Posts tagged ‘public policy’

Defining CS Ed out of existence: Have we made CS too hard to learn and teach?

It was this quote in a tweet from Miles Berry that really made me sit up and take notice of the latest news about the Computing at School initiative:

“If computing increasingly means CS, it looks likely that hundreds of thousands of students, particularly girls and poorer students, will be disenfranchised from a digital education over the next few years.”

He was quoting an article from the New Statesman which can be found here. It describes the history of the rise of the CS curriculum in England. The key paragraph for me is:

The new curriculum was failing. While a tougher course had been introduced, few students were taking it and even fewer teachers could teach it. In many cases, even those who could felt uncomfortable doing so.

The government read the reports and has decided to respond. There’s now an enormous investment in England in trying to train new teachers. The question is whether that’s the right investment.

Meanwhile, in Scotland, the headline of this May 2019 article is “Teachers and students in decline: the computing ‘crisis’ in Scotland’s schools.”

Experts are urging the Scottish Government to take radical steps to boost computing science education to prevent the subject from being squeezed out of schools.

The teaching of computing in schools is in “crisis”, practitioners have told The Ferret, with classes shrinking and teachers in short supply. The latest official data shows that the number of children studying the subject declined last year, while the number of teachers has fallen over the last decade.

Despite a national focus on delivering science and technology education and economic development, schools are finding it increasingly difficult to teach computing science to young people, critics say.

Let’s explicitly consider the questions raised in these two articles. Have we defined CS education in such a way that it’s too hard to teach? That it’s not interesting to learn? Maybe that it’s too hard to learn?

I’ve been writing in the last few months about the surprisingly low uptake of CS education in the United States (for example, in this CACM Blog post). No more than 5% of high school students in any US state are getting any CS classes, from the data available. There is value in setting high standards for CS education (as Alan Kay has been arguing), but that’s an argument for the end goal. Where do we start with CS education? How quickly can and will students learn CS education? What does it mean for something to be too hard to teach or too hard to learn?

Overall, US is following a similar strategy as in England and Scotland for computing in K-12: standalone CS classes, heavy emphasis on in-service teacher development, and counting the number of students in CS classes and the number of teachers leading those classes. There is integrated CS in the US, but as far as I know, no state is tracking those numbers. Public policy tends to focus on things that can be measured. Most of the argument against integration says that too little CS is covered in integrated forms. 95% of US students getting no CS at all is even less coverage than CS in integrated forms.

Let’s consider two hypotheses:

Hypothesis #1: We know how to teach computer science in such a way that all students can learn what they need to be technically-literate citizens, or even to develop the prerequisite knowledge they need to be software professionals. We have not yet achieved this goal because we do not have enough teachers to implement the curriculum. Larger investments in teacher development (perhaps including stipends or better pay to CS teachers) would allow us to scale CS Ed to reach everyone.

Hypothesis #2: We have defined computer science education in a way that is too hard to teach (so too few teachers are unwilling to teach it), and that is too hard to learn (which includes not being motivating enough to recruit students or engage student interest in order to achieve learning).

Given the evidence we have in the US, England, and Scotland, which hypothesis is better supported? You may have a Hypothesis #3 or #4 which is also well-supported by the evidence — I am very interested in hearing it.

In general, we tend to take the “insider view” of CS Ed, as Kahneman warned about (see excerpt here). If you step outside CS Ed, are we making progress along a trajectory that leads to CS education for all? And how long is that trajectory? If you were an Education faculty member and learned that CS had less than 5% of US high school students enrolled, wouldn’t it be reasonable to consider it a fad and likely to pass?

As I wrote in my blog post about what I got wrong in the last decade, I no longer think that CS for All is a matter of access. We have to figure out how to improve participation. I’m in support of Hypothesis #2. We need to re-think what and how we teach CS education. Because of my work these days, I suspect that we made a mistake at the design level. I was involved in the early days of the AP CS Principles (AP CSP) process. Most of the AP CSP curricula I’m aware of were developed by and tested with some of the best CS teachers in the US. That design and development process doesn’t promise a curriculum that many teachers can teach and that most students will learn from.

I just got back from a three day visit in Norway, where they are about to roll-out an integration of CS activities (explicitly programming) into mathematics, science, music, and arts & crafts classes. (See workshop about this topic here.). Maybe that would result in more students learning some computer science. Did US, England, and Scotland make a mistake by emphasizing standalone CS classes over integration?

March 9, 2020 at 7:41 am 21 comments

An Analysis of Supports and Barriers to Offering Computer Science in Georgia Public High Schools: Miranda Parker’s Defense

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.

