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Using a Digital Library to Teach Statistics

ICTCM, March 2006. Using a Digital Library to Teach Statistics. Using a Digital Library to Teach Statistics. Lisa Bloomer Green, Ph. D. Scott McDaniel, Ed. D. Ginger Holmes Rowell, Ph. D. Megan Hall*, Ileah McKee* *Undergraduate Students Middle Tennessee State University. Outline.

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Using a Digital Library to Teach Statistics

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  1. ICTCM, March 2006 Using a Digital Library to Teach Statistics

  2. Using a Digital Library to Teach Statistics Lisa Bloomer Green, Ph. D. Scott McDaniel, Ed. D. Ginger Holmes Rowell, Ph. D. Megan Hall*, Ileah McKee* *Undergraduate Students Middle Tennessee State University

  3. Outline CAUSE and CAUSEweb Using Internet Resources throughout the Learning Cycle Specific Examples & Activities Your Ideas

  4. CAUSE Consortium for the Advancement of Undergraduate Statistics Education

  5. CAUSEweb: Resources Digital Library for Undergraduate Statistics Education www.CAUSEweb.org

  6. CAUSEweb Searching: Browse Categories Lecture Examples Laboratories Out-of-class Teaching Tips Datasets Analysis Tools Curriculum Humor Building Blocks Multimedia

  7. Browsing by Statistical Topic

  8. CAUSEweb Advanced Search

  9. Advanced Searching Conditional probability Keyword Filter by math level Hypothesis testing Keyword Math level Lecture presentation

  10. A Teaching Example Applet + Activity Teacher’s View: = a “Ready-To-Go” Classroom Lesson

  11. Finding “Related” Items Related (or Companion) Items are Identified

  12. Outline CAUSE and CAUSEweb Using Internet Resources throughout the Learning Cycle Specific Examples Your Ideas

  13. PowerPoint slides • Reference Materials • History • Demonstrations: • Applets • Videos • Simulations • Data Sets • Case Studies • Guided Demos • Simulations • Laboratories • Applets • Tutorials Explore Lecture Assess • Teacher-directed assessments • Laboratories • Homework Problems • Self-directed assessments • Immediate Feedback

  14. Outline CAUSE and CAUSEweb Using Internet Resources throughout the Learning Cycle Specific Examples Your Ideas

  15. Lecture Explore Assess To help students understand the use of conditional probability, a teacher can lead a demonstration of the Monty Hall Problem using one of the many available Internet applets. Tutorials, such as Mrs. Glosser’s Math Goodies Lesson on Conditional Probability, can be given to students who miss a class or students that would like additional instruction. In some self-assessment tools like Trees and Conditional Probability from Tutorials for Finite Math, students receive immediate feedback when answering questions including hints for incorrect answers and steps to correct solutions.

  16. Lecture Explore Assess To help students understand the use of conditional probability, a teacher can lead a demonstration of the Monty Hall Problem using one of the many available Internet applets.

  17. http://www.shodor.org/~rcogan/interactivate/activities/montynew/index.htmlhttp://www.shodor.org/~rcogan/interactivate/activities/montynew/index.html Whatever applet we choose goes here. Pause to do a worksheet. Applet

  18. Lecture Explore Assess To help students understand the use of conditional probability, a teacher can lead a demonstration of the Monty Hall Problem using one of the many available Internet applets. Tutorials, such as Mrs. Glosser’s Math Goodies Lesson on Conditional Probability, can be given to students who miss a class or students that would like additional instruction.

  19. http://www.mathgoodies.com/lessons/vol6/conditional.html

  20. Lecture Explore Assess To help students understand the use of conditional probability, a teacher can lead a demonstration of the Monty Hall Problem using one of the many available Internet applets. Tutorials, such as Mrs. Glosser’s Math Goodies Lesson on Conditional Probability, can be given to students who miss a class or students that would like additional instruction. In some self-assessment tools like Trees and Conditional Probability from Tutorials for Finite Math, students receive immediate feedback when answering questions including hints for incorrect answers and steps to correct solutions.

  21. http://people.hofstra.edu/faculty/Stefan_Waner/RealWorld/tutorialsf3/frames6_5B.htmlhttp://people.hofstra.edu/faculty/Stefan_Waner/RealWorld/tutorialsf3/frames6_5B.html

  22. http://ocw.mit.edu/OcwWeb/Mathematics/18-05Spring-2005/CourseHome/index.htmhttp://ocw.mit.edu/OcwWeb/Mathematics/18-05Spring-2005/CourseHome/index.htm

  23. Lecture Explore Assess Introduction to Probability and Statistics

  24. Lecture Explore Assess This slide from Sampling Distributions can help a teacher guide a discussion on when the sample mean can be assumed to be normally distributed. This applet from Statistical Java demonstrates the convergence implied by the central limit theorem by allowing students to manipulate sample size, number of samples, and underlying distribution. This guided applet activity from Wise includes assessment questions that, when answered correctly, allow students to proceed to new concepts.

