Back in October, I posted here about Miranda Parker’s defense. Back then, I tried to summarize all of Miranda’s work as a doctoral student in one sentence:
That was a seriously run-on sentence for an impressive body of work.
On Friday, December 13, I had the great honor of placing an academic hood on Dr. Parker*.
I haven’t really talked too much about Miranda’s dissertation findings yet. I really want to, but I also don’t want to steal the thunder from her future publications. So, with her permission, I’m going to just summarize some of my favorite parts.
First, Miranda built several regression models to explain Georgia high schools teaching computer science in 2016. I predicted way back at her proposal that the biggest factor would be wealth — wealthy schools would teach CS and poorer schools wouldn’t. I was wrong. Yes, that’s a statistically significant factor, but it doesn’t explain much.
The biggest factor is…teaching computer science in 2015. Schools that taught CS in 2015 were many times more likely to be teaching in 2016. Schools have to get started! Hadi Partovi made this argument to me once, that getting started in schools was the biggest part of the battle. Miranda’s model supports his argument. There’s much more to the story, but that’s the biggest takeaway for me on the quantitative analysis.
But her model only explains a bit over 50% of the variance. What explains the rest? We figured that there would be many different factors. To make it manageable, Miranda chose just four high schools to study as case studies where she did multiple interviews. All four of the schools were predicted by her best model to teach computer science, but none of the did.
Yes, as you’d expect, access to a teacher is the biggest factor, but it’s not as simple as just deciding to hire a CS teacher or train an existing teacher to teach CS. For example, one principal laid out for Miranda who could teach CS and who would take their class, and how to fill that gap in the schedule, and so on. In the end, he had a choice of offering choir or offering CS. There were students in choir. CS was a gamble. It wasn’t even a hard decision for that principal.
Here is the story that most surprised me. At two of the schools Miranda studied, they teach lots of cyber security classes, but no computer science. As you would expect, there was a good bit of CS content in the cybersecurity classes. They had the teachers. Why not then teach CS, too?
Because both of these schools were near Fort Gordon which has a huge cybersecurity district. Cybersecurity is a community value. It’s a sure thing when it comes to getting a job. What’s “computer science” in comparison?
In my opinion, there is nothing like Miranda’s study in the whole world. There are the terrific Roehampton reports that give us a quantitative picture about CS in all of England. There are great qualitative studies like Stuck in the Shallow End that tell us what’s going on in high schools. Miranda did both and connected them — the large scale quantitative analysis, and then used that to pick four interesting high schools to dig into for qualitative analysis. It’s a story specific to Georgia, and each US state is going to be a different story. But it’s a whole state, the right level of analysis in the US. It’s a fascinating story, and I’m proud that she pulled it off.
Keep an eye out for her publications about this work over the next couple years.
* By the way, Dr. Parker is currently on the academic job market.
Miranda Parker defends her dissertation this Thursday. It’s a really fascinating story, trying to answer the question: Why does a high school in Georgia decide (or not) to offer computer science? She did a big regression analysis, and then four detailed case studies. Readers of this blog will know Miranda from her guest blog post on the Google-Gallup polls, her SCS1 replication of the multi-lingual and validated measure of CS1 knowledge, her study of teacher-student differences in using ebooks, and her work exploring the role of spatial reasoning to relate SES and CS performance (work that was part of her dissertation study). I’m looking forward to flying down to Atlanta and being there to cheer her on to the finish.
Title: An Analysis of Supports and Barriers to Offering Computer Science in Georgia Public High Schools
Miranda Parker
Human-Centered Computing Ph.D. Candidate
School of Interactive Computing
College of Computing
Georgia Institute of Technology
Date: Thursday, October 10, 2019
Time: 10AM to 12PM EST
Location: 85 5th Street NE, Technology Square Research Building (TSRB), 2nd floor, Room 223
Committee:
Dr. Mark Guzdial (Advisor), School of Interactive Computing, Georgia Institute of Technology
Dr. Betsy DiSalvo, School of Interactive Computing, Georgia Institute of Technology
Dr. Rebecca E. Grinter, School of Interactive Computing, Georgia Institute of Technology
Dr. Willie Pearson, Jr., School of History and Sociology, Georgia Institute of Technology
Dr. Leigh Ann DeLyser, CSforAll Consortium
Abstract:
There is a growing international movement to provide every child access to high-quality computing education. Despite the widespread effort, most children in the US do not take any computing classes in primary or secondary schools. There are many factors that principals and districts must consider when determining whether to offer CS courses. The process through which school officials make these decisions, and the supports and barriers they face in the process, is not well understood. Once we understand these supports and barriers, we can better design and implement policy to provide CS for all.
