Posts tagged ‘computing education research’
SIGCSE Preview: Project Rise Up 4 CS: Increasing the Number of Black Students who Pass AP CS A — by paying them
I’m guessing that Barbara’s paper on Friday 1:45-3 (in Hanover FG – whole program here) is going to be controversial. She’s working on a problem we’ve had in GaComputes for years. Besides Betsy DiSalvo’s work on Glitch, we’ve made little progress in increasing numbers of Black students taking AP CS A and even less progress in getting more of them to pass the test.
She’s had significant progress this last year using an approach that NMSI used successfully in Texas and elsewhere. She’s offering $100 to Black students who attend extra sessions to help them pass the exam and who do pass the exam. She’s expanding the program now with a Google RISE grant. Her approach is informed by Betsy’s work – it’s about going beyond interests to values and giving students help in navigating past their motivations to not-learn. She does have aspects of the project in place to counteract the disincentives of cash payments for academic achievement. In the final interviews, students didn’t talk about the money. It may be that the money wasn’t an incentive as much as a face-saving strategy. (Barb’s preview talk was also recorded as part of a GVU Brown Bag.)
This paper describes Project Rise Up 4 CS, an attempt to increase the number of Black students in Georgia that pass the Advanced Placement (AP) Computer Science (CS) A exam. In 2012 Black students had the lowest pass rates on the AP CS A exam both in Georgia and nationally. Project Rise Up 4 CS provided Black students with role models, hands-on learning, competitions, a financial incentive, and webinars on AP CS A content. The first cohort started in January of 2013 and finished in May 2013. Of the 27 students who enrolled in the first cohort, 14 met all of the completion requirements, and 9 (69%) of the 13 who took the exam passed. For comparison, in 2012 only 22 (16%) of 137 Black students passed the exam in Georgia. In 2013, 28 (22%) of 129 Black students passed the exam in Georgia. This was the highest number of Black students to pass the AP CS A exam ever in Georgia and a 27% increase from 2012. In addition, students who met the completion requirements for Project Rise Up 4 CS exhibited statistically significant changes in attitudes towards computing and also demonstrated significant learning gains. This paper discusses the motivation for the project, provides project details, presents the evaluation results, and future plans.
2nd Annual ACM NDC Study
Of Non-Doctoral Granting Departments in Computing
Please contact ACM Education Manager Yan Timanovsky (email@example.com) ASAP! Deadline is March 16 (extensions possible upon request).
• As an annual survey, NDC produces timely data on enrollment, degree production, student body composition, and faculty salaries/demographics that can benchmark your institution/program(s) and invite useful conversations with your faculty and administration.
• Those who qualify for and complete NDC in its entirety will be entered in a drawing to receive one of (3) unrestricted grants of $2,500 toward your department’s discretionary fund.
SIGCSE Preview: Measuring Demographics and Performance in Computer Science Education at a Nationwide Scale Using AP CS Data
Barbara and I are speaking Thursday 3:45-5 (with Neil Brown on his Blackbox work) in Hanover DE on our AP CS analysis paper (also previewed at a GVU Brown Bag). The full paper is available here: http://bit.ly/SIGCSE14-APCS This is a different story than the AP CS 2013 analysis that Barbara has been getting such press for. This is a bit deeper analysis on the 2006-2012 results.
Here are a couple of the figures that I think are interesting. What’s fitting into these histograms are states, and it’s the same number of bins in each histogram, so that one can compare across.
Fitting this story into the six page SIGCSE format was really tough. I wanted to make the figures bigger, and I wanted to tell more stories about the regressions we explored. I focused on the path from state wealth to exam-takers because I hadn’t seen that story in CS Ed previously (though everyone would predict that it was there), but there’s a lot more to tell about these data.
Figure 1: Histograms describing (a) the number of schools passing the audit over the population (measured in 10K), (b) number of exam-takers over the population, and (c) percentage of exam-takers who passed.
Measuring Demographics and Performance in Computer Science Education at a Nationwide Scale Using AP CS Data
Abstract: Before we can reform or improve computing education, we need to know the current state. Data on computing education are difficult to come by, since it’s not tracked in US public education systems. Most of our data are survey-based or interview-based, or are limited to a region. By using a large and nationwide quantitative data source, we can gain new insights into who is participating in computing education, where the greatest need is, and what factors explain variance between states. We used data from the Advanced Placement Computer Science A (AP CS A) exam to get a detailed view of demographics of who is taking the exam across the United States and in each state, and how they are performing on the exam. We use economic and census data to develop a more detailed view of one slice (at the end of secondary school and before university) of computer science education nationwide. We find that minority group involvement is low in AP CS A, but the variance between states in terms of exam-takers is driven by minority group involvement. We find that wealth in a state has a significant impact on exam-taking.