October 7, 2019 at 7:00 am 1 comment

Why high school teachers might avoid teaching CS: The role of industry

Fascinating blog post from Laura Larke that helps to answer the question: Why isn’t high school computing growing in England?  The Roehampton Report (pre-release of the 2019 data available here) has tracked the state of computing education in England, which the authors describe as a “steep decline.” Laura starts her blog post with the provocative question “How does industry’s participation in the creation of education policy impact upon what happens in the classroom?” She describes teachers who aim to protect their students’ interests — giving them what they really need, and making judgments about where to allocate scarce classroom time.

What I found were teachers acting as gatekeepers to their respective classrooms, modifying or rejecting outright a curriculum that clashed with local, professional knowledge (Foucault, 1980) of what was best for their young students. Instead, they were teaching digital skills that they believed to be more relevant (such as e-safety, touch typing, word processing and search skills) than the computer-science-centric content of the national curriculum, as well as prioritising other subjects (such as English and maths, science, art, religious education) that they considered equally important and which competed for limited class time.

Do we see similar issues in US classrooms?  It is certainly the case that the tech industry is painted in the press as driving the effort to provide CS for All.  Adam Michlin shared this remarkable article on Facebook, “(Florida) Gov. DeSantis okay with substituting computer science over traditional math and science classes required for graduation.” Florida is promoting CS as a replacement for physics or pre-calculus in the high school curriculum.

“I took classes that I enjoyed…like physics. Other than trying to keep my kids from falling down the stairs in the Governor’s mansion I don’t know how much I deal with physics daily,” the governor said.

The article highlights the role of the tech industry in supporting this bill.

Several top state lawmakers attended as well as a representative from Code.org, a Seattle-based nonprofit that works to expand computer science in schools. Lobbyists representing Code.org in Tallahassee advocated for HB 7071, which includes computer science initiatives and other efforts. That’s the bill DeSantis is reviewing.

A Microsoft Corporation representative also attended the DeSantis event. Microsoft also had lobbyists in Tallahassee during the session, advocating for computer science and other issues.

The US and England have different cultures. Laura’s findings do not automatically map to the US. I’m particularly curious if US teachers are similarly more dubious about the value of CS curricula if it’s perceived as a tech industry ploy.

 

July 29, 2019 at 7:00 am 3 comments

Barbara Ericson’s AP CS Report for 2018 and her new blog cs4all.home.blog

Barb has written her blog post about the 2018 AP data (see 2017 report here and 2016 report here), and this year, she’s using it to launch her own blog!  Find it at https://cs4all.home.blog/

Every year I gather and report on the data for AP CS from the College Board which is at http://research.collegeboard.org/programs/ap/data/

There was a huge increase in Advanced Placement (AP) Computer Science Principles (CSP) exam takers nationally (from 43,780 in 2017 to 70, 864 in 2018 – a 62% increase). The Computer Science A (CSA) exam also grew (from 56,088 in 2017 to 60,040 in 2018 – a 7% increase).

Source: AP CS Report for 2018

March 4, 2019 at 7:00 am 1 comment

The biggest concerns for institutionalized CS education in the United States: Standards, limited models, and undergraduate enrollment caps

I was interviewed for the SIGCSE Bulletin by my long-time collaborator, Leo Porter (see https://sigcse.org/sigcse/files/bulletin/bulletin.51.1.pdf).  I talk about this blog, how I started teaching in 1980, about Media Computation, and about what inspires me.

One of the questions relates to the recent discussion about standards and frameworks (see post here).

LP: You have worked with education public policymakers in “Georgia Computes!” and Expanding Computing Education Pathways (ECEP) over the last dozen years. What’s your biggest worry as US states start institutionalizing CS education?

I have two. The first is that the efforts to standardize CS education are making the bar too low. When the K-12 CS Ed Framework was being developed, decisions were being made based on how current teachers might respond. “Teachers don’t like binary, so let’s not include that” is one argument I heard. I realize now that that’s exactly the wrong idea. Standards should drive progress and set goals. Defining standards in terms of what’s currently attainable is going to limit what we teach for years. Computing education research is all about making it possible to teach more, more easily and more effectively. I worry about setting standards based on our limited research base, not on what we hope to achieve.

The second is that most of our decisions are being made around the assumption of standalone CS classes and having teachers with a lot of CS education. I just don’t see that happening at scale in the US. Even in the states with lots of CS teachers in lots of schools, a small percentage of students take those classes. This limits who sees computer science. To make CS education accessible for all, we have to be able to explore alternative models, like integrating computing education in other subjects without CS-specific teachers. If we only count success in CS education as having standalone CS classes, we are incentivizing only one model. I worry about building our policy to disadvantage schools that want to explore integrated models, or have to integrate because of the cost of standalone CS classes.

