Posts tagged ‘computing for everyone’

ICER 2021 Preview: The Challenges of Validated Assessments, Developing Rich Conceptualizations, and Understanding Interest #icer2021

The International Computing Education Research Conference (ICER) 2021 is this week (website here). It should have been in Charleston, South Carolina (one of my favorite cities), but it will instead be all on-line. Unlike previous years, if you are not already registered, you’re unfortunately out of luck. As seen in Matthias Hauswirth’s terrific guest blog post from last week (see here), getting set up in Clowdr is complicated. ICER won’t have the resources to bring people on-line and get them through the half hour prep sessions on-the-fly. There will be no “onsite” registration.

However, all the papers should be available in the ACM Digital Library (free for some time), and I think all the videos of the talks will be made available after the fact, so you can still gain a lot from the conference. Let me point out a few of the highlights that I’m excited about. (As of this writing, the papers are not yet appearing in the ACM DL — all the DOI links are failing for me. I’ll include the links here in hopes that everything is fixed soon.)

Our keynoter is Tammy Clegg, whom I got to know when she was a PhD student at Georgia Tech. She’s now at U. Maryland doing amazing work around computation and relevant science learning. I’m so looking forward to hearing what she has to say to the ICER community.

Miranda Parker, Allison Elliott Tew, and I have a paper “Uses, Revisions, and the Future of Validated Assessments in Computing Education: A Case Study of the FCS1 and SCS1.” This is a paper that we planned to write when Miranda first developed the SCS1 (first published in 2016). We created the SCS1 in order to send it out to the world for use in research. We hoped that we could sometime later do in CS what Richard Hake did in Physics, when he used the FCI to make some strong statements about teaching practices with a pool of 6,000 students (see paper here). Hake’s paper had a huge impact, as it started making the case to shift from lecture to active learning. Could we use the collected use of the SCS1 to make some strong arguments for improving CS learning? We decided that we couldn’t. The FCI was used in pretty comparable situations, and it’s tightly focused on force. CS1 is far too broad, and FCS1 and SCS1 are being used in so many different places — not all of which it’s been validated for. Our retrospective paper is kind of a systemic literature review, but it’s done from the perspective of tracing these two instruments and how they’ve been used by the research community.

One of the papers that I got a sneak peek at was “When Wrong is Right: The Instructional Power of Multiple Conceptions” by Lauren Margulieux, Paul Denny, Katie Cunningham, Mike Deutsch, and Ben Shapiro. The paper is exploring the tensions between direct instruction and more student-directed approaches (like constructionism and inquiry learning) (see a piece I did in 2015 about these tensions). The basic argument of this new paper is that just telling students the right answer is not enough to develop rich understanding. We have to figure out how to help students to be able to hold and compare multiple conceptions (not all of which is canonical or held by experts), so that they can compare and contrast, and use the right one at the right time.

I’m chair for a session on interest. While I haven’t seen the papers yet, I got to watch the presentations (which are already loaded in Clowdr). “Children’s Implicit and Explicit Stereotypes on the Gender, Social Skills, and Interests of a Computer Scientist” by de Wit, Hermans, and Aivaloglou is a report on a really interesting experiment. They look at how kids associate gender with activities (e.g., are boys more connected to video games than girls?). The innovative part is that they asked the questions and timed the answers. A quick answer likely connects to implicit beliefs. If they take a long time to answer, maybe they told you what they thought you wanted to hear? The second paper “All the Pieces Matter: The Relationship of Momentary Self-efficacy and Affective Experiences with CS1 Achievement and Interest in Computing” by Lishinski and Rosenberg asks about what leads to students succeeding and wanting to continue in computing. They look at students affective state coming into CS1 (e..g, how much do they like computing? How much do they think that they can succeed in computing?), and relate that to students’ experiences and affective state after the class. They make some interesting claims about gender — that gender gaps are really self-efficacy gaps.

One of the more unusual sessions is a pair of papers from IT University of Copenhagen that make up a whole session. ICER doesn’t often give over a whole session to a single research group on multiple papers. One is “Computing Educational Activities Involving People Rather Than Things Appeal More to Women (Recruitment Perspective)” and the other is “Computing Educational Activities Involving People Rather Than Things Appeal More to Women (CS1 Appeal Perspective).” The pitch is that framing CS1 as being about people rather than things leads to better recruitment (first paper) and more success in CS1 (second paper) in terms of gender diversity. It’s empirical support for a hypothesis that we’ve heard before, and the authors frame the direction succinctly: “CS is about people not things.” Is that succinct enough to get CS faculty to adopt this and teach CS differently?

