Posts tagged ‘NCWIT’

AP CS A Exam Data for 2016: Barb Ericson’s analysis, Hai Hong’s guest blog post #CSedWeek

As usual, Barbara Ericson went heads-down, focused on the AP CS A data when the 2016 results were released.  But now, I’m only one of many writing about it.  Education Week is covering her analysis (see article here), and Hai Hong of Google did a much nicer summary than the one I usually put together. Barb’s work with Project Rise Up 4 CS and Sisters Rise Up have received funding from the Google Rise program, which Hai is part of. I’m including it here with his permission — thanks, Hai!

Every year, I’m super thankful that Barb Ericson at Georgia Tech grabs the AP CS A data from the College Board and puts it all into a couple of spreadsheets to share with the world.  🙂
Here’s the 2016 data, downloadable as spreadsheets: Overall and By Race & Gender.  For reference, you can find 2015 data here and here.
Below is a round-up of the most salient findings, along with some comparison to last year’s.  More detailed info is in the links above.  Spoiler: Check out the 46% increase in Hispanic AP exam takers!
  • Overall: Continued increases in test-taking, but a dip in pass rates.
    • 54,379 test-takers in 2016.  This reflects a 17.3% increase from 2015 — which, while impressive, is a slower increase than 24.2% in 2015 and 26.3% in 2014.
    • Overall pass rate was 64% (same as last year; 61% in 2014)
  • Girls
    • Female exam takers: 23% (upward trend from 22% in 2015, 20% in 2014)
    • Female pass rate: 61% (same as last year; 57% in 2014)
    • In 8 states fewer than 10 females took the exam: Alaska (9/60), Nebraska (8/88), North Dakota (6/35 ), Kansas (4/57), Wyoming (2/6 ), South Dakota (1/26 ), Mississippi (0/16), Montana(0/9). Two states had no females take the exam: Mississippi and Montana.
  • Black
    • Black exam takers: 2,027 (Increase of 13% from 1,784 in 2015; last year’s increase was 21% from 1,469 in 2014)
    • Black pass rate: 33% (down from 38% in 2015, but close to 2014 pass rate of 33.4%).
    • Twenty-four states had fewer than 10 African American students take the AP CS A exam. Nine states had no African American students take the AP CS A exam: Maine (0/165), Rhode Island (0/94), New Mexico (0/79), Vermont (0/70), Kansas (0/57), North Dakota (0/35), Mississippi (0/16), Montana (0/9), Wyoming (0/6)
  • Hispanic
    • Hispanic exam takers: 6,256 (46% increase from 4,272 in 2015!)
    • Hispanic pass rate: 41.5% (up from 40.5% in 2015)
    • Fifteen states had fewer than 10 Hispanics take the exam: Delaware, Nebraska, Rhode Island, New Hampshire, Maine, Kansas, Idaho, West Virginia, Wyoming, Vermont, Mississippi, Alaska, North Dakota, Montana, and South Dakota. Three states had no Hispanics take the exam: North Dakota(0/35), Montana (0/9), South Dakota (0/26).
And as a hat-tip to Barb Ericson (whose programs we’ve partnered with and helped grow through the RISE Awards these last 3 years) and the state of Georgia:
  • 2,033 exam takers in 2016 (this represents something like a 410% increase in 12 years!)
  • New record number of African Americans and females pass the exam in Georgia again this year!
  • 47% increase (464 in 2016 vs. 315 in 2015) in girls taking the exam.
  • Nationally, the African American pass rate dropped from 37% to 33%.  In Georgia it increased from 32% to 34%.
  • The pass rate for female students also increased in Georgia from 48% to 51%.
  • Only one African American female scored a 5 on the AP CS A exam in Georgia in 2016 and she was in Sisters Rise Up 4 CS (RISE supported project).

December 5, 2016 at 7:13 am 2 comments

Steps Teachers Can Take to Keep Girls and Minorities in Computer Science Education | Cynthia Lee in KQED News

So glad to see Cynthia Lee’s list (described in this blog post) get wider coverage.

Last summer, Cynthia Lee, a lecturer in the computer science department at Stanford University, created a widely-circulated document called, “What can I do today to create a more inclusive community in CS?” The list was developed during a summer workshop funded by the National Science Foundation for newly hired computer science faculty and was designed for busy educators. “I know the research behind these best practices,” said Lee, “but my passion comes from what I’ve experienced in tech spaces, and what students have told me about their experiences in computer science classrooms.”