Since this interview, I have a third concern, that may be more immediate than the other two.  This is what I wrote my CACM Blog on this month. The NYTimes just published an article “The Hard Part of Computer Science? Getting Into Class” about the growing CS undergraduate enrollment and about the efforts by departments to manage the load.  Departments used to talk about building capacity, but increasingly, the discussion is about capping or limiting enrollments.  The reason why this is concerning is because we’ve been down this road before — see Eric Roberts’ history of CS capacity challenges. Our efforts to limit enrollment send a message about computer science being only for elites and being unwelcoming to non-CS majors. This is exactly opposed to the message that Code.org, CS for All, and the AP CS Principles exam is trying to send. We’re creating a real tension between higher education and the efforts to grow CS, and it may (as Eric suggests) send enrollments into the next dive.

February 18, 2019 at 7:00 am 8 comments

How to organize a state (summit): From ECEP and NCWIT

Soon after we started the Expanding Computing Education Pathways (ECEP) Alliance, we were asked: What should a state do first?  If they want to improve CS Education, what are the steps?

We developed a four step model — you can see a three minute video on ECEP that includes the four step model here. It was evidence-based in the sense that, yup, we really saw states doing this.  We had no causal evidence. I’m not sure that that’s possible in any kind of education public policy research.

One of those steps is “Organize.” Gather your allies. Have meetings where you CS Ed people rub elbows with the state public policymakers, like legislators and staffers in the Department of Education (or Department of Public Instruction, or whatever it’s called in your state).

A lot of states have had summits since then (see a list of some here).  Now, working with the fabulous NCWIT team of communicators, graphic designers, and social scientists, ECEP has released a state summit toolkit.  We can’t yet tell you how to organize a state. We can tell you how to organize a state summit.

From finding change agents to building a steering committee of diverse stakeholders, convenings play an important role in broadening participation in computing at the state level. ECEP and NCWIT have developed the State Summit Toolkit to assist leadership teams as they organize meetings, events, and summits focused on advancing K-16 computer science education.

From https://ecepalliance.org/summit-toolkit 

February 15, 2019 at 7:00 am Leave a comment

Need for Reviewers for US Department of Education CS Education Grants – Guest Post from Pat Yongpradit

Pat Yongpradit of Code.org asked me to share this with everyone.

The US Department of Education has announced the EIR grant competition for FY 2019. This year EIR incorporates an exclusive priority for computer science with a focus on increasing diversity and equity in access, as compared to last year where the highlight was that CS was merged with STEM as a combined priority. See more detail in our blog.

There are many moving parts to the federal grant review and award process, including a merit-based review process. In order to adequately score grants featuring computer science, the US Department of Education must have enough reviewers with K-12 computer science education experience. There is more information on the merit-review process and the Department’s mechanism for selecting reviewers in this blog.

Code.org has been asked to put interested folks in touch with leaders of the EIR grant program. If interested, please send your CV to EIRpeerreview@ed.gov.

Having CS knowledgeable reviewers participating in the federal grant review process is crucial to maximizing the opportunity these grants present the field and our collective goal of expanding access to K-12 computer science.

Best,

Pat

February 14, 2019 at 9:45 am Leave a comment

Frameworks and Standards can be limiting and long-lasting: Alan Kay was right

Through the K-12 CS Framework process (December 2016, see the post here), Alan Kay kept saying that we needed real computer science and that the Framework shouldn’t be about consensus (see post here). I disagreed with him. I saw it as a negotiation between academic CS and K-12 CS.

I was wrong.

Now that I can see standards efforts rolling out, and can see what’s actually going into teacher professional development, I realize that Alan was right. Standards are being written to come up to but rarely surpass the Framework. All those ideas like bits and processes that I argued about — they were not in the Framework, so they are not appearing in Standards. The Framework serves to limit what’s taught.

Teachers are experts on what is teachable, but that’s not what a Framework is supposed to be about. A Framework should be about what the field is about, about what’s important to know. Yes, it needs to be a consensus document, but not a consensus about what goes into classrooms. That’s the role of Standards. A Framework should be a consensus about what computing is.

I think what drove a lot of our thinking about the Framework is that it should be achievable.  There was a sense that states and organizations (like CSTA and ISTE) should be able to write standards that (a) meet the Framework’s goals and (b) could be measurably achieved in professional development — “Yup, the teachers understand that.” As I learn about the mathematics and science frameworks, it seems that their goal was to describe the field — they didn’t worry about achievable.  Rather, the goal was that the Framework should be aspirational. “When we get education right for all children, it should look like this.”