In my thesis, I study public high schools in the state of Georgia and the supports and barriers that affect offerings of CS courses. I quantitatively model school- and county-level factors and the impact these factors have on CS enrollment and offerings. The best regression models include prior CS enrollment or offerings, implying that CS is likely sustainable once a class is offered. However, large unexplained variances persist in the regression models.
To help explain this variance, I selected four high schools and interviewed principals, counselors, and teachers about what helps, or hurts, their decisions to offer a CS course. I build case studies around each school to explore the structural and people-oriented themes the participants discussed. Difficulty in hiring and retaining qualified teachers in CS was one major theme. I frame the case studies using diffusion of innovations providing additional insights into what attributes support a school deciding to offer a CS course.
The qualitative themes gathered from the case studies and the quantitative factors used in the regression models inform a theory of supports and barriers to CS course offerings in high schools in Georgia. This understanding can influence future educational policy decisions around CS education and provide a foundation for future work on schools and CS access.
My group will be presenting two posters at ICER this year.
Bahare Naimipour (Engineering Education Research PhD student at U-Michigan) will be presenting our participatory design session with social studies educators, Helping Social Studies Teachers to Design Learning Experiences Around Data–Participatory design for new teacher-centric programming languages. We had 18 history and economics teachers building data visualizations in either Vega-Lite or JavaScript with Google Charts. Everyone got the starter visualization running and made changes that they wanted in less than 20 minutes. Those who started in Vega-Lite also tried out the JavaScript code, but only about 1/4 of the JS groups moved to Vega-Lite successfully.
Miranda Parker (Human-Centered Computing PhD student at Georgia Tech) will be presenting her quantitative model explaining about half of the variance in whether Georgia high schools taught CS in 2016, A Statewide Quantitative Analysis of Computer Science: What Predicts CS in Georgia Public High School. The most important factor was whether the school taught CS the year before, suggesting that overcoming inertia is a big deal — it’s easier to sustain a CS program than start one. She may talk a little about her new qualitative work, where she’s studying four schools as case studies about their factors in choosing to teach CS, or not.
Barbara is co-author on a paper, A Spaced, Interleaved Retrieval Practice Tool that is Motivating and Effective, with Iman Yeckehzaare and Paul Resnick . This is about a spaced practice tool that 32% of the students in an introductory programming course used more than they needed to, and the number of hours of use had a measurable positive effect on the final exam grade.
All of our other papers were rejected this year, but we’re in good company — the accept rate was around 18%. But I do want to talk about a set of papers that will be presented by others at ICER 2019. These are papers that I heard about, then I asked the authors for copies. I’m excited about all three of them.
How Do Students Talk About Intelligence? An Investigation of Motivation, Self-efficacy, and Mindsets in Computer Science by Jamie Gorson and Eleanor O’Rourke (see released version of the paper here)
One of the persistent questions in computing education research is why growth mindset interventions are not always effective (see blog post here). We get hard-to-interpret results. I met Jamie and Nell at the Northwestern Symposium on Computer Science and the Learning Sciences in April (amazing event, see here for more details). Nell worked with Carol Dweck during her graduate studies.
Jamie and Nell found mixed mindsets among the CS students that they studied. Some of the students they studied had growth mindsets about intelligence, but their talk about programming practices showed more fixed mindset characteristics. Other students self-identified as having some of both growth and fixed mindset beliefs.
In particular, some students talked about intelligence in CS in ways that are unproductive when it came to the practice of programming. For example, some students talked about the best programmers as being able to write the whole code in one sitting, or never getting any errors. A more growth mindset approach to programming would be evidenced by talking about building programs in pieces, expecting errors, and improving through effort over time.
This is a really helpful finding. It gives us new hypotheses to explore about why growth mindset interventions haven’t been as successful in CS as in other disciplines. Few disciplines have this strong distinction between their knowledge and their practice as acutely as we do in CS. It’s no wonder that we see these mixed mindsets.