  25. Lecture Explore Assess This slide from Sampling Distributions can help a teacher guide a discussion on when the sample mean can be assumed to be normally distributed.

  26. http://www.stat.psu.edu/~resources/ClassNotes/hrm_08/sld018.htmhttp://www.stat.psu.edu/~resources/ClassNotes/hrm_08/sld018.htm

  27. Lecture Explore Assess This slide from Sampling Distributions can help a teacher guide a discussion on when the sample mean can be assumed to be normally distributed. This applet from Statistical Java demonstrates the convergence implied by the central limit theorem by allowing students to manipulate sample size, number of samples, and underlying distribution.

  28. Applet http://kitchen.stat.vt.edu/~sundar/java/applets/CLTApplet.html Applet

  29. Lecture Explore Assess This slide from Sampling Distributions can help a teacher guide a discussion on when the sample mean can be assumed to be normally distributed. This applet from Statistical Java demonstrates the convergence implied by the central limit theorem by allowing students to manipulate sample size, number of samples, and underlying distribution. This guided applet activity from Wise includes assessment questions that, when answered correctly, allow students to proceed to new concepts.

  30. Using StatCrunch http://www.statcrunch.com/

  31. Using StatCrunch Data -> Simulate Data -> Normal Graphics -> Histogram

  32. Using StatCrunch Try: • Load sample data • Create a scatterplot • Calculate summary statistics • Calculate a T-statistic value

  33. Lecture Explore Assess An in-class activity from the STAR Library can help introduce the concept of regression. Applets like this one from SticiGui Java Tools allow students to explore changes in the data to see how they affect the regression line. Using self-tests like this one from Biometry: Statistics for Ecology, students can submit their answers and receive immediate feedback regarding questions answered correctly and questions or topics needing improvement.

  34. Lecture Explore Assess An in-class activity from the STAR Library can help introduce the concept of regression.

  35. http://www.causeweb.org/repository/StarLibrary/activities/buskirk_young2001/http://www.causeweb.org/repository/StarLibrary/activities/buskirk_young2001/

  36. Insert data into TI-83 http://www.cvgs.k12.va.us/DIGSTATS/Sitemap.html

  37. Use StatCrunch • Use StatCrunch to determine the Regression line for the rebound height.

  38. Lecture Explore Assess An in-class activity from the STAR Library can help introduce the concept of regression. Applets like this one from SticiGui Java Tools allow students to explore changes in the data to see how they affect the regression line.

  39. Regression http://stat-www.berkeley.edu/~stark/Java/Html/Correlation.htm applet

  40. Lecture Explore Assess An in-class activity from the STAR Library can help introduce the concept of regression. Applets like this one from SticiGui Java Tools allow students to explore changes in the data to see how they affect the regression line. Using self-tests like this one from Biometry: Statistics for Ecology, students can submit their answers and receive immediate feedback regarding questions answered correctly and questions or topics needing improvement.

  41. http://aerg.canberra.edu.au/cgi-bin/tiegen?/share/www/envirostats/bm/L6/ffq12.tiehttp://aerg.canberra.edu.au/cgi-bin/tiegen?/share/www/envirostats/bm/L6/ffq12.tie

  42. Lecture Explore Assess DIGSTATS contains examples and data sets, like the one below about Acid Rain. The Intuitor site contains an introduction to hypothesis testing, relating it to what happens at a criminal trial. The ARTIST database contains questions classified by topic and learning outcomes.

  43. Lecture Explore Assess The Intuitor site contains an introduction to hypothesis testing, relating it to what happens at a criminal trial.

  44. http://www.intuitor.com/statistics/T1T2Errors.html

  45. Lecture Explore Assess DIGSTATS contains examples and data sets, like the one below about Acid Rain. The Intuitor site contains an introduction to hypothesis testing, relating it to what happens at a criminal trial.

  46. http://www.cvgs.k12.va.us/DIGSTATS/Sitemap.html

  47. Acid Rain Activity

  48. Evaluate Acid Rain Data using StatCrunch Data -> Load Data -> from paste • Stat -> T Statistics -> One sample

  49. Lecture Explore Assess DIGSTATS contains examples and data sets, like the one below about Acid Rain. The Intuitor site contains an introduction to hypothesis testing, relating it to what happens at a criminal trial. The ARTIST database contains questions classified by topic and learning outcomes.

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