Standards are political documents (something Mike Lach taught me and that Joan Ferrini-Mundy told ECEP), based on Frameworks. Because the K-12 CS Framework is expected to reflect the end state goal, Standards are being written a step below those. Frameworks describe the goals, and Standards describe our current plans towards those goals. Since the Framework is not aiming to describe Computer Science, neither do the state Standards that I’m seeing.

I told Alan about this realization a few weeks ago, and then the Georgia Standards came out for review (see page here). They are a case in point. Standards are political documents. It matters who was in the room to define these documents in this way.

Here’s the exemplar standard from the Grade 6-8 band:

Use technology resources to increase self-direction and self-regulation in learning, including for problem solving and collaboration (e.g., using the Internet to access online resources, edit documents collaboratively)

Can technology resources increase self-direction and self-regulation in learning? Maybe — I don’t know of any literature that shows that. But even if it can, why are these in the Computer Science standards?

The K-2 band comparable Standard is even more vague:

Recognize that technology provides the opportunity to enhance relevance, increase confidence, offer authentic choice, and produce positive impacts in learning.

I have no idea if computers can “increase confidence,” but given what we know about self-efficacy and motivation, I don’t think that’s a common outcome. Why is this in the Computer Science Standards?

There are lots of uses of the word “information.” None of them define information. The closest is here (again, grades 6-8), which lists a bunch of big ideas (“logic, sets, and functions”) but the verb is only that students should be able to “discuss” them:

Evaluate the storage and representation of data; Analyze how data is collected with both computational and non-computational tools and processes

  1. Discuss binary numbers, logic, sets, and functions and their application to computer science
  2. Explain that searches may be enhanced by using Boolean logic (e.g., using “not”, “or”, “and”)

What’s missing in the Framework is also missing in the Georgia standards.

  • The word “bit” doesn’t appear anywhere in these standards — if there is no information, then it makes sense that students don’t need bits.
  • The word “process” does, but mostly in the phrase “design process.” Then it shows up in the Grade 6-8 band, but in highly technical forms: “process isolation” and “boot process.”
  • There are no names: No Turing, no Hopper. There is no history, so no grounding in where computer science came from and what the big and deep ideas are.

There are strange phrases like “binary language,” which I don’t understand.

This is from Georgia, where there is a strong video game development lobby. Thus, all students are expected (by Grades 6-8) to:

Develop a plan to create, design, and build a game with digital content for a specific target market.

And

Develop a visual model of a game from the Game Design Document (GDD).

And

Create a functional game, using a game development platform, based on the storyboards, wireframes, and comprehensive layout.

It’s clear that the Georgia Standards are the result of a political process.

The bottom line is that I now wish that we had made sure that the K-12 CS Framework reflected computer scientists’ understanding of Computer Science. It instead reflected K-12 classroom computer science as defined in 2016. They presume languages like Scratch and curricula like AP CS Principles.  That’s reasonable in Standards that describe what goes into the classroom tomorrow, but Frameworks should describe a broader, longer-range thinking. Our

There are no plans that I’m aware of to define a new Framework. The Standards are still just being developed for many states, so they’re going to last for years. This is what Computer Science will be in the United States for the next couple decades, at least.

January 21, 2019 at 7:00 am 47 comments

Analyzing CS in Texas school districts: Maybe enough to take root and grow

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.

Computer_Science_Regional_Data___STEM_Center___The_University_of_Texas_at_Austin

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.

Computer_Science_Regional_Data___STEM_Center___The_University_of_Texas_at_Austin-3

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.

 

October 19, 2018 at 7:00 am 2 comments

A high-level report on the state of computing education policy in US states: Access vs Participation

states-policyInteresting analysis from Code.org on the development of policies in US states that promote computing education — see report here, and linked below.  The map above is fascinating in that it shows how much computing education has become an issue in all but five states.

The graph below is the one I found confusing.

urm-access

I’ve been corrected: the first bar says that where the school’s population is 0-25% from under-represented minority groups, 41% of those schools teach CS.  Only 27% of mostly-minority schools (75%-100% URM, in the rightmost column) offer CS.  This is a measure of which schools offer computer science.

The graph above doesn’t mean that there are any under-represented minority students in any CS classes in any of those high schools.  My children’s public high school in Georgia was over 50% URM, but the AP CS class was 90% white and Asian kids.  From the data we’ve seen in Georgia (for example, see this blog post), few high schools offer more than one CS class. Even in a 75% URM high school, it’s pretty easy to find 30 white and Asian guys.  Of course, we know that there are increasing numbers of women and under-represented minority students in computer science classes, but that’s a completely different statistic from what schools offer CS.

