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Assessing Internet Resources in Statistics Education

Assessing Internet Resources in Statistics Education. Ginger Holmes Rowell Middle Tennessee State University September 11, 2007. Internet. A Great Source of Statistics Education Learning Resources

philip-mccarthy
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Assessing Internet Resources in Statistics Education

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  1. Assessing Internet Resources in Statistics Education Ginger Holmes Rowell Middle Tennessee State UniversitySeptember 11, 2007

  2. Internet • A Great Source of Statistics Education Learning Resources • interactive applets, videos, tutorials, lesson plans, case studies, engaging learning activities, …

  3. Now what was that URL??? Issues with Internet-basedInstructional Technologies • Limited time for • Finding & evaluating • Selecting & assessing • Amount of information on the Internet can be overwhelming.

  4. Assessing - Technology As teachers, we are accustomed to assessing student learning.

  5. Assessing - Technology Is thatdifferent from assessing technology-based learning resources?

  6. Overview • A General Approach for Assessing Instructional Technologies • Triadic Assessment • Example of Existing Assessments • Digital Libraries • MERLOT/CAUSEweb Peer Review Criteria • Other Resources/Examples

  7. Virtual Laboratories in Probability and Statistics Rice Virtual Lab in Statistics http://illuminations.nctm.org http://www.math.uah.edu/stat/ http://onlinestatbook.com/rvls.html Internet-Based Instructional Technologies: Large Statistics Projects http://www.cvgs.k12.va.us/DIGSTATS/

  8. General Assessment: Instructional Technologies • The TLT Group, Teaching, Learning, and Technology http://www.tltgroup.org/ • Flashlight Program: For the Study and Improvement of Educational Uses of Technology http://www.tltgroup.org/flashlightP.htm

  9. Activity Technology Outcome Technology Assessment • Monad Approach • Dyadic Approach • Triadic Approach Flashlight Project, The TLT Group, Gilbert

  10. Desired Outcome Recognize convergence of empirical distribution to theoretical function. Visualize shape of binomial distribution for different values of n and p. Select Activity Simulate a binomial experiment 10, 100 & 1000 times. Vary n and p. Select Technology 10 Simulations – Low Tech 1000 Simulations – High Tech (Computer Simulation) Assessing TechnologyAn Example

  11. Binomial Distribution Simulation(n=10, p=0.5) Note the apparent convergence of the relative frequency function (red) to the probability mass function (blue). 10 Repetitions 100 Repetitions 1000 Repetitions http://www.math.uah.edu/stat/bernoulli/Binomial.xhtml

  12. p = 0.5 p = 0.85 p = 0.15 Binomialn=10 Note the change in the shape of the distribution (blue) as p changes. http://www.math.uah.edu/stat/bernoulli/Binomial.xhtml

  13. One Solution for Internet-based Instructional Technologies • Digital Libraries • Organized, Searchable • Reviewed & Assessed (sometimes) • National Science Digital Library (www.nsdl.org)

  14. http://www.merlot.org/ http://www.causeweb.org http://www.mathdl.org/ http://www.smete.org/ Digital Library Projects (Science, Math, Engineering & Technology Education)

  15. Not just for statistics An established assessment process http://www.merlot.org/ http://www.causeweb.org MERLOT & CAUSEweb • Mission: support undergraduate statistics teachers & learners

  16. www.CAUSEweb.org • Getting Started • Guidelines for Research • Readings & Publications • Literature Index • Dissertations • Working Groups and Projects • CAUSEway workshops • USCOTS • Webinars • Future Opportunities (keep checking this website.) • Digital Library for Undergraduate Statistics Education

  17. www.merlot.org

  18. www.merlot.org

  19. MERLOT Statistics • MERLOT Statistics and CAUSEweb share an Editorial Board • Roger Woodard, Editor editor@causeweb.org • Peer reviewed materials appear in both locations & a composite review is shown • Peer reviews help to: • Pass on expert knowledge • Point out possible downsides

  20. MERLOT/CAUSEweb Review Components • Descriptive • All items in MERLOT and CAUSEweb have a descriptive component • Evaluation • Peer-reviewed items have an evaluation component • Evaluations are shown on the record • MERLOT has a “star” rating system

  21. Review Component: Description • Overview • Type of Material • Technical Requirements • Learning Goals* • Recommended Uses* • Target Student Population • Prerequisites* *only required on peer reviewed items

  22. Review Component: Evaluation • Content Quality • Potential Effectiveness as a Teaching Tool • Ease of Use • Issues and Comments

  23. CAUSEweb/MERLOT Review Criteria Overview • Quality of Content(concepts, models and skills): valid & educationally significant • Likely Effectiveness:improves ability to learn material, easily integrated, learning goals easily identifiable, conducive for writing good learning assignments, promotes/uses effective learning strategies • Ease of Use: easy first time use, consistent appearance, clear instructions, not limited by technical resources

  24. Content Quality • Does the item present valid concepts, models, and skills? • Is the information presented by the item factually correct? • Does the item use appropriate vocabulary? • Does the information presented by the item follow generally accepted notation? • Does the item encourage appropriate statistical practice? • Does the item integrate graphics and multimedia when appropriate?