August 16, 2021 at 7:00 am Leave a comment

Social Studies Teachers using Programming for Data Visualization: An FIE 2020 Paper Preview

The Frontiers in Education (FIE) 2020 conference starts Wednesday October 21 in Uppsala, Sweden — see program here. My student Bahare Naimipour will be presenting our paper “Engaging Pre-Service Teachers in Front-End Design: Developing Technology for a Social Studies Classroom” (see preprint here) by Bahare, me, and Tammy Shreiner. This work came long before the NSF work that we just got funded for (see blog post here), but it’s in the same line of research.

The paper is about two of our participatory design sessions with pre-service social studies teachers in Tammy’s class on data literacy. In both of these sessions, we asked teachers to program in JavaScript or Vega-Lite to build a visualization, and in the second one, we also introduce CODAP, a visualization tool explicitly designed for middle and high school students. The paper is less about the technology and more about what the teachers told us about what they thought about tools for visualization in their class.

Social studies teachers are such an interesting group to study. They’re not particularly interested in STEM, data, or computers. They want to teach social studies. Very few of our participants had ever seen any code. (One told us, “This looks a lot like setting up my MySpace page in middle school!”)They’re only interested if we can help them teach what they want to teach. It’s a hard audience to engage, in all the right ways.

I’m going to highlight just two lessons we learned here:

First: The results from the two participatory design sessions were remarkably different. Participatory design isn’t a “okay, we did that — check off the box” methodology. Each group of participants can be remarkably different. There’s no generalization here. Each session is useful, but I don’t know how many sessions we’d have to do to get anywhere near saturation. That’s okay — we learned design lessons from each session.

Second: There is no one answer to how teachers think about programming. I have heard from many people that teachers find programming hard (see this CACM Blog post about that discussion), and I’ve hypothesized that to be true in this blog (see this post). So, now I’ve been in the room as social studies teachers have their first programming experiences and interviewing them afterwards, and….it’s complicated.

Teachers tell us often in our sessions that programming is overwhelming, but several teachers also told us that CODAP (explicitly designed for their use, and not a programming tool) was overwhelming. The question is whether it’s worth the complexity — and for whom. We get contradictory responses from the teachers, which we report in this paper. One told us that she wanted a simpler tool for herself and JavaScript for her students: “I don’t mind keeping life simple for me, but I wanted to challenge my students and give them useful, new skills.” Another teacher told us the opposite: “I would like Java[script] because it would let me do more to the visualization. Vega-lite would be better for students because it seems far more simple.”

We couldn’t fit in all the great stories and insights from these two participatory design sessions. Like the teacher who wants JavaScript in her class because, “That’s similar to what they use in math and science, right? I don’t want history to be the ‘dumbed-down’ programming.” I found that surprising, and wondered what the teachers would think of a block-based language. Another teacher told us that she wants to use programming in her history class, “Because maybe that would make history ‘cool.’” One of the tensions I found most interesting in these sessions was between the desire to know the tools and be comfortable in front of the class, and the desire to push their students to learn more. Some teachers told us that they preferred CODAP to any programming tool because they would be embarrassed to get a syntax error in front of their kids, which they realized would always be possible when programming. Other teachers told us that they were more concerned with going beyond basic tools — (paraphrasing one comment we received), “My students will already know Excel and Google Sheets. I want them to do more in my class.”

Our work is ramping up now. We had another PD session with pre-service teachers in March, just before pandemic lockdown, which was our first one with our data visualization tool in the mix. We’ve just held our first workshop in August for in-service (practicing) teachers. We’ve got more workshops planned over the next year. You’ll likely be hearing more from these studies in future posts.

October 19, 2020 at 7:00 am 4 comments

Proposal #1 to Change CS Education to Reduce Inequity: Teach computer science to advantage the students with less computing background

This is my second post in a series about how we have to change how we teach CS education to reduce inequity. I started this series with this post, making an argument based on race, but might also be made in terms of the pandemic. We have to change how we teach CS this year.