Toward Context-Dependent Models of Productive Knowledge in Programming Cognition, by Brian A. Danielak
I’ve known Brian since he was a PhD student, and have been hoping that he’d start to publish some of his dissertation work. I got to read one chapter of it, and found it amazingly insightful. Brian explained how what we might see as a “random walk” of syntax was actually purposeful and rational behavior. I was excited to hear about this paper, and I enjoyed reading it.
It’s such an unusual paper for ICER! It’s empirical, but has no methods section. A big part of it is connecting to prior literature, but it’s not about a formal literature review.
Brian is making an argument about how we characterize knowledge and student success in CS. He points out that we often talk about students being wrong and having misconceptions, which is less productive than figuring out what they understand and where their alternative conceptions work or fail. I see his work following on to the work of Rich et al. (mentioned in this blog post) on CS learning trajectories. There are so many things to learn in CS, and sometimes, just getting started on the trajectory is a big step.
Spatial Encoding Strategy Theory: The Relationship between Spatial Skill and STEM Achievement by Lauren Margulieux.
Lauren is doing some impressive theoretical work here. She’s considering the work exploring the relationship between spatial reasoning and CS learning/performance, then constructs a theory explaining the observed results. Since it’s Lauren, the theory is thorough and covers well the known results in this space. I wrote her that I didn’t think that theory explains things that we expect are related to spatial reasoning, but we don’t yet have empirical evidence to support it. For example, when programmers simulate a program in their mind, their mental models may have a spatial component to them, but I don’t know of empirical work that explores that dimension of CS performance. But again, since it’s Lauren, I wouldn’t be surprised if her presentation addresses this point, beyond what was in the paper. (Also, read Lauren’s own summary of the paper here.)
I am looking forward to the discussion of these papers at ICER!
My Blog@CACM for this month is about Code.org’s decision to shift gradually the burden of paying for CS professional development to the local regions — see link here. It’s an important positive step that needs to happen to make CS sustainable with the other STEM disciplines in K-12 schools.
We’re at an interesting stage in CS education. 40-70% of high schools have CS, but the classes are pretty empty. I use Indiana and Texas as examples because they’ve made a lot of their data available. Let’s drill a bit into the Texas data to get a flavor of it, available here. I’m only going to look at Area 1’s data, because even just that is deep and fascinating.
Brownsville Intermediate School District. 13,941 students. 102 in CS.
Of the 10 high schools in Brownsville ISD, only two high schools have anyone in their CS classes. Brownsville Early College High School has 102 students in CS Programming (no AP CS Level A, no AP CSP). That probably means that one teacher has several sections of that course — that’s quite a bit. The other high school, Porter Early College High School has fewer than five students in AP CS A. My bet is that there is no CS teacher there, only five students doing an on-line class. That means for 10 high schools and 13K students, there is really only one high school CS teacher.
Edinburg Consolidated Independent School District, over 10K students, 92 students in CS.
This is a district that could grow CS if there was will. There are 6 high schools, but two are special cases: One with less than 5 students, and the other in a juvenile detention center. The other four high schools are huge, with over 2000 students each. In Economedes, that are only 9 students in AP CS A — maybe just on-line? Edinburg North and Robert R Vela high school each have two classes: AP CS A and CS1. With 21 and 14, I’m guessing two sections. The other has 43 and 6. That might be two sections of AP CS A and another of CS1, or two sections of AP CS A and 6 students in an on-line class. In any case, this suggests two high school CS teachers (maybe three) in half of the high schools in the district. Those teachers aren’t teaching only CS, but with increased demand and support from principals, the CS offerings could grow.
It’s fascinating to wander through the Texas data, to see what’s there and what’s not. I could be wrong about what’s there, e.g., maybe there’s only one teacher in Edinburg and she’s moving from school-to-school. Given these data, there’s unlikely to be a CS teacher in every high school, who just isn’t teaching any CS. These data are a great snapshot. There is CS in Texas high schools, and maybe there’s enough there to take root and grow.
Jeff Yearout has been teaching for over 25 years, and is just in his second year of teaching CS. His concerns in his blog echo many of the same ones that I hear from higher-education CS teachers, e.g., dealing with the wide variance of students, and getting all students to engage around code (pseudo or otherwise).
I think one of the hardest things to manage in designing a curriculum is how to dial the difficulty up at a proper pace for the “center mass” of the class skill level. And in this new curriculum from PLTW this particular unit starts out manageable, but suddenly shoots up rapidly, thus the “code shock” mentioned above. I also have the challenge of having a lot of kids in class who simply don’t want to interact in class when, for instance, I’m working through pseudocode on the board.