For the first time ever, CS Education research is a field eligible for NSF CAREER. Applicants will be able to select STEM-CP: CE21 as the program for the July deadline. Please help getting the word out to potential applicants. We’d like to see some good proposals in this first year inviting CE21 CAREER proposals.
The National Science Foundation’s Computer and Information Science and Engineering Directorate (CISE) invites proposals this year to the Faculty Early Career Development (CAREER) program for faculty engaging in Computing Education research. That is, if you apply for the CAREER program, you’ll be able to select “STEM-CP: CE21″ as your Unit of Consideration. The intent of the CAREER program (http://www.nsf.gov/career) is to provide stable support at a sufficient level and duration to enable awardees to develop careers as outstanding researchers and educators who effectively integrate teaching, learning and discovery.
CISE is organizing a one-day proposal writing workshop (registration and details at: http://cs.gmu.edu/events/nsfcisecareer2014/) for CAREER-eligible faculty on March 31, 2014 in Arlington, VA. The registration deadline is February 28th. Unlike past years, this will be the only CISE CAREER workshop during this calendar year. Please circulate this information among interested faculty. The next deadline for CISE CAREER proposals is July 21, 2014.
Please let me know if you have any questions or concerns.
Jeffrey R.N. Forbes Program Director CISE/CNS Education and Workforce Cluster National Science Foundation firstname.lastname@example.org, +1 (919) 292-4291
I’m completely open to the idea that completion rates are the wrong measures of success for MOOCs. But I do believe that we need some measure. What would success for MOOCs mean? How do we know if it’s being achieved? Or if it’s a waste of time and money?
In the meantime, the Harvard and MIT researchers said they hoped the new studies would help people understand that technology and scale are not the only things that distinguish MOOCs from other kinds of higher education.
“People are projecting their own desires onto MOOCs,” said Mr. Ho, “and then holding them accountable for criteria that the instructors and institutions and, most importantly, students don’t hold for themselves.”
What a cool idea! Rob Moore is building on the subgoal labeling work that we (read: “Lauren”) did, and is using crowd-sourcing techniques to generate the labels.
Subgoal labeling  is a technique known to support learning new knowledge by clustering a group of steps into a higher-level conceptual unit. It has been shown to improve learning by helping learners to form the right mental model. While many learners view video tutorials nowadays, subgoal labels are often not available unless manually provided at production time. This work addresses the challenge of collecting and presenting subgoal labels to a large number of video tutorials. We introduce a mixed-initiative approach to collect subgoal labels in a scalable and efficient manner. The key component of this method is learnersourcing, which channels learners’ activities using the video interface into useful input to the system. The presented method will contribute to the broader availability of subgoal labels in how-to videos.
An important and interesting position, that I first learned about from the work of Caroline Simard. There is significant evidence that Silicon Vally is not a meritocracy, but there is significant advantage to the people in power there to maintain the myth.
But if the tech scene is really a meritocracy, why are so many of its key players, from Mark Zuckerberg to Steve Jobs, white men? If entrepreneurs are born, not made, why are there so many programs attempting to create entrepreneurs? If tech is truly game-changing, why are old-fashioned capitalism and the commodification of personal information never truly questioned?
The myths of meritocracy and entrepreneurialism reinforce ideals of the tech scene that shore up its power structures and privileges.
The myths of authenticity, meritocracy, and entrepreneurialism do have some basis in fact. But they are powerful because they reinforce ideals of the tech scene that shore up its power structures and privileges. Believing that the tech scene is a meritocracy implies that those who obtain great wealth deserve it, and that those who don’t succeed do not. The undue emphasis placed on entrepreneurship, combined with a limited view of who “counts” as an entrepreneur, function to exclude entire categories of people from ascending to the upper echelon of the industry. And the ideal of authenticity privileges a particular type of self-presentation that encourages people to strategically apply business logics to the way they see themselves and others.
California community colleges’ experiment with accelerated remediation: Maybe there’s more learning going on
Remedial courses in higher-education are important to get right, for lots of reasons. Certainly, that’s one of the big stumbling blocks in MOOCs — many people who start a MOOC aren’t prepared for that level material (or maybe, the MOOCs presume too much knowledge to start). The CAITE alliance was able to improve diversity in Massachusetts’ universities, by improving the transfer from community college, but that path sometimes requires remedial courses. If we could get remediation right, we might improve diversity, make distance learning more successful, and (as suggested below) improve graduation rates.