The series has several inspirations, but the concrete one that I want to reference back to each week is the statement from the University of Maryland’s CS department:

Creating a task force within the Education Committee for a full review of the computer science curriculum to ensure that classes are structured such that students starting out with less computing background can succeed, as well as reorienting the department teaching culture towards a growth mindset

We as individual computing teachers make choices that influence whether students with less computing background can succeed. I often see choices being made that encourage the most capable students, but at the cost of the least prepared students. Part of this is because we see ourselves as preparing students for top software engineering jobs. The questions that get asked on technical interviews explicitly drive how many CS departments teach algorithms and theory. We want to encourage “excellence.” But whose excellence do we care about? Is Silicon Valley entrepreneurial perspectives the only ones that matter? The goal of “becoming a great software engineer” does not consider alternative endpoints for computing education (see post here). Not all our students want those kinds of jobs. Many of our students are much more interested in giving back to their community, rather than take the Silicon Valley jobs that our programs aim for (see post here).

Please don’t teach students as if they are you. First, you (as a CS teacher, as someone who reads this blog) are wildly different than our normal student. Second, your memories of how you learned and what worked for you are likely wrong. Humans are terrible at reconstructing how their memory was at a prior time and what led to their learning. That’s why we need research.

In this post, I will identify four of the methods that are differential, that advantage the students with less computing background — there are many more:

  • Use Peer Instruction
  • Explain connection to community values
  • Use Parsons Problems
  • Use subgoal labeling

Use Peer Instruction

When I talk to computer science teachers about peer instruction and how powerful it is for learning, the most common response is, “Oh, we already do that.” When I press them, they tell me that they “have class discussions” or “use undergraduate teaching assistants.” Nope, that’s not peer instruction.

Peer instruction (PI) is a technical term meaning a very specific protocol. Digital Promise and UTeach are creating a set of CS teaching micro credentials, and the one that they have on PI defines it well (see link here). PI is where the teacher poses a question for the class for individual responses, students discuss their answers, students respond again, and the teacher reveals the answer and explains the answer. The evidence suggesting that PI really works is overwhelming, and it can be used in any CS class — see http://peerinstruction4cs.com/ for more information on how to do it. I use it regularly in Senior-level undergraduate courses and graduate courses. There are ways to do PI when teaching remotely, as I talked about in this post.

I’m highlighting PI because the evidence suggests that it has a differential impact (see study here). It doesn’t hurt the top students, but it reduces failure rate (measured in multiple CS courses) for students with less background (see paper here). That’s exactly what we’re looking for in this series — how do we improve the odds of success for students who are not in the most privileged groups.

Explain connection to community values

I blogged last year about a paper (see post here) that showed female, Black, Latino/Latina, and first-generation students take CS because they want to help society. These students often do not see a connection between what’s being taught in CS classes and what they want. That’s because we often teach to prepare students for top software engineering jobs — it’s a mismatch between our goals and their goals.

I don’t know if this is an issue in upper-level classes. Maybe students in upper-level classes have already figured out how CS connects to their goals and values. Or maybe we have already filtered out the CS students who care about community values by the upper-level and graduate courses.

CS can certainly be used to advance social goals and community values. Teach that. In every CS class, for everything you teach, explain concretely how this concept or skill could be used to advance social good, cultural relevance, and community values. If you can’t, ask yourself why you’re teaching this concept or skill. If it’s just to promote a Silicon Valley jobs program, consider dropping it. We are all revising our classes this summer for fall. It’s a good time to do this review and update.

Use Parsons Problems

Parsons problems (sometimes referred to as “mixed-up code problems”) are where students are given a programming problem, and given all the lines of code to solve the problem, but the lines are scrambled (I usually say “on refrigerator magnets”). The challenge is to assemble the correct program. My wife, Barbara Ericson, did her dissertation work (see post here) showing that Parsons problems were effective (led to the same learning as writing the programs from scratch or from debugging programs) and efficient (low time cost, low cognitive load). She also invented dynamically adaptive Parsons problems which are even better (for effectiveness and efficiency) than traditional Parsons problems.

Parsons problems work on-line, so they fit into remote teaching easily. I’ve been doing paper-based (and Canvas-based) Parsons for exams and quizzes for several years now (see post here). Parsons problems work great in lower-level classes. There is relatively little research on using them in upper-level and graduate courses — I suspect that they could be useful, if only to break up the all-coding-all-the-time framing of CS classes.

I’m highlighting Parsons problems for two reasons.

  • First, they’re efficient. As Manuel noted (as I quoted in my Blog@CACM post), BIPOC students are much more likely to be time-stressed than more privileged students. I’m reading Grading for Equity by Joe Feldman which makes this point in more detail (see website). Our less-privileged students need us to find ways to teach them efficiently. This is going to be a particularly concern during a pandemic when students will have more time constraints, especially if they, or a relative, or someone they live becomes ill.
  • Second, they are a more careful and finer-grained assessment tool (see this post). If you ask students with less ability to write a piece of code, you might get students who only get part of the code working, but you get little data from students who only knew how to write part of the code but get none of it working. Parsons problems help the students with less computing background to show what they do know, and to help the teacher figure out what they don’t know how to write yet.

