<commit_before>[Edit this page]({{site.github.repository_url}}/edit/master/README.md)

# Why bias matters {#why}
Because equity matters.

When looking at groups of students in aggregate, there are clear gaps in outcomes for students grouped based on race, ethnicity, gender, disability and class.  This shows up in high-level measures like achievement, retention and graduation even at the national level.  In addition, pathways towards STEM careers are increasingly important to many students, families, educators and policy makers.  Yet the STEM pipeline also shows clear gaps in outcomes for students grouped by race, ethnicity, gender, disability and class.

It's clear that intersectionality is important and students each have their own unique lived experience.  And that the cultures of different schools, cities and regions shape student experiences as well.  Nevertheless, high-level gaps in students outcomes based on single demographic characteristics reveal critical equity issues in the education system that should be targeted for research and intervention.

As the [MIT Teaching Systems Lab](http://tsl.mit.edu/) works with K12 educators, teacher preparation programs, and other organizations to scale access to new STEM opportunities, we all risk replicating these gaps in areas like computational thinking and making.

Implicit bias is a promising line of research for understanding and addressing these issues in K12 education.  Studies across a variety of fields including education, medicine, law enforcement, criminal justice, and housing suggest that people unconsciously consider race, ethnicity and gender when making key decisions that shape other people's lives.  Research into implicit bias may reveal insight into how to reduce gaps in K12 student outcomes and how we can improve.

## Gaps in K12 student outcomes 
Black students are more than three times as likely to be suspended or expelled as their white peers ([OCR 2016](http://www2.ed.gov/about/offices/list/ocr/docs/crdc-discipline-snapshot.pdf)).  This difference is visible even in preschool (see [Gilliam 2016](http://ziglercenter.yale.edu/publications/Preschool%20Implicit%20Bias%20Policy%20Brief_final_9_26_276766_5379.pdf)), and there are important intersectional effects for black female students ([Crenshaw 2014](http://www.law.columbia.edu/null/download?&exclusive=filemgr.download&file_id=613546)).  Black and Latino students are 36% of students in schools with calculus, but only make up 21% of students enrolled in calculus ([Office for Civil Rights 2016](http://www2.ed.gov/about/offices/list/ocr/docs/2013-14-first-look.pdf)).

While female students are more academically successful in K12 settings than male students, they report liking math and science less than male students ([NCES 2015](http://nces.ed.gov/pubs2015/2015075.pdf)).  In upper high school grades, when students have more choice over their course of study, girls opt-out of specific math and science courses ([Blickenstaff 2006](http://www.tandfonline.com/doi/abs/10.1080/09540250500145072)).  In AP mathematics, boys consistently outnumber girls by up to 10,000 students and females only make up 22% of students in AP Computer Science ([College Board 2007](http://research.collegeboard.org/sites/default/files/publications/2012/7/researchreport-2007-4-ap-students-college-analysis-five-year-academic-careers.pdf)).  For career and technical education programs, girls make up 25% of students in STEM courses ([OCR 2012](http://www2.ed.gov/about/offices/list/ocr/docs/gender-equity-in-education.pdf)).

Students with disabilities are 12% of all students, but 67% of students subject to restraint or seclusion ([OCR 2016](http://www2.ed.gov/about/offices/list/ocr/docs/2013-14-first-look.pdf)).


## Where can I learn more?
- [Racial Disproportionality in School Discipline: Implicit Bias is Heavily Implicated (Rudd 2014)](http://kirwaninstitute.osu.edu/wp-content/uploads/2014/02/racial-disproportionality-schools-02.pdf)
- [Diversity Gaps in Computer Science: Exploring the Underrepresentation of Girls, Blacks and Hispanics (Google, 2016)](http://services.google.com/fh/files/misc/diversity-gaps-in-computer-science-report.pdf)
- [Black Girls Matter: Pushed out, Overpoliced and Underprotected (Crenshaw 2014)](http://www.law.columbia.edu/null/download?&exclusive=filemgr.download&file_id=613546)
- [Women and science careers: leaky pipeline or gender filter? (Blickenstaff 2006)](http://www.tandfonline.com/doi/abs/10.1080/09540250500145072)
- [Google's Computer Science Education Research](https://www.google.com/edu/resources/computerscience/research/)
- [https://code.org/diversity](https://code.org/diversity)
<commit_msg>Add info about subject gap for female students<commit_after>[Edit this page]({{site.github.repository_url}}/edit/master/README.md)

# Why bias matters {#why}
Because equity matters.

When looking at groups of students in aggregate, there are clear gaps in outcomes for students grouped based on race, ethnicity, gender, disability and class.  This shows up in high-level measures like achievement, retention and graduation even at the national level.  In addition, pathways towards STEM careers are increasingly important to many students, families, educators and policy makers.  Yet the STEM pipeline also shows clear gaps in outcomes for students grouped by race, ethnicity, gender, disability and class.

