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Enhancing Statistics Education through Service Learning at Gainesville State College

This project integrates service learning into introductory statistics courses at Gainesville State College, focusing on statistical thinking and real-world applications. Students work with local agencies, employing the GAISE Process to formulate questions, collect and analyze data, and interpret results beneficial to the community. The course promotes active learning with Minitab software, and includes both individual and group assessments. Feedback from students reflects increased comprehension, effective communication in statistical language, and recognition of statistics' real-world relevance.

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Enhancing Statistics Education through Service Learning at Gainesville State College

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  1. SYNTHESIS THROUGHSERVICE LEARNING IN STATISTICS Gina Reed Gainesville State College

  2. ASA/MAA Recommendations • Emphasize the elements of statistical thinking • Incorporate more data and concepts • Foster active learning

  3. GAISE Process • Formulate Statistical Question • Collect Data • Analyze Data • Interpret Results

  4. Definition of Service Learning in Statistics • A concrete application of statistical methods using real data with the analysis and interpretation that is useful to a community agency

  5. Course Level • Introductory Statistics • Introductory Honors Statistics • Data Analysis and Probability For Elementary Education

  6. Necessary Technology • Minitab Software • Graphing Calculator • Word

  7. Potential Client Pool • United Way agencies • Community groups • K-12 schools • Local businesses

  8. Initial Client Contact • Professor • Student

  9. Client Selection • Suitability (mutually beneficial) • Instrument Development • Data Collection • Data Analysis

  10. Project Description • Statistical Question • Clarify the problem • Question formulation must be answered using data • Data Collection • Design a plan • Employ plan

  11. Project Description • Data Analysis • Select appropriate graphs and numeric measures • Interpretation • Relate interpretation to answering the original question • Executive Summary • Written report • Oral report

  12. Labs and Class Activities • Measures of Central Tendency • Measures of Variation • Graphs (Histograms, Bar Graphs, Scatterplots, etc.) • Correlation (if feasible) • Confidence Intervals • Hypothesis Testing

  13. Timeline • First day of class-General description • Fourth week-Initial contact and questions • Eighth week-Data collection • Twelfth week-Preliminary written report • Sixteenth week • Final written report • Oral presentation

  14. Grading • Individual grade • Written component • Oral component • Group grade

  15. Group Member Evaluation • Please list each member’s name, including yourself, and evaluate them on scale of 0 to 100 in each category. • Initial Planning • Organization skills • Leadership • Attendance at meetings • Data entry • Minitab participation • Written report participation • Oral report preparation • Overall performance • Additional comments:

  16. Student Reflective Process • Necessary component of service learning • Written • Oral

  17. Considerations • Pre-semester contact • Reconfiguration of curriculum • Agency’s priorities • Post-semester contact

  18. Impact • Course • Students

  19. Student Evaluation of Project • I better understood the material covered in the course • I can write more effectively using statistical language. • I am more competent using technological tools (i.e. calculators, computer software). • I can see the "real world" applications of statistics. • Is a positive addition to the course. • What did you like best about the project? • What did you like least about the project?

  20. Grading rubric for written/oral reports • Problem Identification/Question Formulation • Clear description of data collection  • Clear description of data analysis •  Analysis/ Interpretation technically correct •  Readability of visual aids •  Transitions •  Closing •  Executive Summary

  21. Resources • Mathematics In Service To The Community: Concepts and Models for Service-Learning in Mathematical Sciences , MAA Notes #66, Charles Hadlock, Editor • Guidelines for Assessment and Instruction in Statistics Education (GAISE) Report, ASA, Franklin et al., 2007 http://www.amstat.org/education/gaise/

  22. Contact Information greed@gsc.edu

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