Use subgoal labelling

Subgoal labelling is pretty amazing (see Wikipedia page). Even our first experiment with subgoal labelling for CS worked examples (see post here) has shown improvements in learning (measured immediately after instruction), retention (measured a week later), and transfer (student success on a new task without instruction). Since then, Lauren Margulieux, Briana Morrison, and Adrienne Decker have published a slew of great results.

The one that makes it on this list is their most recent finding (see post here). Subgoal labeling in an introductory computing course, compared to one not using subgoal labeling, led to reduced drop or failure rates. That’s a differential benefit. There was not a statistically significant improvement on learning (measured in terms of exam scores), but it kept the students most at risk of failing or dropping out in the course. That’s teaching to advantage the students with less background in computing. We don’t know if it works for upper-level or graduate classes — my hypothesis is that it would.

July 20, 2020 at 7:00 am 5 comments

Data science as a path to integrate computing into K-12 schools and achieve CS for All

My colleague Betsy DiSalvo is part of the team that just released Beats Empire, an educational game for assessing what students understand about middle school computer science and data science https://info.beatsempire.org The game was designed by researchers from Teachers College, Columbia University; Georgia Tech; University of Wisconsin, Madison; SRI International; Digital Promise; and Filament Games in concert with the NYC Dept. of Education. Beats Empire is totally free; it has already won game design awards, and it is currently in use by thousands of students. Jeremy Roschelle was a consultant on the game and he just wrote a CACM Blog post about the reasoning behind the game (see link here).

Beats Empire is an example of an important development in the effort to help more students get the opportunity to participate in computing education. Few students are taking CS classes, even when they’re offered — less than 5% in every state for whom I’ve seen data (see blog post here). If we want students to see and use computing, we’ll need to put them in other classes. Data science fits in well with other classes, especially social studies classes. Bootstrap: Data Science (see link here) is another example of a computing-rich data science curriculum that could fit into a social studies class.

Social studies is where we can reach the more diverse student populations who are not in our CS classes. I’ve written here about my work developing data visualization tools for history classes. For a recent NSF proposal, I looked up the exam participation in the two Advanced Placement exams in computer science (CS Principles and CS A) vs the two AP exams in history (US history and World history). AP CS Principles was 32% female, and AP CS A was 24% female in 2019. In contrast, AP US History was 55% female and AP World History was 56% female. Five times as many Black students took the AP US History exam as took the AP CS Principles exam. Fourteen times as many Hispanic students took the AP US History exam as took the AP CS Principles exam.

Data science may be key to providing CS for All in schools.

April 27, 2020 at 7:00 am Leave a comment

When computer science has to be a requirement if we want it to be available to everyone

Robert Sedgewick had an essay published at Inside Higher Ed last month on Should Computer Science Be Required? (see link here). He has some excellent reasons for why students should study computer science. Several of them overlap with reasons I’ve suggested (see blog post here). He writes:

Programming is an intellectually satisfying experience, and certainly useful, but computer science is about much more than just programming. The understanding of what we can and cannot do with computation is arguably the most important intellectual achievement of the past century, and it has led directly to the development of the computational infrastructure that surrounds us. The theory and the practice are interrelated in fascinating ways. Whether one thinks that the purpose of a college education is to prepare students for the workplace or to develop foundational knowledge with lifetime benefits (or both), computer science, in the 21st century, is fundamental.

So, we both agree that we want all students in higher education to take a course in computer science — but he doesn’t want that course to be a requirement. He explains:

When starting out at Princeton, I thought about lobbying for a computer science requirement and asked one of my senior colleagues in the physics department how we might encourage students to take a course. His response was this: “If you do a good course, they will come.” This wisdom applies in spades today. A well-designed computer science course can attract the vast majority of students at any college or university nowadays — in fact, there’s no need for a requirement.

Colleges and universities offer the opportunity for any student to take as many courses as they desire in math, history, English, psychology and almost any other discipline, taught by faculty members in that discipline. Students should have the same opportunity with computer science.

I have heard this argument before. Colleagues at Stanford have pointed out that most of Stanford undergraduates take their courses, without a requirement. Many colleagues have told me that a requirement would stifle motivation and would make students feel that we were forcing computer science down their throats. They would rather “attract” students (Sedgewick’s word above). I recognize that most students at the University of Michigan where I now work have the kind of freedom and opportunity that Sedgewick describes.