It's clear that intersectionality is important and students each have their own unique lived experience.  And that the cultures of different schools, cities and regions shape student experiences as well.  Nevertheless, high-level gaps in students outcomes based on single demographic characteristics reveal critical equity issues in the education system that should be targeted for research and intervention.

As the [MIT Teaching Systems Lab](http://tsl.mit.edu/) works with K12 educators, teacher preparation programs, and other organizations to scale access to new STEM opportunities, we all risk replicating these gaps in areas like computational thinking and making.

Implicit bias is a promising line of research for understanding and addressing these issues in K12 education.  Studies across a variety of fields including education, medicine, law enforcement, criminal justice, and housing suggest that people unconsciously consider race, ethnicity and gender when making key decisions that shape other people's lives.  Research into implicit bias may reveal insight into how to reduce gaps in K12 student outcomes and how we can improve.

## Gaps in K12 student outcomes 
Black students are more than three times as likely to be suspended or expelled as their white peers ([OCR 2016](http://www2.ed.gov/about/offices/list/ocr/docs/crdc-discipline-snapshot.pdf)).  This difference is visible even in preschool (see [Gilliam 2016](http://ziglercenter.yale.edu/publications/Preschool%20Implicit%20Bias%20Policy%20Brief_final_9_26_276766_5379.pdf)), and there are important intersectional effects for black female students ([Crenshaw 2014](http://www.law.columbia.edu/null/download?&exclusive=filemgr.download&file_id=613546)).  Black and Latino students are 36% of students in schools with calculus, but only make up 21% of students enrolled in calculus ([Office for Civil Rights 2016](http://www2.ed.gov/about/offices/list/ocr/docs/2013-14-first-look.pdf)).

While female students are more academically successful in K12 settings than male students, they report liking math and science less than male students ([NCES 2015](http://nces.ed.gov/pubs2015/2015075.pdf)).  In upper high school grades, when students have more choice over their course of study, girls opt-out of specific math and science courses ([Blickenstaff 2006](http://www.tandfonline.com/doi/abs/10.1080/09540250500145072)).  For career and technical education programs, girls make up 25% of students in STEM courses ([OCR 2012](http://www2.ed.gov/about/offices/list/ocr/docs/gender-equity-in-education.pdf)).  Within AP courses, more girls make up 61% of biology students and 55% of Environmental Science students, but only 35% of Physics students (all courses) and 23% of Computer Science students ([College Board 2007](http://research.collegeboard.org/sites/default/files/publications/2012/7/researchreport-2007-4-ap-students-college-analysis-five-year-academic-careers.pdf)).  This is particularly important because students enrolled in AP courses are more likely to major in those subjects in college, especially for STEM subjects ([College Board 2014](https://secure-media.collegeboard.org/digitalServices/pdf/ap/ap-student-success-college-recent-research.pdf)).

Students with disabilities are 12% of all students, but 67% of students subject to restraint or seclusion ([OCR 2016](http://www2.ed.gov/about/offices/list/ocr/docs/2013-14-first-look.pdf)).


## Where can I learn more?
- [Racial Disproportionality in School Discipline: Implicit Bias is Heavily Implicated (Rudd 2014)](http://kirwaninstitute.osu.edu/wp-content/uploads/2014/02/racial-disproportionality-schools-02.pdf)
- [Diversity Gaps in Computer Science: Exploring the Underrepresentation of Girls, Blacks and Hispanics (Google, 2016)](http://services.google.com/fh/files/misc/diversity-gaps-in-computer-science-report.pdf)
- [Black Girls Matter: Pushed out, Overpoliced and Underprotected (Crenshaw 2014)](http://www.law.columbia.edu/null/download?&exclusive=filemgr.download&file_id=613546)
- [Women and science careers: leaky pipeline or gender filter? (Blickenstaff 2006)](http://www.tandfonline.com/doi/abs/10.1080/09540250500145072)
- [Google's Computer Science Education Research](https://www.google.com/edu/resources/computerscience/research/)
- [https://code.org/diversity](https://code.org/diversity)

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