  25. Content Quality • Does the item present educationally significant concepts, models, and skills? • Does the item help develop conceptual understanding of statistics? • Is the item non-trivial? Is the level of understanding obtained relative to the amount of time required? • Is the item dealing with an important topic? • Does it deal with a topic that students typically find challenging?

  26. Potential Effectiveness as a Teaching Tool • Are the teaching-learning goals easy to identify? • Can the item be readily integrated into a statistics course or curriculum? • Does the item fit into standard presentation of statistics courses? • Can the item be used with standard texts?

  27. Potential Effectiveness as a Teaching Tool • Does the item promote and/or use effective learning strategies? • Does the item promote active engagement? • Does the item help develop critical thinking skills? • Will the item promote student discovery? • Can learning be readily assessed?

  28. Ease of Use • How easy is the item to use for the first time? • Does the item have a consistent feel and appearance? • Does the item have clear instructions? • Is the item convenient and inviting to use? • Does the item provide an effective feedback mechanism? • Can the item be used by individuals with a variety of backgrounds/technical skills? • Is technical support necessary? • Is the item limited by technical resources such as Internet connection speed or special plug-ins?

  29. Interested in helping Peer Review for CAUSEweb/MERLOT?Contact: editor@causeweb.org

  30. Other Technology Assessment Resources & Examples • Network for the Evaluation of Education and Training Technologies • Comparison Terms • Example: Robert DelMas paper/software • ARTIST • Great Resource for Statistics Assessment • On-line Introductory Statistics Test Builder

  31. General Assessment:Instructional Technologies Network for the Evaluation of Education and Training Technologies (EvNet) • National multi-discipline, multi-sector network • Committed to improving instructional technologies (used in Canadian education) through research focused on assessment and evaluation http://socserv2.socsci.mcmaster.ca/srnet/exsum.htm

  32. Best Practice Labels Warmware Inclusive Access Equity User Control Learner Driven Knowledge building Meaningful Interaction Multidimensional … Worst Practice Labels Coldware Blocks access Expensive/costly Loss of Control Destructive of collaborative learning No interactivity … Technology Assessment:EvNet Best/Worst Practices EvNet, p. 2-3 http://socserv2.socsci.mcmaster.ca/srnet/exsum.htm

  33. Best Practices Example • Robert delMas: develop & assess software • Software: Sampling Distribution • Objective: students explore simulations of sampling distributions to learn and/or discover Central Limit Theorem concepts • Assessment: combine existing research models (Nickerson and Holland et al. "Inductive Reasoning Model") list features to promote understanding (1996 ISAE Roundtable Conference on the Role of Technology)

  34. https://ore.gen.umn.edu/artist/index.html ARTIST: Assessment Resource Tools for Improving Statistical Thinking

  35. https://ore.gen.umn.edu/artist/index.html ARTIST Assessment Builder (for Introductory Statistics) Free login

  36. References • Cuneo, Carl. "Twenty Criteria for Evaluating Instructional Technologies: From Best to Worst Practices." EvNet Website. http://socserv2.mcmaster.ca/srnet/tools/tktoc.htm Retrieved July 26, 2004. • delMas, Robert. "A Framework for the Evaluation of Software for Teaching Statistical Concepts" 1996 IASE Roundtable Conference Proceedings. Website Version. http://www.stat.Auckland.ac.nz/~iase/php?show=8. PDF file 7.delMas.pdf. Retrieved July 15, 2004. • EvNet, Network for the Evaluation of Training and Technology. http://socserv2.socsci.mcmaster.ca/srnet/exsum.htm Retrieved July 23, 2004.

  37. References Continued • Garfield, Joan. "Preface" to 1996 IASE Roundtable Conference Proceedings. Website Version. http://www.stat.Auckland.ac.nz/~iase/php?show=8. PDF file iforward.pdf. Retrieved July 23, 2004. • Gilbert, Steven. "What is a Triad?" PowerPoint Presentation. The Teaching, Learning, and Technology Group. http://www.tltgroup.org/media/fl/Triad.htm Retrieved July 2002. • Gilbert, Steven. The Teaching, Learning, and Technology Group. http://www.tltgroup.org/resources/TranslucentTechnologies5-08-02.htm Retrieved July 2002. • Shaughnessy, J. Michael. "Discussion: Empirical Research On Technology And Teaching Statistics" 1996 IASE Roundtable Conference Proceedings. Website Version. http://www.stat.Auckland.ac.nz/~iase/php?show=8. PDF file 17.shaughnessy.pdf. Retrieved July 23, 2004.

  38. References Continued • Siegrist, Kyle. "The Binomial Coin Experiment." Virtual Laboratories in Probability and Statistics, 1997-2004. University of Alabama in Huntsville. http://www.math.uah.edu/stat/ Retrieved June 2002. • The Teaching, Learning, and Technology (TLT) Group. Information Page. The TLT Group. http://www.tltgroup.org/ Retrieved July 2002. • Valdez, Gilbert. "Evaluation Standards and Criteria for Technology Implementation," North Central Regional Educational Laboratory. http://www.ncrel.org/tandl/eval_standards_and_criteria.htm Retrieved July 23, 2004.

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