But I know many institutions and situations where Sedgewick’s description is simply not true. CS teachers can try attracting students, but they are only going to get them if it’s a required course.

  • In most Engineering programs with which I am familiar, students have relatively few electives. In most of the years that I was at Georgia Tech, Nuclear Engineering students had no elective hours — if they took even a single course out of the planned, lockstep four years, the degree would take them longer (and cost them more). Mechanical Engineering students had exactly one three credit hour course that they could choose over a four year program. These are programs where students are not allowed to take any course that they desire. Those two degree programs are extreme situations (but still exist at many institutions). Even here in Computer Science & Engineering at the University of Michigan, students are limited in the number of elective hours that they can take outside of CS and engineering classes. For many technical degrees, if you want students to take a particular subject, you have to convince the faculty who own that degree to include computer science in those requirements. At Georgia Tech, we had to sell a computer science requirement to the rest of campus. I tell that story here.
  • If students are paying by the course, then they take the courses that they need, not the ones that they might desire. I worked with Chattahoochee Technical College when I was in Georgia. They struggled to get students to even finish the requirements for a certificate. Students would take the small number of courses that helped them to gain the job skills that they needed. Completing courses that were merely interesting or recommended to them was simply an unnecessary expense. There are millions of students in US community colleges. If you really think that everyone should learn computer science, you have to think about reaching those millions of students, too.
  • Finally, there are the students who might end up loving computer science but, right now, they don’t desire it. First, they may simply not know what computer science is. In California (as an example), only 39% of high schools offer computer science, and only 3% of all California high school students take it (see link here). Alternatively, they may know what computer science is, but have already decided that it’s not for them. In my group’s research, we use expectancy-value theory often to explain student behavior — if students don’t think that they’ll succeed in computer science, or don’t see people like them belonging in computer science, then they won’t take it. That’s one reason for mandatory computer science in high schools — to give everyone the chance to discover a love in CS. So far, it’s not working in US high schools. I have argued that (see article here) we don’t know how to ramp up to that at the high school level yet. But we certainly could at the higher education level.

I understand Sedgewick’s argument for why he wants to offer the most compelling computer science course he can at Princeton, in order to attract students and motivate them to learn about computing. (I don’t agree with him that curated online videos are better than live lectures, but I think we mean something different by “lectures.”) I also understand why making that course a requirement might undermine his efforts to motivate and engage students. But that’s only a small percentage of students at the Princeton-like institutions in the US, and elsewhere. I’m sure he would agree that everyone deserves the opportunity to learn about computer science. His reasons why CS is important for Princeton students are valid for everyone. We are going to need different strategies to reach everyone. For some students, a requirement is the only way that we are going to make it available to them.

December 2, 2019 at 7:00 am 16 comments

Our work at SIGCSE 2019: Ebooks, jobs, privilege, what is research, and what is literacy

There was a time before each SIGCSE Symposium or ICER Conference that I would write a blog post about all the cool things being presented from Georgia Tech (see for example SIGCSE 2018 and SIGCSE 2017 posts).  But now, the list of things at SIGCSE 2019 from Georgia Tech and University of Michigan (my new home) is enormous.

I’m going to take a different strategy — the selfish and easier strategy.  Let me just tell you about the things that I know about because I work with the authors. All the proceedings are available here.

  • Wednesday is the RESPECT conference (see program here). At the 9:30 session, Katie Cunningham will be one of the presenters of a paper “Job Placement Experience and Perceptions of Alumni from a Three-year Computer Science Program” with Miguel Lara and Bude Su about the job placement experience of students who graduate from the CSin3 program that won a best paper award at last year’s SIGCSE Symposium.
  • On Thursday afternoon, I’m going to be on a panel with Lauren Margulieux, Leo Porter, Greg Nelson, and our organizer and moderator, Colleen Lewis.  The topic of the panel is “Negotiating Varied Research Goals in Computing Education Research.”  Greg and Amy Ko published a paper at this last year’s ICER, which won one of the best paper awards, that I and others strongly disagreed with.  Amy’s blog post captures the discussion and arguments well. This panel continues that discussion.
  • Friday morning: I will be giving a keynote “Computing Education as a Foundation for 21st Century Literacy.”  I’ve practiced it every seminar and event I could find for the last couple months, so I’m ready.
  • At Friday’s 10 am poster session, ECEP will be presenting a poster (Jeff Xavier as lead) on “Fostering State-level Change In CS Education: The Expanding Computing Education Pathways Alliance.”
  • At a Friday morning 10:45 am special session, Miranda Parker will join Helen Hu, Jason Black, and Colleen Lewis to explore the role of privilege in CS education — an issue near and dear to Miranda’s heart (see, for example, this post).
  • At Friday afternoon’s 3 pm poster session, Katie Cunningham will have a poster with Miguel Lara and Bude Su echoing their RESPECT paper.
  • Friday afternoon at 3:45, Barb Ericson will be on a panel with Alison Derbenwick Miller, Lecia Barker, and Owen Astrachan on broadening participation in computing.  “You don’t have to be a white male that was learning how to program since he was five:” Computer Use and Interest From Childhood to a Computing Degree. I know that Barb is working hard on analyzing all the AP CS 2018 data, so this may be the unveiling of her annual analysis (see 2017 and 2016 posts here).
  • Saturday afternoon, Barb Ericson (with Brad Miller and Jackie Cohen of U-M) will present a workshop on how to use the Runestone ebook platform that we use in our work (see, for example, this post).