Too often students from diverse backgrounds “feel that they simply aren’t wanted,” said Lee. “What I hear from students is that when they are working on their assignments, they love [computer science]. But when they look up and look around the classroom, they see that ‘there aren’t many people like me here.’ If anything is said or done to accentuate that, it can raise these doubts in their mind that cause them to questions their positive feelings about the subject matter.”

Source: Steps Teachers Can Take to Keep Girls and Minorities in Computer Science Education | MindShift | KQED News

November 30, 2016 at 7:31 am Leave a comment

Google-Gallup Reports on Race and Gender Gaps in CS: Guest Blog Post from Miranda Parker

Google’s latest reports from their collaboration with Gallup lines up with Miranda Parker’s research interests in privilege in CS education (see preview of her RESPECT 2015 paper here). I invited her to write a guest blog post introducing the new reports. I’m grateful that she agreed.

Google, in collaboration with Gallup, has recently released new research about racial and gender gaps in computer science K-12 classrooms. A lot of the report confirms what we already knew: there are structural and social barriers that limit access to CS for black, Hispanic, and female students. I don’t mind the repeated results though–it helps form an even stronger argument that there is a dearth of diversity in computing classrooms across the country.

The report does highlight interesting tidbits that may not have been as obvious before. For example, black and Hispanic students are 1.5 and 1.7 times more likely than white students to be “very interested” in learning computer science. This knowledge, combined with the data that black and Hispanic students are less likely to have access to learning CS, creates a compelling argument for growing programs focused at these groups.

Research like this continues to push the envelope of what is known about racial and gender gaps in computer science. However, it may be time to dig deeper than visible identities and explore if there are other variables that, independently or together with the other traits, create a stronger argument for why the diversity gap exists. Does socioeconomic status better explain racial gaps? What about spatial ability? These are variables that we at Georgia Tech are looking at, as we hypothesize about what can be done to level the playing field in computing.

goedu_racial_gender_info_1018_r1_01_2-width-1000

 

Today, we’re releasing new research from our partnership with Gallup that investigates the demographic inequities in K-12 computer science (CS) education in two reports, Diversity Gaps in Computer Science: Exploring the Underrepresentation of Girls, Blacks and Hispanics and Trends in the State of Computer Science in U.S. K-12 Schools. We surveyed 16,000 nationally representative groups of students, parents, teachers, principals, and superintendents in the U.S.  Our findings explore the CS learning gap between white students and their Black and Hispanic peers as well as between boys and girls and confirm just how much demographic differences matter.  We’re excited to share this data to bring awareness to issues on the ground in order to help expand CS education in meaningful ways.

Source: Racial and gender gaps in computer science learning: New Google-Gallup research

October 26, 2016 at 7:22 am 1 comment

The big reason why women drop out of engineering and computing: It isn’t in the classroom

Yep. Though I’ve seen a lot of in-classroom culture that drives out women, the bigger driver is that computing culture drives out many people, like the Stack Overflow results recently mentioned here.

Engineering classes and assignments do not “weed out” women; indeed, data show that women students do as well or better than male students in their course work. Instead, women students often point to the culture of engineering itself as a reason for leaving engineering.

This starts with activities that are designed to show novices how the profession actually does its work, how to interact with clients and other professionals, and how to exercise discretionary judgment in situations of uncertainty. Many discover that the engineering profession is not as open to being socially responsible as they hoped.

And, during the more informal, out-of-classroom training and socialization, women experience conventional gender discrimination that leaves them marginalized. These factors appear to be the main reasons these accomplished women leave their chosen profession.

Source: The big reason women drop out of engineering isn’t in the classroom – MarketWatch

October 7, 2016 at 7:08 am Leave a comment

Barriers to Stack Overflow Use for Females

Stack Overflow is an often used resource by programmers today. It’s also a barrier to women entering computing. Here’s a blog post summarizing a recent study on why women find Stack Overflow so unwelcoming.

There are many movements to get women into programming, but what about keeping them there? If they don’t feel comfortable using the resources that are available for all programmers then that is a big problem for retention in the field. To do our part in being more proactive in welcoming women into the field, we sought to uncover some reasons for this low participation.

Source: Paradise Unplugged: Barriers to Stack Overflow Use for Females | fordable

September 12, 2016 at 7:08 am 11 comments

Learning CS while Learning English: Scaffolding ESL CS Learners – Thesis from Yogendra Pal

When I visited Mumbai for LaTICE 2016, I mentioned meeting Yogendra Pal. I was asked to be a reader for his thesis, which I found fascinating. I’m pleased to report that he has now graduated and his thesis, A Framework for Scaffolding to Teach Vernacular Medium Learners, is available here: https://www.cse.iitb.ac.in/~sri/students/#yogendra.

