1 / 24

CAUSE Webinar

CAUSE Webinar. GAISEing into the Future of Statistics Education Chris Franklin University of Georgia Jessica Utts University of California, Davis November 14, 2006. GAISE. G uidelines for A ssessment and I nstruction in S tatistics E ducation http://www.amstat.org/education/gaise/

cala
Download Presentation

CAUSE Webinar

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. CAUSE Webinar GAISEing into the Future of Statistics Education Chris Franklin University of Georgia Jessica Utts University of California, Davis November 14, 2006

  2. GAISE • Guidelines for Assessment and Instruction in Statistics Education http://www.amstat.org/education/gaise/ • Strategic initiative of ASA. The GAISE documents were endorsed by ASA in 2005. • Provides a Framework for Teaching Statistics • Within the PreK-12 Mathematics Curriculum and • For the College Introductory Course

  3. Guidelines for Teaching and Learning Statistics within the PreK-12 Mathematics Curriculum -- The Pre K-12 GAISE Framework Christine Franklin University of Georgia Department of Statistics chris@stat.uga.edu

  4. PreK-12 Writers and Advisors Writers: Advisors: • Christine Franklin Peter Holmes • Gary Kader Brad Hartlaub • Denise Mewborn Landy Godbold • Jerry Moreno Cliff Konold • Roxy Peck Susan Friel • Mike Perry • Richard Scheaffer

  5. What’s Happening in Statistics Education, PreK-12 • Everyone – students, teachers, parents, employers – interested in data • Data analysis has become a key component of modern PreK-12 mathematics curriculum • NCTM, NAEP, State guidelines -- GA has become a model state with the integration of data in the new GA Performance Standards for mathematics

  6. Current efforts • Curriculum Standards (PSSM) of the National Council of Teachers of Mathematics (NCTM) Data Analysis and Probability (one of the 5 strands) runs throughout the curriculum Advanced Placement Statistics • First exam in 1997 – 7500 exams • In 2006 – 90,000 exams

  7. What’s Needed for the Future • Statistics is a relatively new science that is still developing • Many teachers have not had any opportunity to develop sound knowledge of the principles and practices of data analysis they are now called upon to teach • “Fleshing out” of the NCTM “Standards” is more essential for the statistics strand than for others

  8. GAISE • The goals of the Pre K-12 document are to provide a basic framework for informed Pre K-12 stakeholders that describes what is meant by a statistically literate high school graduate and to provide steps to achieve this goal. • This framework provides a conceptual structure for statistics education which gives a coherent picture of the overall curriculum. This framework supports and complements the objectives of the NCTM PSSM. • A goal in the professional growth of our mathematics teachers should be to give a big picture of statistics and allow implementation of the NCTM Standards in an informed way.

  9. Levels in PreK-12 GAISE • The main content of the Pre K-12 Framework is divided into three levels, A, B, and C that roughly parallel the PreK-5, 6-8, and 9-12 grade bands of the NCTM Standards. • The framework levels are based on experience not age. Check out the ASA/NCTM Committee Statistics Teacher Network Newsletter, Issue 68 (current issue) • http://www.amstat.org/education/stn/

  10. FRAMEWORK FOR STATISTICS EDUCATION Statistical analysis is an investigatory process that turns often loosely formed ideas into scientific studies by: • refining a problem into one or more questions that can be addressed with data • designing a plan to collect appropriate data • analyzing the collected data by graphical and numerical methods, • interpreting the analysis so as to reflect light on the original question. An understanding of variability is crucial for the practice of this process

  11. A Curriculum Framework for Pre K-12 Statistics Education Basic principles around which this Framework revolves can be summarized as: • Both conceptual understanding and procedural skill should be developed deliberately, but conceptual understanding should not be sacrificed for procedural proficiency. • Active learning is key to the development of conceptual understanding. • Real world data must be used wherever possible in statistics education. • Appropriate technology is essential in order to emphasize concepts over calculations These principles continue as the foundation of the College GAISE recommendations.