I highly encourage everyone to check out the SIGCSE 2019 Best Paper award-winning presentations (see list here).  I’m particularly excited that Andreas Stefik’s (with Richard Ladner, William Allee, and Sean Mealin) project on developing CS Principles materials for blind and visually-impaired students is being recognized. We need to take more seriously making computing accessible to a broader range of students.

 

February 25, 2019 at 7:00 am 2 comments

Do we want STEM education or do we want STEM learning?

I’ve mentioned a couple times that I’m working on using programming in teaching social sciences.  The goal is to teach STEM concepts (e.g., modeling, simulation, using graphical representations like charts, thinking about bias/skew and missing variables in big data, etc.), but in non-STEM subjects.  I argue that the “non-STEM subjects” part is key if you want diversity, if you want to draw in people who aren’t naturally going to show up in STEM classes.
I bounced this off an NSF program officer, and I got a pretty strong: “No.”  I’ll quote part of the response here.
While this is an intriguing idea, no, it would not be fundable in the XXX program as it does not involve the engagement of STEM faculty or their courses, assessments, or materials, or STEM majors.  (All of these are not necessary, but STEM is necessary, not just STEM learning.)
XXX is not just about improving or supporting STEM learning.  It is about improving STEM education.
There’s a distinction being drawn here between “STEM learning” and “STEM education.”  It’s an interesting and important distinction. I’m not at all saying that the officer is wrong.  This program officer is saying (paraphrasing), “It’s not just about learning STEM concepts. It’s about supporting the infrastructure and mechanisms through which we teach STEM.” (By the way, since this exchange, I’ve found other NSF officers in other programs that are more focused on STEM learning not just STEM education.)
That’s a fair concern. We do need STEM classes, curricula, assessments, and faculty. But if we really care about interdisciplinarity and broadening participation, we need to care about more than that.  We need to fund efforts to integrate STEM learning and use STEM thinking (e.g., Bacon’s Novum Organum) across the curriculum, to influence how we think about everything. We also need the infrastructure to support the institution of STEM education. The challenge is doing both.
There is an obvious connection to computing education.  We need more computer science teachers, curricula, tools, and classes. But we also need more students learning about computing, which might happen more inexpensively in mathematics, science, and social science classes. How do we prioritize?

December 21, 2018 at 3:39 pm 2 comments

What is programming-as-literacy, what does it look like, and what should we worry about? Alan Kay in Scientific American

Last month, I wrote a blog post about programming as a kind of literacy. I got some pushback.  Really? Literacy?  That programming in C stuff?  Well, no, programming in C is not what I mean by a form of literacy.  I recommended looking at some of what Alan Kay had written in Scientific American.

I decided to do that for myself.

Alan’s first article for Scientific American was in 1977, “Microelectronics and the Personal Computer,”  about the idea of a personal computer and the explorations they were doing at Xerox PARC with Smalltalk. I liked this one a lot because it emphasizes simulations “the central property of computing.”

The second was in 1984, “Computer Software.” Here’s where he defines literacy with the computer. It’s way more than just programming.

