Using Authentic Discovery Projects to Improve Student Outcomes in Statistics

1. Using Authentic Discovery Projects to Improve Student Outcomes in Statistics Joint Mathematics Meetings January 16, 2010 Dianna Spence Brad Bailey Robb Sinn Department of Mathematics & Computer Science North Georgia College & State University Dahlonega, Georgia

2. Agenda • Overview of Project Scope and Activities – Dianna • Findings from Phase I – Dianna • Pilot Instructor Experiences and Observations – Brad • Future Directions – Brad

3. NSF Grant Project Overview • NSF CCLI Phase I Grant: “Authentic, Career-Specific Discovery Learning Projects in Introductory Statistics” • Goals: Increase students’... • knowledge & comprehension of statistics • perceived usefulness of statistics • self-beliefs about ability to use and understand statistics • Tasks: • Develop Instructional Materials • Develop Instruments • Measure Effectiveness

4. Student Projects • t-tests • Variables • student selects • Designs • Independent samples • Dependent samples • Linear regression • Variables • student selects • often survey based constructs • Survey design • Sampling • Regression analysis

5. Materials Developed(Web-Based) Instructor Guide Project overview Timelines Best practices Student handouts Evaluation rubrics • Student Guide • Project Guide • Help for each project phase • Technology Guide • Variables and Constructs

6. Instruments Developed: Content Knowledge • Instrument • 21 multiple choice items • KR-20 analysis: score = 0.63 • Exploratory Results • treatmentgroup significantly higher (p < .0001) • effect size = 0.59 • Instrument shortened to 18 items for main pilot

7. Instruments Developed: Perceived Usefulness of Statistics • Instrument • 12-item Likert style survey; 6-point scale • Cronbach alpha = 0.93 • Exploratory Results • treatment group significantly higher (p < .01) • effect size = 0.295 • Instrument unchanged for main pilot

8. Instruments Developed: Statistics Self-Efficacy • Beliefs in ability to use and understand statistics • Instrument • 15-item Likert style survey; 6-point scale • Cronbach alpha = 0.95 • Exploratory Results • gains realized, but not significant (1-tailed p = .1045) • effect size = 0.15 • Instrument unchanged for main pilot

9. Phase I Research: Pilot of Developed Materials • 3 institutions • university (3 instructors) • 2-year college (1 instructor) • high school (1 instructor) • Quasi-Experimental Design • Spring 2008: Begin instructor “control” groups • Fall 08 - Fall 09: “Experimental” groups

10. Results: t-Tests • Perceived Usefulness • Pretest: 50.42 • Posttest: 51.40 • Significance: p = 0.208 • Self-Efficacy for Statistics • Pretest: 59.64 • Posttest: 62.57 • Significance: p = 0.032** • Content Knowledge • Pretest: 6.78 • Posttest: 7.21 • Significance: p = 0.088*

11. Subscales: Statistics Self-Efficacy • Strong Gains • SE for Regression Techniques ( p = 0.035 ) • SE for General Statistical Tasks ( p = 0.018 ) • Little or No Improvement • SE for t-test Techniques ( p = 0.308 )

12. Subscales: Content Knowledge • Regression Techniques • Moderate Gains ( p = 0.086 ) • T-test Usage • Moderate Gains ( p = 0.097 ) • T-test Inference • No Gain

13. Multivariate Analysis: Content Knowledge

14. Multivariate Analysis: Statistics Self-Efficacy

15. Instructor Experiences and Observations Best Practices Have Intermediate goals Defined deliverables and project phases Student accountability at each phase Requirements for final report outline template prior work samples

16. Instructor Experiences and Observations Students need guidance with research question Set Student Expectations Students underestimate time/effort required Students often unclear on exactly what to do once they have collected the data Students should be prepared for results that may be weak, non-significant, etc. realistic view of statistics avoid too much disappointment

17. Student Feedback Student Quotes Shared by Instructors “While our results did not meet our initial expectations, this is not an utter disappointment. Before this project, statistics looked simple enough for anyone to sit down and do, but now it is evident that it requires more creativity and critical thinking than initially expected. Overall, it was an edifying experience.” “The main thing that we have learned is that statistics take time. They cannot be conjured up by a few formulas in a few minutes. The time and effort that is put into a small research project such as this is significant. On a large scale, one can quickly understand the kind of commitment of money and time that is required just to obtain reasonable data.”

18. Future Directions NSF CCLI Type II Grant Proposal Submitted January 2010 Goals Include: Nation wide pilot Vertical Integration to early secondary Revisions to Materials Increased flexibility Accommodate early high school grades Qualitative Component More insight into instructor impact Advisory Panel of Statisticians & Educators

19. For more information • Project Website • http://radar. northgeorgia.edu/~djspence/nsf/ • Instructional Materials Home • http://radar.northgeorgia.edu/~rsinn/nsf/ • Contact Us • Dianna: dianna.spence@northgeorgia.edu • Brad: brad.bailey@northgeorgia.edu • Robb: robb.sinn@northgeorgia.edu