  12. 0 GAISE College Group Joan Garfield Univ. of Minnesota (Chair) Martha Aliaga ASA George Cobb Mt. Holyoke College Carolyn Cuff Westminster College Rob Gould UCLA Robin Lock St. Lawrence University Tom Moore Grinnell College Allan Rossman Cal Poly San Luis Obispo Bob Stephenson Iowa State Jessica Utts UC Davis Paul Velleman Cornell University Jeff Witmer Oberlin College

  13. 0 The Goal Produce a set of recommendations and guidelines for instruction and assessment in introductory statistics courses at the undergraduate level.

  14. Four Part Report • Introduction and History • Goals for Students in an Introductory Course: What it Means to be Statistically Educated • Six Recommendations for helping teachers achieve those goals • Appendix of Examples and Suggestions

  15. 0 Six Recommendations • Emphasize statistical literacy and develop statistical thinking • Use real data • Stress conceptual understanding rather than mere knowledge of procedures • Foster active learning in the classroom • Use technology for developing conceptual understanding and analyzing data • Integrate assessments that are aligned with course goals to improve as well as evaluate student learning.

  16. Expanding on Recommendations1and2 Emphasize statistical literacy and develop statistical thinking and Use real data • Use scenarios that are familiar to students as well as data of interest to them • Don’t use data out of context of the problem to be solved • Start with a question and model the whole process to arrive at the answer, even if it’s throughout the course and not all in the same day

  17. Using Familiar Scenarios:Wason Selection TaskModified somewhat from this source: http://coglab.wadsworth.com/experiments/WasonSelection.shtml Cards have a letter on one side and a number on the other. Consider the rule: If a card has a B on one side, it has a 21 on the other side. Question: Which card(s) do you need to turn over to verify that the rule holds for all cards? B 16 C 21

  18. Using Familiar Scenarios:Wason Selection Task Cards have a letter on one side and a number on the other. Consider the rule: If a card has a B on one side, it has a 21 on the other side. Question: Which card(s) do you need to turn over to verify that the rule holds for all cards? Answer: B and 16 B 16 C 21

  19. New ExampleSource: The Tipping Point; orig. Professor Leda Cosmides, UCSB The Rule: No one under 21 is allowed to drink alcohol in a bar. There are 4 people drinking in a bar: • One is drinking beer. • One is 16 years old. • One is drinking coke. • One is 21 years old. Question: For which person(s) do we need more information to verify that the law is being upheld?

  20. New ExampleSource: The Tipping Point; orig. Professor Leda Cosmides, UCSB The Rule: No one under 21 is allowed to drink alcohol in a bar. There are 4 people drinking in a bar: • One is drinking beer. • One is 16 years old. • One is drinking coke. • One is 21 years old. Question: For which person(s) do we need more information to verify that the law is being upheld? The person drinking beer and the 16-year old .

  21. The two tasks are the same, but one is a familiar scenario! That makes it easier to understand. Cards have a letter on one side and a number on the other. Consider the rule: If a card has a B on one side, it has a 21 on the other side. Question: Which card(s) do you need to turn over to verify that the rule holds for all cards? Answer: B and 16 Beer Age 16 Coke Age 21

  22. 0 Example of Modeling Statistical Thinking Model statistical thinking for students by presenting examples as questions that need an answer, and showing the statistical process for finding the answer. Work examples from the beginning (the question) to the end (the conclusion). Question of interest: Do men lose more weight by dieting or by exercising regularly? Study done at Stanford, used overweight male volunteers, randomly assigned to one year of diet or exercise. Lost more weight with diet. Useful for illustrating these concepts and processes: • Types of studies (randomized experiment versus observational study) • Design of randomized experiments • When cause and effect can be concluded (or not); it can for this experiment • How to do hypothesis tests, from start to finish • How to construct and interpret a confidence interval

  23. GAISE Recommendations: Making It Happen Start with small steps, for example: • Add an activity to your course • Have your students do a small project • Integrate an applet into a lecture • Demonstrate the use of software to your students • Increase the use of real data sets • Add a case study (newspaper story <-> journal article) • Choose one topic to delete from the list you currently try to cover and using the time saved to focus more on understanding concepts.

  24. QUESTIONS?? http://www.amstat.org/education/gaise chris@stat.uga.edu jmutts@ucdavis.edu

More Related