I suspect that the actual participation of URM students in CS is markedly lower than the proportion in the school.  In other words, in a high school with 25% URM, I’ll bet that the students in the CS classes are less than 25% URM.  Even in a 75% URM high school, I’ll bet that CS participation is less than 75% URM.

Access ≠ participation.

Source: The United States for Computer Science – Code.org – Medium

October 12, 2018 at 7:00 am 11 comments

ECEP has a new home at The University of Texas at Austin: First meeting this week at CSforAll

I can’t tell you how exciting this press release is for me.  Rick Adrion, Renee Fall, Barbara Ericson, and I started the Expanding Computing Education Pathways Alliance (http://ecepalliance.org) in 2012 to provide states with support as they broadened participation in computing education.  Six years later, we had 16 states and Puerto Rico involved — but we were ready to be done.  We all four had worked on previous alliances (CAITE and Georgia Computes) and felt that the movement needed new leaders.  I am so very pleased that Carol Fletcher and her wonderful team decided to carry on ECEP, and NSF has agreed to continue funding ECEP as it expands to TWENTY-THREE states and US territories!

ECEP (now based out of UT-Austin) will have its first meeting this week, at Wayne State University in Detroit (where Barbara and I first met in 1983) as part of the CSforAll summit.

The National Science Foundation (NSF) has awarded the UT STEM Center a three-year $2.5 million grant to lead the Expanding Computing Education Pathways (ECEP) Alliance. ECEP is one of eight Broadening Participation in Computing Alliances (BPC) funded by the NSF to increase the number and diversity of students in K-16 pathways. ECEP works with state leadership teams to achieve this goal through education policy reform. First launched in 2012 through an NSF grant to Georgia Tech and the University of Massachusetts Amherst, ECEP has since grown through four phases from two states to sixteen and Puerto Rico. Building on the existing network of ECEP states noted in the map above, the ECEP leadership team is pleased to announce the fifth phase addition of six new states to the Alliance: Hawaii, Minnesota, Mississippi, Ohio, Oregon, and Washington.

Source: National Alliance for Expanding Computing Education Pathways has a new home at The University of Texas at Austin

October 8, 2018 at 7:00 am Leave a comment

South Carolina requires CS to fulfill high school requirement, and Keyboarding is no longer CS

Pat Yongpradit of Code.org shared some great news with me.  Well, it’s not really “new” — it happened back in March 2018. But it was something that both of us worked on, and it was great to finally see it happen.

South Carolina was one of the first ECEP (Expanding Computing Education Pathways) Alliance states. They had one of the first statewide summits on computing education (see blog post here). They were one of the first states to require computer science for all high school students.

The problem was that they didn’t actually require computer science. They allowed some 90 classes to count as CS, and only six actually contained CS content (like programming or algorithms). Even a course on “keyboarding” counted as “CS” under the South Carolina system. South Carolina resisted changing this requirement, as Tony Dillon of the state Department of Education argued (see this blog post). I’ve worried that other states that mandate CS would fall into a similar trap (see blog post here on that).

That changed March 28, 2018 with this memo. South Carolina has computer science standards. Keyboarding no longer counts.

It’s an interesting question how this happened.  I know that Pat and others at Code.org have been working a lot in South Carolina.  I know that our South Carolina ECEP collaborators, like Eileen Kraemer, Tiffany Barnes, and Mary Lou Maher, have been working tirelessly on the state. I also know that my involvement from Georgia had limited success.  As one Department of Education official said when I was working in Columbia, “No professor from Georgia Tech is going to tell me about AP CS.”

My suspicion is that this happened because there was significant internal and external pressure.  South Carolina wasn’t going to do much when it was just external pressure. But when it was both, there were changes made.

Pat has promised me that Code.org is going to be helping South Carolina fulfill their plans for new CS requirements.

 

September 10, 2018 at 7:00 am Leave a comment

The Story of MACOS: How getting curriculum development wrong cost the nation, and how we should do it better

Man: A Course of Study (MACOS) is one of the most ambitious US curriculum efforts I’ve ever heard about. The goal was to teach anthropology to 10 year olds. The effort was led by world-renowned educational psychologist Jerome Bruner, and included many developers, anthropologists, and educational psychologists (including Howard Gardner). It won awards from the American Education Research Association and from other education professional organization for its innovation and connection to research. At its height, MACOS was in thousands of schools, including whole school districts.

Today, MACOS isn’t taught anywhere. Funding for MACOS was debated in Congress in 1975, and the controversy led eventually to the de-funding of science education nationally.

Peter Dow’s 1991 book Schoolhouse Politics: Lessons from the Sputnik Era is a terrific book which should be required reading for everyone involved in computing education in K-12. Dow was the project manager for MACOS, and he’s candid in describing what they got wrong. It’s worthwhile understanding what happened so that we might avoid it in computing education. I just finished reading it, and here are some of the parts that I found particularly insightful.