UTeach has published a nice blog post that explains (with graphs!) the ideas that I alluded to in my Blog@CACM post from last month. While currently CS teacher production is abysmal, UTeach prepared CS teachers tend to stay in their classrooms for more years than I might have expected. More, there is evidence that suggests that there is significant slice of the CS undergraduate population that would consider becoming teachers if the conditions were right. There is hope to imagine that we can making produce more CS teachers, if we work from the University side of the equation. Working from the in-service side is too expensive and not sustainable.
Michael Marder, Professor of Physics and Executive Director of UTeach, and Kim Hughes, Director of the UTeach Institute, write…
The number of computer science and computer science education teachers prepared per year is smaller than for any other STEM subject — even engineering and physics — and while estimates vary, it is safe to say it is on the order of 100 to 200 per year, compared to the thousands of biology or general science teachers prepared.
The U.S. has around 24,000 public and 10,000 private high schools. Only 10% to 25% have been offering computer science, so to provide all of them with at least one teacher at the current rate simply looks impossible.
The Washington Post got it wrong when it announced that Virginia is the first state to mandate CS education for all students. South Carolina has had that mandate for 30 years. But they couldn’t prepare enough teachers to teach computer science, so they took classes they were already teaching (like “keyboarding”) and counted those as CS classes.
Virginia could fall into the same trap, but I don’t think so. Instead, I predict that most Virginia high school students will take CS on-line (and that likely goes for the rest of the US, too). I was struck by how the Richmond-Times Dispatch described the vote to mandate CS (below quoted from here):
The standards, approved unanimously, but reluctantly, by the state Board of Education on Thursday, are a framework for computer science education in the state. Other states have advisory standards, but Virginia became the first to have mandatory standards.
Board member Anne Holton voiced her concern with the grade level appropriateness of the standards before the vote.
“The standards, they seem ambitious to me,” she said. “These are not meant as aspirational standards, they are meant as a mandate that our teachers need to be able to teach.”
“We’re clearly leading the nation and that puts an extra burden on us to get it right.”
Mark Saunders, the director of the Education Department’s Office of Technology and Virtual Learning, led a presentation of the department’s process in adopting the standards.
The presentation satisfied the board enough to vote on the standards rather than delay action until January.
I’m reading between the lines here, but I’m guessing the process went something like this: Board members balked at a statewide mandate because they knew they didn’t have the teachers to support it. There certainly are CS teachers in Virginia, many of them prepared by CodeVA. But not enough to support a statewide mandate. Then they were assured that the Virtual Learning system could handle the load, so they voted for it (“reluctantly” as the article says).
I don’t know that anybody’s tracking this, but my guess is that it’s already the case that most high school students studying CS in the United States are doing it online. Since we are not producing enough new CS teachers, the push to grow CS education in high schools is probably going to push more CS students online. This is how schools in Arkansas and other states are meeting the requirements for schools to offer CS — simply make the virtual high school CS course available, and you’ve met the requirement. No teacher hiring or professional learning required. I know from log file analyses that we are seeing huge numbers of students coming into our ebooks through virtual high school classes.
I don’t usually blog on a Saturday, but this is huge.
In this week’s address, the President discussed his plan to give all students across the country the chance to learn computer science (CS) in school. The President noted that our economy is rapidly shifting, and that educators and business leaders are increasingly recognizing that CS is a “new basic” skill necessary for economic opportunity. The President referenced his Computer Science for All Initiative, which provides $4 billion in funding for states and $100 million directly for districts in his upcoming budget; and invests more than $135 million beginning this year by the National Science Foundation and the Corporation for National and Community Service to support and train CS teachers. The President called on even more Governors, Mayors, education leaders, CEOs, philanthropists, creative media and technology professionals, and others to get involved in the efforts.
The argument made in Wired is an interesting one, and I partially buy it. Are high school and elementary schools the right places to teach programming to everyone? Does everyone at that level need to learn to program? What are we giving up by teaching coding? Here’s one possible scenario, a negative one but a likely one: We push CS into K-12 schools, but we can’t get everywhere. The rich schools are getting it first, so we run out of money so that we get to all rich schools and no poor schools. Computing education is now making larger the difference between the rich and the poor.
