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How gender affects participation in “Techy” or “Fuzzy” Classes at Stanford

How gender affects participation in “Techy” or “Fuzzy” Classes at Stanford. Kaitlin Asrow Aron Hegyi Katrina Li Kate Kourlis. Project Goal.

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How gender affects participation in “Techy” or “Fuzzy” Classes at Stanford

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  1. How gender affects participation in “Techy” or “Fuzzy” Classes at Stanford Kaitlin Asrow Aron Hegyi Katrina Li Kate Kourlis

  2. Project Goal We are trying to see if there is a difference in the amount men and women participate in class according to whether the class is perceived as “techy,” “fuzzy,” or “neutral” by male and female Stanford students.

  3. Group’s Predictions • Men would answer more in classes rated as “techy” by both men and women. • Women would answer more in classes rated as “fuzzy” by both men and women. • There would be equal gender participation in classes rated as “neutral” by men and women.

  4. Method Used to Determine Distinctions between Classes • Surveyed Language and Gender class students, asking them to rank the observed classes on a scale from 1-7. 1 being fuzziest and 7 techiest. • Then, taking the averages separately for men’s and women’s responses, we ranked the classes as perceived “fuzzy,” “techy,” or “neutral.” • We determined the results that fell between 3.5 and 4.5 to be “neutral” classes, which are neither distinctly “fuzzy” or “techy.”

  5. Fuzzy, Techy or Neutral • Average of all numbers given • Fuzzy 0 - 3.5 • Neutral 3.5 - 4.5 • Techy 4.5 - 7

  6. History of Ancient Empires Huck Finn & American Culture International Politics-Iran Humanities-Hamlet PoliSci-Post War Reconstruction Sociology-Race and Ethnic Relations War in the Modern World Japanese History IHUM: Myth and Modernity Historical linguistics Film Feminist studies CASA183D: Border Crossings Theories of Film Practice Intro. To Queer Theory Intro to Social Stratification Modernist Russian poetry International relationsUrban Studies Political Science FUZZY

  7. Physics for Non Majors MS&E - Stochastic Modeling Engineering-Intermediate discrete mathematics Biology-Intro Mechanical Engineering mathematical logic Biochemistry Engineering Statistics Material Science & Engineering Intro to Statistics Chemistry Engineering-Advanced Probability and Statistics computer science-Intermediate Computer Science-Advanced MS&E (Finance) ME (Product Design) Math-Intermediate E50 Intro to MatSci CS106a Section TECHY

  8. Neutral • Econ102b - Econometrics • Linguistics (Lexical Semantics) • CS 247: Human Computer Interaction Design

  9. Women fuzzy; males neutral Lingustics-Intermediate Linguistics Seminar Linguistics-Beginning Females neutral; males fuzzy Intro to Wilderness Skills Females neutral; males techy Econ102b - Econometrics Differences in opinion

  10. STUDENTS COMMENTS

  11. Null hypothesis can be rejected (P < .01) in all four cases. There is a significant difference between the proportion of male or female students in each category of class (Techy and Fuzzy) and the proportion of comments that each gender makes.

  12. In the Fuzzy/Neutral classes, the data are not statistically significant.

  13. Interesting Findings • Expectations/disbelief • Impossibility of classifying linguistics • Presuppositions

  14. Possible Flaws of the Study:Why do we find males participating more than females in both “techy” and “fuzzy” classes? • Internal validity • Data collection methods • Some people only recorded every other comment; this could have affected the numbers of female/male speakers. • Subconscious selection • Maybe males spoke more slowly or their comments were shorter, making their tokens easier to collect and record.

  15. So what? • Stanford relevance • Why do we have the terms “fuzzy” and “techy” here at Stanford? • Fascination with classification • Tendency to label/identify ourselves • Binary view • Seeing things as black or white • Hierarchy • Funding issues (how much University affects these stereotypes)

  16. THE END

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