First, Dow doesn’t dismiss the critics of MACOS. Rather, he recognizes that the tension is between learning objectives. What do we want for our children? What kind of society do we want to build?

I quickly learned that decisions about educational reform are driven far more by political considerations, such as the prevailing public mood, than they are by a systematic effort to improve instruction. Just as Soviet science supremacy had spawned a decade of curriculum reform led by some of our most creative research scientists during the late 1950s and 1960s, so now a new wave of political conservatism and religious fundamentalism in the early 1970s began to call into question the intrusion of university academics into the schools…Exposure to this debate caused me to recast the account to give more attention to educational politics. No discussion of school reform, it seems, can be separated from our vision of the society that the schools serve.

MACOS was based in the best of educational psychology at the time. Students engaged in inquiry with first-hand accounts, e.g., videos of Eskimos. The big mistake the developers made was they gave almost no thought to how it was going to get disseminated. Dow points out that MACOS was academic researchers intruding into K-12, without really understanding K-12. They didn’t plan for teacher professional development, and worse, didn’t build any mechanism for teachers to tell them how the materials should be changed to work in real classrooms. They were openly dismissive of the publishers who might get the materials into the world.

On teachers: There was ambivalence about teachers at ESI. On the one hand the Social Studies Program viewed its work as a panacea for teachers, a liberation from the drudgery of textbook materials and didactic lessons. On the other, professional educators were seen as dull-witted people who conversed in an incomprehensible “middle language” and were responsible for the uninspired state of American education.

On publishers: These two experienced and widely respected publishing executives listened politely while Bruner described our lofty education aspirations with characteristic eloquence, but the discussion soon turned to practical matters such as the procedures of state adoption committees, “tumbling test” requirements, per-pupil expenditures, readability formulas, and other restrictions that govern the basal textbook market. Spaulding and Kaplan tried valiantly to instruct us about the realities of the educational publishing world, but we dismissed their remarks as the musings of men who had been corrupted by commercialism. Did they not understand that our mission was to change education, not submit to the strictures that had made much of instruction so meaningless? Could not men so powerful in the publishing world commit some of their resources to support curriculum innovation? Had they no appreciation of the intellectual poverty of most social studies classrooms? I remember leaving that room depressed by the monumental conservatism of our visitors and more determined than ever to prove that there were ways to reach the schools with good materials. Our arrogance and naivete were not so easily cured.

By 1971, Dow realizes that the controversies around MACOS could easily have been avoided. They had made choices in their materials that highlighted the challenges of Eskimo life graphically, but the gory details weren’t really necessary to the learning objectives. They simply hadn’t thought enough about their users, which included the teachers, administrators, parents, and state education departments.

My favorite scene in the book is with Margaret Mead who tries to help Dow defend MACOS in Congress, but she’s frustrated by their arrogance and naivete.

Mead’s exasperation grew. “What do you tell the children that for?…I have been teaching anthropology for forty years,” she remarked, “and I have never had a controversy like this over what I have written.”

But Mead’s anger quickly returned. “No, no, you can’t tell the senators that! Don’t preach to them! You and I may believe that sort of thing, but that’s not what you say to these men. The trouble with you Cambridge intellectuals is that you have no political sense!”

Dow describes over two chapters the controversies around MACOS and the aftermath impacts on science education funding at NSF. But he also points out the problems with MACOS as a curriculum. Some of these are likely problems we’re facing in CS for All efforts.

For example, he talks about why MACOS was removed from Oregon schools, using the work of Lynda Falkenstein. (Read the below with an awareness of the Google-Gallup and EdWeek polls showing that administrators and principals are not supportive of CS in schools.)

She concluded that innovations that lacked the commitment of administrators able to provide long-term support and continuing teacher training beyond the initial implementation phase were bound to faster regardless of their quality. Even more than controversy, she found, the greatest barrier to successful innovation was the lack of continuity of support from the internal structure of the school system itself.

I highly recommend Schoolhouse Politics. It has me thinking about what it really takes to get any education reform to work and to scale. The book is light on evaluation evidence that MACOS worked. For example, I’m concerned that MACOS was so demanding that it may have been too much for underprepared students or teachers. I am totally convinced that it was innovative and brilliant. One of the best curriculum design efforts I’ve ever read about, in terms of building on theory and innovative design. I am also totally convinced that it wasn’t ready to scale — and the cost of that mistake was enormous. We need to avoid making those mistakes again.

