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Integrating Data Analysis At Berea College Jill Bouma Berea College August 13, 2010

Integrating Data Analysis At Berea College Jill Bouma Berea College August 13, 2010. Integrating Data Analysis at Berea College. Small, liberal arts college, 3-person department Part of NSF Integrating Data Analysis project in 2002 ADVANTAGES at Berea for adding data analysis:

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Integrating Data Analysis At Berea College Jill Bouma Berea College August 13, 2010

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  1. Integrating Data Analysis At Berea College Jill Bouma Berea College August 13, 2010

  2. Integrating Data Analysis at Berea College • Small, liberal arts college, 3-person department • Part of NSF Integrating Data Analysis project in 2002 ADVANTAGES at Berea for adding data analysis: • Small class sizes – 10-25 • students have own laptops DISADVANTAGES: • no TAs • heavy teaching loads Unusual School • only low-income students – all full-scholarship, all work • often come with poor prep and math skills

  3. Teaching Quantitative Skills Until 2002, very little data analysis in courses Saw adding modules as way to • improve quantitative skills • enhance research skills

  4. Emphasis on Modules from DataCounts! website: • Students don’t find own data • But, if set up properly, can include all components of research project: • pose question • review lit • propose hypothesis • analyze data – test IVs on DV • interpret tables and relationships between variables • make conclusion

  5. Example of An Early Learning Experience: Influence of Race and Gender on Income Used in Social Problems class, 100-level course • 20 students in class • Takes four 50-minute class days GOALS: • Learn about race and gender inequality in income • Use census data to make national and state comparisons in terms of earnings module available online at: • http://serc.carleton.edu/sp/ssdan/examples/31584.html

  6. Quantitative Goals of Module • Create and read frequency tables • Learn logic of independent and dependent variables • Create and interpret bivariate tables • “tell a story” about income inequality using data as evidence • Gain confidence using numbers

  7. Day 1: Learn How to Read Frequencies and Tables in a Handout Example: Reading Frequencies: 2000 Full time, year round civilian workers in Kentucky, CPS <15K 15-25k 25-35K 35-50K 50-75K 75K+ 17.3% 24.9% 22.6% 16.7% 10.0% 8.6% Example: Reading tables: Gender differences in Income, KY 2000 (Census) Income Male Female Total <15K 12.1% 24.4% 17.3% 15-25k 19.7% 32.0% 24.9% 25-35K 21.5% 24.1% 22.6% 35-50K 19.7% 12.6% 16.7% 50-75K 14.4% 3.8% 10.0% 75K+ 12.5% 3.2% 8.6% All 789,674 572,908 N = 1,362,582

  8. Day 2: Learn How to Create Tables • Learn independent and dependent variables • Make hypotheses about relationship between variables • Learn how to use SSDAN datasets to run frequencies and set up simple bivariate tables • Learn how to create properly labeled tables from the data generated

  9. Day 3: Learn How to Present Data • Students work in pairs on state of own choosing • 5-minute presentation of findings to class: • Hypothesis (and let others guess) • Table of results • Describe findings with proper language

  10. Day 4: Peer Review of Paper • Students come to class with completed draft of data analysis paper • In pairs, review and edit one another’s papers, following guided prompts • Main goal: students learn to write “story” using data as evidence

  11. Do Students Learn?Assessing the Module Formal Paper Assignment: • Write formal paper examining earnings for US, KY, and state of own choosing • Compare earnings by gender and race • KEY was “telling a story” with numbers • Assessment: C or above indicated competence • all but one earned at least C (after rewrites) • large majority earned As and Bs

  12. Assessment of Module Pre/post Test (MC, T/F, and short answer questions) • Pre-test average: 59 (first day of class) • Post-test average: 94 (part of final exam) • Improved on all questions • Reported higher confidence levels • Could transfer skills to same type of table

  13. Student Feedback about the Writing Assignment/Module Context Statement: With paper, asked to describe: • what learned • what enjoyed • what found most difficult • how affected own sense of confidence with numbers Final: On final exam asked for advice for teaching this next year

  14. Context Statement: What Students Reported Learning • How to read information from charts • How to analyze and interpret data • How to use data for supporting/disproving theories • How to make sense of statistics and numbers • How to tell story about numbers.

  15. Context Statement: What students liked • Fun to compare states • Interesting data • Gaining experience with numbers • Telling a story with data • Learned something new • Gained a new source of power • Enjoyed math and understanding information

  16. Context Statements: What students found most difficult • Making sense of numbers (the most common) • Verbalizing a story • Finishing the paper • unusual task, not like other papers • Needed more time • lot of information to pull together • Working with tabs (computer/formatting issues) “The more I tried to put it together, the more questions I had.” “I found this to be the most difficult paper I have ever written…” (Student who made an A)

  17. Advice from Students (on final) • More practice • More examples • Keep pushing • Keep encouraging

  18. Sample Quotes from Students (final) “ I love the fact that we did this! I didn’t understand or enjoy it at first, but after practicing with it, I found that it was much easier than it seems. My advice would be to start more slowly with it. It seemed like we just jumped into it and it was very intimidating.” “ Going over it repeatedly was the most beneficial for me. The first few times we went over it I did not understand it at all, but with time, and you going over and explaining it multiple times, I began to understand. The __ % of __ earned __ equation helped.”

  19. Comments on Student Evals • “I worked a lot in this class, and was always taken to the brink of overwhelmed but not crossing over. I think this is a sign of an excellent class. The data analysis we did was a particular challenge. I came away from the exercise knowing I learned something completely out of my comfort zone.” • “Keep on trying with the Data Analysis.... we (students) need it... no matter how badly we do not like it at first.”

  20. My Own Assessment of Using Data Modules • Challenging but rewarding • Takes a lot of prep work first time • Need lots of repetition and feedback • Needs lots of cheerleading • Could always use more time • After one try, convinced worthwhile

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