Alan_Kay_-_Computer_Software_SciAm_Sept_84

The third was in 1991, “Computers, Networks and Education.” This is the one where Alan really questioned whether things with computing were going in the right direction. For example, he worried about how people thought about “literacy” on the computer.

sci_amer_article-literacy-as-burden

He returned to the importance of simulation.

sci_amer_article-value-of-computing-is-simulation

And he was worried about people being critical of information that they find on the Internet (note that this is 1991, before Web browsers).

sci_amer_article-networked-computers

But in the end, Alan was hopeful, that we might develop a skeptical attitude with computing.

sci_amer_article-simulation

December 17, 2018 at 7:00 am 3 comments

Computational thinking abstracts too far from the computer: We should teach CS with inquiry

Judy Robertson has a blog post that I really enjoyed: What Children Want to Know About Computers. She argues that computational thinking has abstracted too far away from what students really want to know about, the machine.

Computational thinking has been a hugely successful idea and is now taught at school in many countries across the world. Although I welcome the positioning of computer science as a respectable, influential intellectual discipline, in my view computational thinking has abstracted us too far away from the heart of computation – the machine. The world would be a tedious place if we had to do all our computational thinking ourselves; that’s why we invented computers in the first place. Yet, the new school curricula across the world have lost focus on hardware and how code executes on it.

Her post includes pictures drawn by children about what they think is going on inside of the computer.  They’re interested in these things!  We should teach them about it.  One of the strongest findings in modern science education is that inquiry works. Students learn science well if it’s based in the things that they want to know. Judy argues that kids want to know about the computer and how code executes on the computer. We shouldn’t be abstracting away from that. We should be teaching what the kids most want to learn.

To be clear, I am not criticizing the children, who were curious, interested and made perfectly reasonable inferences based on the facts they picked up in their everyday lives. But I think that computer science educators can do better here. Our discipline is built upon the remarkable fact that we can write instructions in a representation which makes sense to humans and then automatically translate them into an equivalent representation which can be followed by a machine dumbly switching electrical pulses on and off. Children are not going to be able to figure that out for themselves by dissecting old computers or by making the Scratch cat dance. We need to get better at explicitly explaining this in interesting ways.

December 10, 2018 at 7:00 am 3 comments

MicroBlocks Joins Conservancy #CSEdWeek

This is great news for fans of GP and John Maloney’s many cool projects. MicroBlocks is a form of GP. This means that GP can be funded through contributions to the Conservancy.

We’re proud to announce that we’re bringing MicroBlocks into the Conservancy as our newest member project. MicroBlocks provides a quick way for new programmers to jump right in using “blocks” to make toys or tools. People have been proclaiming that IoT is the future for almost a decade, so we’re very pleased to be able to support a human-friendly project that makes it really easy to get started building embedded stuff. Curious? Check out a few of the neat things people have already built with MicroBlocks.

MicroBlocks is the next in a long line of open projects for beginners or “casual programmers” lead by John Maloney, one of the creators of Squeak (also a Conservancy project!) and a longtime Scratch contributor. MicroBlocks is a new programming language that runs right inside microcontroller boards such as the micro:bit, the NodeMCU and many Arduino boards. The versatility and interactivity of MicroBlocks helps users build their own custom tools for everything from wearables to model rockets or custom measuring devices and funky synthesizers.

Source: MicroBlocks Joins Conservancy

December 5, 2018 at 7:00 am Leave a comment

The systemic factors that limit Black participation in the Tech sector

I learned a lot from Kamau Bobb’s recent Atlantic article, “The Black Struggle for Technology Jobs.”  In it, he details the systemic factors that limit Black participation in the Tech sector.  He uses the possibility of Amazon’s HQ2 going to Atlanta as a framing.

After Atlanta made the shortlist of cities vying for Amazon’s second global headquarters, HQ2, it submitted a multibillion-dollar investment to try to seal the deal. (Other cities’ proposals were even bigger.) At stake is nothing less than the city’s economic future: HQ2 promises more than 50,000 high-tech jobs with an average salary of more than $100,000. With the tech industry looking like the future of all industry, Atlanta landing Amazon’s HQ2 would be a dream come true.

But a dream for whom? Highly educated people, particularly those with technical skills, are the ones who are really eligible for these prized jobs. People without that kind of education risk becoming even more marginalized in an increasingly tech-driven economy. In Atlanta, one of the most segregated cities in the United States, history has already largely determined who gets to benefit from the potential of Amazon.

In 2016, there was only one census tract in Atlanta where the population was more than 65 percent black, and where more than half the population age 25 or older had a bachelor’s degree or higher. In 2000, there were 10. Here, many black and brown students, and poor students of all backgrounds, receive a substandard education that does not prepare them for entry to the select colleges and universities tech companies draw their workforces from. Consequently, with or without Amazon’s investment, the city’s black population likely won’t land stem jobs unless they can gain access to the rigorous educational paths required to compete for them. In Atlanta and the many other American cities still scarred by decades of racist education policies, the future of work is still largely defined by a past from which their residents of color can’t seem to break free.

