Gender Discrimination Presentation

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Effect of Teaching gender bias on 10th grade female students

attitudes and interest in science careers

Nasiha Ocasio, Secondary Science

Education, Queens College

SEYS 778, Spring 2009

Background

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Location of Study

Two10th grade Living Environment classes were selected in a public high school in Brooklyn, NY for the study.

Ethnic makeup of the school is 28% african american/carribean, 70% latino and 2% other.

The Subjects

Group 1: 5 females, 7 males

12 students total

Group 2: 6 females, 6 males

12 students total

Group1:non-treatment Group2:treatment

The Measure Background Survey (demographic info such as ethnicity, gender, age,

grade, etc)

Contemporary Gender Discrimination Scale (1 to 5 Likert-type scale. Higher average score indicates positive gender attitudes)

Examples of questions: Society no longer treats women as inferior to men.

Any discrimination toward women that still exists today will be gone within 5-10 years.

Although women were frequently denied jobs 50 years ago, it rarely happens today.

The Treatment

Students in Group 2 participated in three information sessions that presented “expert evidence” that there is no difference in innate science and math ability between boys and girls.

Students in group 1 received no information sessions.

Exerpts from Information Sessions

The Male and Female Brain: How are they different?

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The male brain is slightly bigger than the female brain. Why? Men have larger bodies, more muscle and need more brain to control that

muscle.

Exerpts cont’d.

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Men are better at seeing objects in three dimensions. Women are better at

remembering details.

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Nine squares on each side, red, yellow, blue

Exerpts cont’d.

Are Boys better than girls in Math and Science?

Myth: Girls don’t have the right brai n structures to be good at math and science.

Fact: Studies have shown girls actually get higher grades in ALL their high school courses than boys, including math and science.

Myth: Adult men are better than adult women at math and science.

Fact: While men dominate the math and science fields, researchers believe that the likely cause is free choice, rather than ability.

Myth: When a boy is good at math and science, it’s because of his “natural talent.” If a girl is good at math, it is because she is very hardworking.

Fact: Research shows that although the male and female brains do work differently, both can develop the same level of math and science ability by using different strategies.

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Analysis of Data

Science Attitudes Pre-Treatment

Bars show counts

2.50 3.00 3.50 4.00

meanq1q7

0

1

2

3

Count

0 1

2.50 3.00 3.50 4.00

meanq1q7

Mean

females: 3.31

Males: 3.18

Mean

females: 2.91

Males: 3.04

Group 1 Group 2

Science Attitudes Post-Treatment

Bars show counts

3.00 3.50 4.00

meanq1aq7a

0

1

2

3

Count

0 1

3.00 3.50 4.00

meanq1aq7a

Mean

females: 3.52

Males: 3.40

Mean

females: 3.24

Males: 3.02

Group 1 Group 2

ANOVA

Sum of

Squares df Mean Square F Sig. Between Groups (Combined) .069 1 .069 .365 .552 Within Groups 4.155 22 .189

meanq1q7 * condition

Total 4.224 23 Between Groups (Combined) 1.361 1 1.361 11.014 .003 Within Groups 2.718 22 .124

meanq1aq7a * condition

Total 4.078 23

P > 0.05

difference between groups pre and post-treatment

P < 0.05

ANOVA

Sum of

Squares df Mean Square F Sig. Between Groups (Combined) .158 1 .158 .855 .365 Within Groups 4.066 22 .185

meanq1q7 * gender

Total 4.224 23 Between Groups (Combined) .009 1 .009 .046 .831 Within Groups 4.070 22 .185

meanq1aq7a * gender

Total 4.078 23

Difference between genders pre and post treatment

P values (p>0.05)

Results As expected, there was no significant difference

in the results between groups 1 and 2 pre-treatment.

Data analysis showed significant increase in attitudes and interest in science post-treatment in Group 2, but not Group 1.

Interestingly, there is no significant difference between the effect of the treatment on either males or females.

Conclusions and Further Study Results of the current study indicate a positive

effect of education on both male and female perceptions of women in science careers.

Limitations: both groups were small in size. Not a random sample. Students were mixed age. Short length of study does not permit observation of females later on in life, or if these information sessions will increase their probability of pursuing science as a career.