The story below is unusual: Make remediation better, by making it shorter. A simple time-on-task model would suggest that there’s less being learned. I hypothesize that it might be working (i.e., resulting in more learning), by looking at it from a different model.
At the Future Computer Science Research Summit in Orlando in early January, Nobel laureate Carl Wieman gave a talk where he referenced the famous Richard Hake 6000 subject study. One of the results of that study is that traditional lecture only results in students learning about 30-40 percent of what was being taught, but with student engagement pedagogies, 60-80 percent is learned.
Note the word: engagement. We can engage by using techniques like peer instruction. I wonder if we can also engage by saying, “This required course will be made shorter. You still need it to move on to something you want, but now, it’s less painful.” Could that result in more learning? Maybe that 30-40% becomes 50-60%? So a reduction of a few weeks in time may actually result in equal or more learning?
Remedial courses are widely seen as one of the biggest stumbling blocks to improving college graduation rates, as few students who place into remediation ever earn a degree.
The problem is particularly severe for black and Hispanic students, who account for almost half of the California community college system’s total enrollment of 2.4 million.
More than 50 percent of black and Hispanic community college students place three or more levels below college mathematics, said Myra Snell, a math professor at Los Medanos College. And only 6 percent of those remedial students will complete a credit-bearing math course within three years of starting the first remedial course.
A key reason for abysmal pass rates is the length of remedial sequences, argue Snell and Katie Hern, an English instructor at Chabot College, which, like Los Medanos, is a two-year institution located in California.
“The lower down you start, the fewer students complete,” Hern said.
The two instructors decided to do something about the problem. In 2010 they founded the California Acceleration Project. Armed with research from the Carnegie Foundation for the Advanced of Teaching and the Community College Research Center at Columbia University’s Teachers College, they encouraged their peers to offer shorter remedial sequences in math and English.
Call for Papers:
COMPUTER SCIENCE EDUCATION
A journal published by Taylor & Francis
Special Issue on Concept Inventories in Computing
SUBMISSION DEADLINE: May 9, 2014
Steven A. Wolfman, University of British Columbia
A concept inventory is a short, low-stakes assessment of core conceptual
topics in a discipline that is both easy to process for individual
students and aggregate across students—often composed only of
multiple-choice questions. Physics’s Force Concept Inventory is the
Concept inventories have revolutionized education in several disciplines
by making assessment simple, sustainable, scalable, and generalizable.
They enable educational researchers to make rapid progress by sharing
assessment tools. They expose trends in student learning to educational
practitioners and to the students themselves.
The guest editor invites authors to submit manuscripts for this special
issue devoted to concept inventories in computing. Suitable topics
include, but are not limited to:
+ Introduction of full or partial concept inventories for areas of
computing with evidence supporting their validity
+ Methodology for development of concept inventories in computing
+ Application of computing concept inventories to assess trends in
student learning, to compare across institutions or types of
institutions, to explore the impact of pedagogies, or otherwise
further computing education
+ Evidence—e.g., from course analysis or expert interviews or
surveys—to guide development of concept inventories in
+ Literature reviews of concept inventories in computing
May 9, 2013 :: Manuscripts due
Jul 11, 2014 :: Author notification
Aug 22, 2014 :: Final submission due
December 2014 :: Publication
Submissions should be emailed to the guest editor at:
Full papers are ideally 5,000 to 7,000 words long. Both abstracts and full
papers should be in Word or PDF, formatted according to the instructions
Questions about the special issue should be sent to the guest editor
Steven Wolfman <email@example.com>. General questions may also be sent to The
Journal of Computer Science Education editors, Sally Fincher and Laurie
NYTimes: Tech’s Diversity Problem Is Apparent as Early as High School – interview with Barbara Ericson
On the ongoing thread of media coverage over Barbara’s analysis of AP CS 2013 exam results, this is a standout. The NYTimes had a blog post interviewing Barb, and they did a nice job. They highlighted not just the outliers (like Wyoming with no test-takers) but the interesting trends (there used to be a good number of AP CS exam takers in Wyoming).
Even in California, where it would seem that more children would be exposed to adults working in computer science, just 22 percent of test takers were girls, 1.5 percent were black and 8 percent were Hispanic.
The A.P. data also shows how the situation in computer science has worsened over time. In Wyoming, for instance, no high school student of any race or gender took the test, while 35 students took the test there in 2001.
In early January, right after the NSF CE21 PI’s meeting, there was a summit on setting a Future Computing Education Research agenda. The materials from that event are now being made available.