June 18, 2018 at 7:00 am 6 comments

Are you talking to me? Interaction between teachers and researchers around evidence, truth, theory, and decision-making

In this blog, I’m talking about computing education research, but I’m not always sure and certainly not always clear about who I’m talking to. That’s a problem, but it’s not just my problem. It’s a general problem of research, and a particular problem of education research. What should we say when we’re talking to researchers, and what should we say when we’re talking to teachers, and where do we need to insert caveats or explain assumptions that may not be obvious to each audience?

From what I know of philosophy of science, I’m a post-positivist. I believe that there is an objective reality, and the best tools that we humans have to understand it are empirical evidence and the scientific method. Observations and experiments have errors and flaws, and our perspectives are biased. All theory should be questioned and may be revised. But that’s not how everyone sees the world, and what I might say in my blog may be perceived as a statement of truth, when the strongest statement I might make is a statement of evidence-supported theory.

It’s hard to bridge the gap between researchers and education. Lauren Margulieux shared on Twitter a recent Educational Researcher article that addresses the issue. It’s not about getting teachers access to journal articles, because those articles aren’t written to speak to nor address teachers’ concerns. There have to be efforts from both directions, to help teachers to grok researchers and researchers to speak to teachers.

I have three examples to concretize the problem.

Recursion and Iteration

I wrote a blog post earlier this month where I stated that iteration should be taught before recursion if one is trying to teach both. For me, this is a well-supported statement of theory. I have written about the work by Anderson and Wiedenbeck supporting this argument. I have also written about the terrific work by Pirolli exploring different ways to teach recursion, which fed into the work by Anderson.

In the discussion on the earlier post, Shriram correctly pointed out that there are more modern ways to teach recursion, which might make it better to teach before iteration. Other respondents to that post point out the newer forms of iteration which are much simpler. Anderson and Wiedenbeck’s work was in the 1980’s. That sounds great — I would hope that we can do better than what we did 30 years ago. I do not know of studies that show that the new ways work better or differently than the ways of the 1980’s, and I would love to see them.

By default, I do not assume that more modern ways are necessarily better. Lots of scientists do explore new directions that turn out to be cul-de-sacs in light of later evidence (e.g., there was a lot of research in learning styles before the weight of evidence suggested that they didn’t exist). I certainly hope and believe that we are coming up with better ways to teach and better theories to explain what’s going on. I have every reason to expect that the modern ways of teaching recursion are better, and that the FOR EACH loop in Python and Java works differently than the iteration forms that Anderson and Wiedenbeck studied.

The problem for me is how to talk about it.  I wrote that earlier blog post thinking about teachers.  If I’m talking to teachers, should I put in all these caveats and talk about the possibilities that haven’t yet been tested with evidence? Teachers aren’t researchers. In order to do their jobs, they don’t need to know the research methods and the probabilistic state of the evidence base. They want to know the best practices as supported by the evidence and theory. The best evidence-based recommendation I know is to teach iteration before recursion.

But had I thought about the fact that other researchers would be reading the blog, I would have inserted some caveats.  I mean to always be implicitly saying to the researchers, “I’m open to being proven wrong about this,” but maybe I need to be more explicit about making statements about falsifiability. Certainly, my statement would have been a bit less forceful about iteration before recursion if I’d thought about a broader audience.

Making Predictions before Live Coding

I’m not consistent about how much evidence I require before I make a recommendation. For a while now, I have been using predictions before live coding demonstrations in my classes. It’s based on some strong evidence from Eric Mazur that I wrote about in 2011 (see blog post here). I recommend the practice often in my keynotes (see the video of me talking about predictions at EPFL from March 2018).

I really don’t have strong evidence that this practice works in CS classes. It should be a pretty simple experiment to test the theory that predictions before seeing program execution demonstrations helps with learning.

  • Have a set of programs that you want students to learn from.
  • The control group sees the program, then sees the execution.
  • The experimental group sees the program, writes down a prediction about what the execution will be, then sees the execution.
  • Afterwards, ask both groups about the programs and their execution.

I don’t know that anybody has done this experiment. We know that predictions work well in physics education, but we know that lots of things from physics education do not work in CS education. (See Briana Morrison’s dissertation.)

Teachers have to do lots of things for which we have no evidence. We don’t have enough research in CS Ed to guide all of our teaching practice. Robert Glaser once defined education as “Psychology Engineering,” and like all engineers, teachers have to do things for which we don’t have enough science. We make our best guess and take action.

So, I’m recommending a practice for which I don’t have evidence in CS education. Sometimes when I give the talk on prediction, I point out that we don’t have evidence from CS. But not always. I probably should. Maybe it’s enough that we have good evidence from physics, and I don’t have to get into the subtle differences between PER and CER for teachers. Researchers should know that this is yet another example of a great question to be addressed. But there are too few Computing Education Researchers, and none that I know are bored and looking for new experiments to run.