So is it wrong to teach a person to code? No. I don’t deny that coding is a useful skill to have in a modern ubiquitous computing society. It can help people personalize and understand the devices and services they use on a daily basis. It’s also good news that methods for teaching kids how to code are improving and becoming more effective, or that kids can ostensibly learn on their own when left to their own devices. The problem is elevating coding to the level of a required or necessary ability. I believe that is a recipe for further technologically induced stratification. Before jumping on the everybody-must-code bandwagon, we have to look at the larger, societal effects — or else risk running headlong into an even wider inequality gap. For instance, the burden of adding coding to curricula ignores the fact that the English literacy rate in America is still abysmal: 45 million U.S. adults are “functionally illiterate” and “read below a 5th grade level,” according to data gathered by the Literacy Project Foundation. Almost half of all Americans read “so poorly that they are unable to perform simple tasks such as reading prescription drug labels.” The reading proficiency of Americans is much lower than most other developed countries, and it’s declining.
Computational literacy is important, and school age is where to develop it. Programming can be a useful medium for learning the rest of STEM, so learning programming early can support later learning.
Eventually. That is the desired end-state.
We should focus on universal computing education in higher-ed before putting CS into K-12 classrooms: The problem is that we’re nowhere near that goal now. Less than 10% of NYC schools offer any kind of computer science, and less than 10% of US high schools offer AP CS. I argue that we should require computer science in colleges and universities in the US first, and then in K-12 classrooms, so that the teacher come out of undergraduate already knowing how to program and use it in their classes. I worry that if we can’t make required computer science happen in higher ed, the costs for getting it into all of K-12 are too large — so only the rich will get it. I worry also about the kinds of arguments we make. If we can’t make universal computational literacy happen in higher ed, what right do we have to force it on all the high schools and elementary schools? “This isn’t good for us, but it’s good for you”?
The biggest challenge in growing computing education in K-12 is finding enough teachers. Programs like TEALS are stop-gap measures. We need to recruit teachers to meet the needs in NYC. Most professional development programs are under-subscribed — there are lots of empty seats. How do we convince teachers to go take extra classes in computing, especially if they’re already an established teacher in some other discipline? If we taught everyone computing in undergraduate, we’d teach all the pre-service teachers. We wouldn’t have to do extra in-service professional development. (Pre-service education is much less expensive to implement than in-service. In-service teachers get paid to attend workshops. Pre-service is funded by tuition.)
We absolutely should be doing research on how to put computing into K-12 schools. I am concerned about the costs of large scale implementation before we know what we’re doing — both in terms of making it work, and in what happens when it doesn’t.
Literacy starts with community:Situated learning is a theory which explains why people learn. Students learn to join a community of practice. They want to be like people that they admire, to adopt their values and practices. Think about computing education from a situated learning perspective. Let’s imagine that reading has just been invented. It’s a powerful literacy, and it would be great to teach it to young kids so that they can use it for their whole lives and all their years of schooling. But if we try to teach it to them before many adults are reading and writing, it comes off as inauthentic. You can imagine a child thinking, “Why should I learn to read? The only people who read are monks and professors. I don’t want to be like that.” If few people read, then few people write. There’s not even much for the children to read.
I suspect that textual literacy was first learned by adults before it became a school subject. Adults learned to read and write. They wrote books and newspapers, and used reading in their daily lives. Eventually, it became obvious that children should be taught to read.
Today, children don’t see a world of computational literacy. Children don’t see many adults writing bits of code to do something useful or something beautiful or something enlightening. You can imagine a child thinking, “Why should I learn to program? The only people who program are geeky software developers and professors. I don’t want to be like that. And even if I did want to be like them, the geeky software developers don’t use Scratch or Blockly or App Inventor.” Students today are not immersed in a world of code to explore and learn from. Most programs that are available to study are applications. Studying existing programs today is like learning to read only with legal documents or the Gutenberg Bible. Where are the McGuffy Readers of code, or the Dr Seuss of imaginative programs? Those would be expected produces from a computationally literate society. A generation of college-educated programming professionals would help to create that society.
If you want students to gain literacy, place them in a community that is literate. That’s what Seymour Papert was talking about when he described Logo as a Mathland. We need a community of adults who program if we want children to grow up seeing programming as something natural, useful, and desirable.
