory college level?
For introductory college level: • What if anything has changed from Cobb ’92, or is this report mainly further fleshed out now? • What should we do: • If K-12 guidelines really were achieved? • For students getting 4 or 5 on AP Stat Exam? • What about a multi-week unit in some course focusing on the relationships between statistics, mathematics, and computer science: how they’ve changed over time, and how they’ve affected all three disciplines? Terminology: statistics, data analysis, quantitative literacy, …
Dissemination • What are the audiences and how to influence them? • ASA members who teach intro college • K-12 teachers • K-12 administrators • Non-ASA members who teach intro college • College math education professors … • Have a good executive summary, 4-5 pages max, and test it on the desired audiences • Approval process through ASA and other associations • Use ASA and other websites. • Advertising?