Code.org and UTeach CSP

Another example of the complexity of talking to teachers about research is reflected in a series of blog posts (and other social media) that came out at the end of last year about the AP CS Principles results.

  • UTeach wrote a blog post in September about the excellent results that their students had on the AP CSP exam (see post here). They pointed out that their pass rate (83%) was much higher than the national average of 74%, and that advantage in pass rates was still there when the data were disaggregated by gender or ethnicity.
  • There followed a lot of discussion (in blog posts, on Facebook, and via email) about what those results said about the UTeach curriculum. Should schools adopt the UTeach CSP curriculum based on these results?
  • Hadi Partovi of Code.org responded with a blog post in October (see post here). He argued that exam scores were not a good basis for making curriculum decisions. Code.org’s pass rates were lower than UTeach’s (see their blog post on their scores), and that could likely be explained by Code.org’s focus on under-represented and low-SES student groups who might not perform as well on the AP CSP for a variety of reasons.
  • Michael Marder of UTeach responded with two blog posts. One conducted an analysis suggesting that UTeach’s teacher professional development, support, and curriculum explained their difference from the national average (see post here), i.e., it wasn’t due to what students were served by UTeach. A second post tried to respond to Hadi directly to show that UTeach did particularly well with underrepresented groups (see post here).

I don’t see that anybody’s wrong here. We should be concerned that teachers and other education decision-makers may misinterpret the research results to say more than they do.

  • The first result from UTeach says “UTeach’s CSP is very good.” More colloquially, UTeach doesn’t suck. There is snake oil out there. There are teaching methods that don’t actually work well for anyone (e.g., we could talk some more about learning styles) or only work for the most privileged students (e.g., lectures without active learning supports). How do you show that your curriculum (and PD and support) is providing value, across students in different demographic groups? Comparing to the national average (and disaggregated averages) is a reasonable way to do it.
  • There are no results saying that UTeach is better than Code.org for anyone, or vice-versa. I know of no studies comparing any of the CSP curricula. I know of no data that would allow us to make these comparisons. They’re hard to do in a way that’s convincing. You’d want to have a bunch of CSP students and randomly assign them to either UTeach and Code.org, trying to make sure that all relevant variables (like percent of women and underrepresented groups) is the same in each. There are likely not enough students taking CSP yet to be able to do these studies.
  • Code.org likely did well for their underrepresented students, and so did UTeach. It’s impossible to tell which did better. Marder is arguing that UTeach did well with underrepresented groups, and UTeach’s success was due to their interventions, not due to the students who took the test.  I believe that UTeach did well with underrepresented groups. Marder is using statistics on the existing data collected about their participants to make the argument about the intervention. He didn’t run any experiments. I don’t doubt his stats, but I’m not compelled either. In general, though, I’m not worried about that level of detail in the argument.

All of that said, teachers, principals, and school administrators have to make decisions. They’re engineers in the field. They don’t have enough science. They may use data like pass rates to make choices about which curricula to use. From my perspective, without a horse in the race or a dog in the fight, it’s not something I’m worried about. I’m much more concerned about the decision whether to offer CSP at all. I want schools to offer CS, and I want them to offer high-quality CS. Both UTeach and Code.org offer high-quality CS, so that choice isn’t really a problem. I worry about schools that choose to offer no CSP or no CS at all.

Researchers and teachers are solving different problems. There should be better communication. Researchers have to make explicit the things that teachers might be confused about, but they might not realize what the teachers are confused about. In computing education research and other interdisciplinary fields, researchers may have to explain to each other what assumptions they’re making, because their assumptions are different in different fields. Teachers may use research to make decisions because they have to make decisions. It’s better for them to use evidence than not to use evidence, but there’s a danger in using evidence to make invalid arguments — to say that the evidence implies more than it does.

I don’t have a solution to offer here. I can point out the problem and use my blog to explore the boundary.

June 15, 2018 at 1:00 am 5 comments

Some principals are getting interested in CS, but think pressure for CS is mostly coming from Tech companies

How do high school principals in small, medium and large districts view the Computer Science for All movement?

 

High school leaders in smaller districts are most enthusiastic about the trend, a new survey by the Education Week Research Center found. Overall, 30% of all principals say CS is not “on their radar,” and 32% say CS is an “occasional supplement or enrichment opportunity.”  I found the two graphs above interesting.  The majority of principals aren’t particularly excited by CS, and most principals think that it’s the Tech firms that are pushing CS onto schools, not parents.

Source: Principals Warm Up to Computer Science, Despite Obstacles

May 28, 2018 at 7:00 am 3 comments

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