The importance of getting it right: I was recently at a meeting for establishing a Framework for K-12 Computer Science Education, and Michael Lach spoke (see a description of him here). He warned curriculum writers and state/district leaders to go slow, to get it right. He pointed out that if we get it wrong, administrators and principals will decide that “Computing can’t be taught to everyone. It really is just for the geeky white boys.” And we’ll lose decades towards making computing education available to everyone. (Lach’s talk was deep and insightful — I’ll say more about it in a future blog post.) We have to get it right, and it’s better to go slow than to create computing education just for the rich.
We now have TWO ebooks supporting CS Principles (see website here) now available — one for teachers and one for students.
Our teacher ebook summer study is now ended. (Announcement about launching the study is here.) We’re crunching the data now. We’ve already learned a lot about what teachers want in an ebook. We learned where our user interface wasn’t obvious, and where we needed to explain more. We learned that teachers expect end-of-chapter exercises. We have used what we have learned so far to produce the two new ebooks.
STUDENT CSP EBOOK: About a year ago, we received additional NSF funding (from the Improving Undergraduate STEM Education (IUSE) program) to develop a student version of our CSP ebook. We have been running participatory design studies and gathering usability surveys from students to get input on what a student ebook should look like. We have now released the first version of the student ebook.
The student CSP ebook is available at http://interactivepython.org/runestone/static/StudentCSP/index.html It doesn’t require a login, but we recommend that teachers have their students login. Without a login, we store saved answers on the local computer, but if the student logs in, we save the answers by the student’s username. The course name is StudentCSP.
We recommend that teachers create a custom version of the student ebook for your students. This allows teachers to customize the ebook, assign homework, and view student’s progress, and even create additional assessments for students.
New Version TEACHER CSP EBOOK: We iterated on our teacher ebook at the same time that we were developing the student ebook. We hypothesize that the student CSP ebook may actually encourage teachers to complete the teacher ebook. We can imagine that teachers who use the student ebook might want to stay one step ahead of the students, e.g., “My students are starting Chapter 3 on Monday, so I better finish Chapter 3 this weekend.”
We have now created a second version of our teacher CSP ebook. This one is in lockstep with the student CSP ebook, includes all the end-of-chapter exercise answers and teacher notes (e.g., on how to teach particular concepts, common student difficulties, etc.). We are not making the second teacher ebook available openly (because it includes answers to the student problems).
Teachers, please contact us at cslearn4u@gmail.com with the name and location of your school, and we’ll send you the URL.
You must register on Runestone first at http://interactivepython.org/runestone/default/user/register. Enter StudentCSP as the course name. Be sure to record your username. We find that users often forget what they entered and assume it was their e-mail address — and it may not have been. You can also choose to sign in with your account on Google Plus, Facebook, Twitter, or several others.
Create a unique name for your course (use your school name and StudentCSP and year maybe), add a description, and your institution, and then select “CS Principles: Big Ideas in Programming by Mark Guzdial, Barbara Ericson, and Briana Morrison“.
Leave the rest as defaults and click the “Submit” button. This will build a custom version of the student ebook for your students and it will have a unique URL and course name. You will be listed as the instructor and can look at the log files and view other information on the instructor page (you can get to this by clicking on the icon that looks like a head and shoulders and the top right of your screen when you are in the ebook).
The linked blog post below bemoans the fact that the AP CS is growing, perhaps at the expense of growth in AP Statistics. AP Stats is still enormously successful, but the part of the post that’s most interesting is the author’s complaints about what’s wrong with CS. I read it as, “Students should know that CS is not worthy of their attention.”
It’s always worthwhile to consider thoughtful critiques seriously. The author’s points about CS being mostly free of models and theories is well taken. I do believe that there are theories and models used in many areas of CS, like networking, programming languages, and HCI. I don’t believe that most CS papers draw on them or build on them. It’s an empirical question, and unfortunately, we have the answer for computing education research. A recent multi-national study concluded that less than half of the papers in computing education research draw on or build on any theory (see paper here).
Though the Stat leaders seem to regard all this as something of an existential threat to the well-being of their profession, I view it as much worse than that. The problem is not that CS people are doing Statistics, but rather that they are doing it poorly: Generally the quality of CS work in Stat is weak. It is not a problem of quality of the researchers themselves; indeed, many of them are very highly talented. Instead, there are a number of systemic reasons for this, structural problems with the CS research “business model.”
Hadi Partovi of Code.org has a blog post (see here) with data from their on-line classes. He’s making the argument that classroom teachers are super important for diversity and for student success.