The conference web page has been updated so all presentations given during the meeting are linked to the agenda. http://stanford.edu/~kmenchac/FDS2014/schedule.html#agenda
The Stanford Digital Library is housing the white papers from our conference. We do not plan to have them published, although some or all may be referred to in the final report (which will be shared with you). http://purl.stanford.edu/mn485tg1952
I met with a prospective PhD student recently, who told me that she’s interested in using big data to inform her design of computing education. She said that she disliked designing something, just crossing her fingers hoping it would work. She and the faculty she’s working with are trying to use big data to inform their design decisions.
That’s a fine approach, but it’s pretty work-intensive. You gather all this data, then you have to figure out what’s relevant, and what it means, and how it influences practice. It’s a very computer science-y way of solving the problem, but it’s rather brute force.
There is a richer data source with much more easily applicable design guidelines: educational psychology literature. Educational psychologists have been thinking about these issues for a long time. They know a lot of things.
We’re finding that we can inform a lot of our design decisions by simply reading the relevant education literature:
- Like our work on subgoal labeling,
- And on worked examples,
- And on lower-cognitive load learning,
- And on peer instruction.
I was recently reading a computer science paper in which the author said that we don’t know much about mathematics education, and that’s because we’ve never had enough data to come up with findings. But there were no references to mathematics education literature. We actually know a lot about mathematics education literature. Too often, I fear that we computer scientists want to invent it all ourselves, as if that was a better approach. Why not just talk to and read the work of really smart people who have devoted their lives to figuring out how to teach better?
The blog post linked below felt close to home, though I measure it differently than lines of code. The base point is that we tend to start introductory programming courses assuming way more knowledge than is already there. My experience this semester is that we tend to expect students to gain more knowledge more quickly than they do (and maybe, than they can).
I’m teaching Python Media Computation this semester, on campus (for the first time in 7 years). As readers know, I’ve become fascinated with worked examples as a way of learning programming, so I’m using a lot of those in this class. In Ray Lister terms, I’m teaching program reading more than program writing. In Bloom’s taxonomy terms, I’m teaching comprehension before synthesis.
As is common in our large courses at Georgia Tech (I’m teaching in a lecture of 155 students, and there’s another parallel section of just over 100), the course is run by a group of undergraduate TA’s. Our head TA took the course, and has been TA-ing it for six semesters. The TA’s create all homeworks and quizzes. I get to critique (which I do), and they do respond reasonably. I realize that all the TA’s expect that the first thing to measure in programming is writing code. All the homeworks are programming from a blank sheet of paper. Even the first quiz is “Write a function to…”. The TA’s aren’t trying to be difficult. They’re doing as they were taught.
One of the big focal research areas in the new NSF STEM-C solicitation is “learning progressions.” Where can we reasonably expect students to start in learning computer science? How fast can we reasonably expect them to learn? What is a reasonable order of topics and events? We clearly need to learn a lot more about these to construct effective CS education.
I’m not going to articulate the next few orders of magnitude, both because they are not relevant to beginner or intermediate programmers, and because I’m climbing the 1K → 10K transition myself, so I’m not able to articulate it well. But they have to do with elegance, abstraction, performance, scalability, collaboration, best practices, code as craft.
The 3am realization is that many, many “introduction” to programming materials start at the 1 → 10 transition. But learners start at the 0 → 1 transition — and a 10-line program has the approachability of Everest at that point.
All the press coverage of Barbara Ericson’s AP CS 2013 exam results analysis has led to a lot of discussion among my Facebook friends. The results are even more telling than the raw numbers.
- Rebecca Dovi and Ria Galanos, both exceptional AP CS high school teachers and both in Virgina, started comparing notes on the Hispanic students who took the AP CS exam from that state. They could name half of them. Looks like those two teachers were responsible for half of the Hispanic exam takers from Virginia.
- Why is that Tennessee has ranked so well for female AP CS exam takers among all the states? It is due to one exceptional AP CS teacher, Jill Pala, who teaches at an all-girls school. Barb verified this claim. Jill’s class generated 30 of the 71 female exam-takers in Tennessee. Without Jill, Tennessee would be in the middle of the pack. With Jill, they have the highest percentage of female AP CS exam-takers among all the states.
On the one hand, what a wonderful statement about the impact that a single exceptional teacher can make! Hey, states that want to raise their exam taker numbers — go hire yourselves a Rebecca, Ria, or Jill! Or provide the professional development to grow your own!
On the other hand — our numbers are SO small that a single teacher can make the difference for a whole state. There were 2103 schools that passed the AP CS audit in 2012. That’s probably exactly the number of AP CS teachers, too. There were 11,694 schools that passed the audit for AP Calculus! Great teachers matter in Calculus, too. But there are so many teachers, an individual teacher probably can’t make or break a whole state’s ranking. Wouldn’t it be nice for AP CS to be in that position?