I learned a lot from Yogendra’s thesis, like what “vernacular medium learners” means. Here’s the problem that he’s facing (and that Yogendra faced as a student). Students go through primary and secondary school learning in one language (Hindi, in Yogendra’s personal case and in his thesis), and then come to University to study Computer Science. Do you teach them (what Yogendra calls “Medium of Instruction” or MoI) in English, or in Hindi? Note that English is pervasive in Computer Science, e.g., almost all our programming languages use English keywords.

Here’s Yogendra’s bottomline finding: “We find that self-paced video-based environment is more suitable for vernacular medium students than a classroom environment if English-only MoI are used.” Yogendra uses a design-based research methodology. He measures the students, tries something based on his current hypothesis, then measures them again. He compares what he thought would happen to what he saw, and revises his hypothesis — and then iterate. Some of the scaffolds he tested may seem obvious (like using a slower pace), but a strength of the thesis is that he develops rationale for each of his changes and tests them. Eventually, he came to this surprising (to me) and interesting result: It’s better to teach with Hindi in the classroom, and in English when students are learning from self-paced videos.

The stories at the beginning of the thesis are insightful and moving. I hadn’t realized what a handicap it is to be learning English in a class that uses English. It’s obvious that the learners would be struggling with the language. What I hadn’t realized was how hard it is to raise your hand and ask questions. Maybe you have a question just because you don’t know the language. Maybe you’ll expose yourself to ridicule because you’ll post the question wrong.

Yogendra describes solutions that the Hindi-speaking students tried, and where the solutions didn’t work. The Hindi-speaking students used English-to-English dictionaries. They didn’t want English-Hindi dictionaries, because they wanted to become fluent in English, but they needed help with the complicated (especially technical) words. They tried using online videos for additional explanations of concepts, but most of those were made by American or British speakers. When you’re still learning English, switching from an Indian accent to another accent is a barrier to understanding.

The middle chapters are a detailed description of Yogendra’s attempts to scaffold student learning. He tried to teach in all-Hindi but some English technical terms like “execute” don’t have a direct translation in Hindi. He selected other Hindi words to represent the technical terms, but the words he selected as the Hindi translation were unusual and not well-known to the students. Perhaps the most compelling insight for me in these chapters was how important it was to both the students and the teachers that the students learn English — even when the Hindi materials were measurably better for learning in some conditions.

In the end, he found that Hindi language screencasts led to better learning (statistically significantly) when the learners (who had received primary and secondary school instruction in Hindi) were in a classroom, but that the English language screencasts led to better learning (again, statistically significantly) when the learners were watching the screencasts self-paced. When the students are self-paced, they can rewind and re-watch things that are confusing, so it’s okay to struggle with the English. In the classroom, the lecture just goes on by. It works best if it’s in Hindi for the students who learned in Hindi in school.

Yogendra tells a convincing story. It’s an interesting question of how these lessons transfer to other contexts. For example, what are the issues for Spanish-speaking students learning CS in the United States? In a general form, can we use the lessons from this thesis to make CS learning accessible to more ESL (English as a Second Language) learners?

September 8, 2016 at 5:50 pm 6 comments

Women 1.5 Times More Likely to Leave STEM Pipeline after Calculus Compared to Men: Lack of Mathematical Confidence a Potential Culprit

When you read this paper, consider Nathan Ensmenger’s assertion that (a) mathematics has been show to predict success in CS classes but not in computing careers and (b) increasing mathematics requirements in undergraduate CS may have been a factor in the decline in female participation in computing.

Our analyses show that, while controlling for academic preparedness, career intentions, and instruction, the odds of a woman being dissuaded from continuing in calculus is 1.5 times greater than that for a man. Furthermore, women report they do not understand the course material well enough to continue significantly more often than men. When comparing women and men with above-average mathematical abilities and preparedness, we find women start and end the term with significantly lower mathematical confidence than men. This suggests a lack of mathematical confidence, rather than a lack of mathematically ability, may be responsible for the high departure rate of women. While it would be ideal to increase interest and participation of women in STEM at all stages of their careers, our findings indicate that if women persisted in STEM at the same rate as men starting in Calculus I, the number of women entering the STEM workforce would increase by 75%.

Source: PLOS ONE: Women 1.5 Times More Likely to Leave STEM Pipeline after Calculus Compared to Men: Lack of Mathematical Confidence a Potential Culprit

August 24, 2016 at 7:06 am 8 comments

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