Learning #1: Classrooms progress farther than students studying alone
In the graph below, the X axis is student age, the Y axis is their average progress in our courses. The blue line is students in classrooms with teachers. The red line is students studying without a classroom/teacher.
Learning #3: The ethnic backgrounds of students with teachers are impressively diverse
The data below doesn’t come from all students, because (for privacy reasons) we do not allow students to tell us their ethnic background. This chart was collected via an opt-in survey of teachers in the U.S. offering our courses, and as such is susceptible to inaccuracy. The picture it paints helps confirm our thesis that by integrating computer science into younger-aged classrooms in public schools, we can increase the diversity of students learning computer science.
A new survey from both CSTA and Oracle. None of the findings are too surprising. What’s probably surprising is that this picture doesn’t seem too different from past CSTA surveys (see list of all of them here). Efforts like the Hour of Code are reaching lots of students, but may not yet be making much impact on most schools and districts.
In addition, participants applied the term “computer science” to a vast array of topics and courses, many of which were submitted as “other” courses in response to the topics that were provided in the survey. Participants classified studies in business management, yearbook layout, artificial intelligence, robotics, office applications, and automated design as computer science courses. This broad use of “computer science” to encompass curriculum and courses that would not be considered “computer science” at a college/university or professional level indicates a need for educational community consensus on a common definition of computer science education and curricular content, lest we lead students or teachers to believe they are preparing students for college and careers when in fact, they are not. This perhaps begs the question whether “computer science” as a designation is being applied inappropriately for funding or other reasons.
Administrators stated that the most prevalent computer science course offered was Web Design and Development, followed by Intro to Computer Science with 54% of the schools offering it in grade 9, 47% offering it in grade 10, 39% offering it in grade 11, 37% offering it in grade 12, and only 27% offering at least one intro to CS course all four years. These were followed by computer graphics and programming. The top four content areas covered in computer science courses were listed as problem solving at 65%, ethical and social issues and graphics tied at 57%, and web development at 51%. However, analysis of algorithms came in at 35% as did testing and debugging. Each of these content areas are core to computer science and in particular programming.
One of the most important findings from the study suggests that better-funded schools are offering CS to their students at a far higher rate than low-income schools. This research verifies what was only previously suspected. Of the 27% of schools where the majority of students qualify for free or reduced lunch, 63% offer computer science courses. Of the 44% of schools where the majority of students do not qualify for free lunch, 84% offer computer science courses.
There are lots of these kinds of lists around the beginning of a new year, but I thought that these predictions were interesting. I’m betting that the first one below is right, but I know a lot of people are betting against it. I’m seeing the second one in my discussions with K12 education policymakers in states. They want their students to come out with “job skills,” which is hard to do with an introduction to computing designed for students who have no previous background.
10. Online learning will grow modestly (Eduventures): The company predicts that enrollment in wholly online degree programs will be modest this year, with only 2 percent growth due mostly to uncertainty and indecision among adult learners. At the same time, the percentage of colleges entering the online market will grow very little, if at all. “Growth will be stunted due to increased regulatory concerns such as state authorization, competition from large adult-serving providers, and enrollment strategies incapable of keeping pace with the savvyness of today’s adult learners,” it stated. “Institutions will back away from online programming to focus on blended learning and improving quality and access for traditional age students.”
11. Outcomes will dominate (Eduventures): Eduventures research shows that in 2013, “career preparation” surpassed “academic strength” as the top priority for both students and parents in selecting a school. Adding to parent and student concerns, the government has increased its focus on this issue, including the possibility of Title IV funding consequences. “Look for schools to become more aggressive in differentiating themselves in reporting outcomes data in 2015,” said the company.
The Computer Science Teachers Association (CSTA) announces its search for an Executive Director. The Executive Director must be deeply committed to CSTA’s core mission, which is to empower, support and advocate for K-12 computer science teachers worldwide. The Executive Director reports to and works collaboratively with the Board of Directors to set strategic direction, develop goals, attain/manage resources, and establish policies for the organization.
The Executive Director is responsible for the organization’s consistent achievement of its mission and financial objectives and ensures ongoing programmatic excellence, rigorous program evaluation, and consistent quality of finance, administration, fundraising, communications, and organizational systems.
This is a full-time position. The Executive Director manages a staff including an Assistant Director and four part-time administrators (meeting planner, web developer, project coordinator, and newsletter editor), and conducts their work from a virtual office. Considerable travel is required.
Computing Ed Research - Guzdial's Take 
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