I’m biased in favor of this article because one of the students he interviews in this piece is my daughter, Katie. I learned from Katie’s comments, too.  I knew that the public high school where we sent all three of our children was unusually diverse, yet it was a family conversation how the gifted/accelerated classes were almost all white and Asian.  Because of what Barb and I do, we kept an eye on the AP CS class at that high school, and were surprised every year at how few Blacks ever entered the class, despite the significant percentage of Black students in the school. I’m glad that, years later, Katie still thinks about those issues and why so few Black students made it into her AP classes.

 

December 3, 2018 at 8:00 am 2 comments

Literature is to Composition, as Computer Science is to Computational Literacy/Thinking

Coding_Literacy___The_MIT_Press

Annette Vee was visiting in Ann Arbor, and looked me up. We had coffee and a great conversation.  Annette is an English Professor who teaches Composition at University of Pittsburgh (see website here). She published a book last year with MIT Press, Coding Literacy: How Computer Programming is Changing Writing. (I’m part way through it and recommend it!) She knew me from this blog and my other writing about computational literacy. I was thrilled to meet someone who makes the argument for code-as-literacy with a real claim to understanding literacy.

One of the themes in our conversation was the distinction between literature and composition.  (I’m going to summarize something we were talking about — Annette is not responsible for me getting things wrong here.) Literature is about doing writing very well, about writing great works that stand the test of time. It’s about understanding and emulating greater writers.  Composition is about writing well for communicationIt’s about letters to Grandma, and office memos, and making your emails effective.  Composition is about writing understandable prose, not great prose as in literature. People in literature sometimes look down on those in composition.

There’s a similar distinction to be made between computer science as it’s taught in Universities and what Annette and I are calling coding/computational literacy (but which might be what Aman Yadav and Shuchi Grover are calling computational thinking).  Computer science aims to prepare people to engineer complex, robust, and secure systems that work effectively for many users. Computational literacy is about people using code to communicate, to express thoughts, and to test ideas. This code doesn’t have to be pretty or robust. It certainly shouldn’t be complex, or nobody will do it. It should be secure, but that security should probably be built into the programming system rather than expecting to teach people about it (as Ben Herold recently talked about).  People in computer science will likely look down on those teaching computational literacy or computational thinking. That’s okay.

Few people will write literature. Everyone will compose.

November 23, 2018 at 7:00 am 41 comments

When do we know that a programming course is not working for non-CS majors?

There’s a good discussion going on in Facebook that I wanted to make more public and raise as a question here.  The crush of undergraduates in CS today is making it difficult to offer tailored introductory CS courses to different majors.  The problem is particularly acute when designing instruction for future CS teachers.  If you put the CS teachers in the same course as the CS majors, it’s cheaper and easier — you just teach one big course, rather than multiple smaller courses. But is it as effective?

Some of my colleagues suggest that we can design undergraduate introductory computing courses that are effective for both non-CS and CS majors.  Here’s my question: How do you know when it’s not working?  At what point would you admit that the one course option isn’t working? It’s an interesting empirical question.

Here are some possible measures:

  • Learning is a tricky measure.  For any discipline, the majors in that discipline are more motivated to learn more than the outside-the-discipline majors. You can’t expect the non-CS majors to learn more than the CS majors.  Then again, the non-CS majors probably come in knowing less.  If you do pre- and post-tests on CS knowledge, do non-CS majors have as large of a gain as the CS majors?  I don’t know, but in any case, it’s not a great measure for deciding if a class is succeeding for the non-CS majors.
  • Taking more CS courses may be an effective measure, but only if you have more than one course that’s useful to non-CS majors.  If the rest of the classes are about software development, then non-CS majors will probably not want to go on, even if the intro course was effective and well-designed.
  • Retention is a reasonable measure.  If more of the non-CS majors are dropping out from the course than the CS majors, you may not be meeting their needs.
  • My favorite measure is relevance I argued in my blog post on Monday that programming is a practice that is relevant to many communities. Do the non-CS majors see the relevance of computing for them and their community after the introductory course?  If not, I don’t think it’s meeting their needs.
  • Another tricky measure is use. Should non-CS majors be able (after their first course) to build some program that they find useful?  Certainly, if you achieve that goal, you have also achieved relevance.  How do you judge useful?  CS faculty may not be good judges of what a non-CS major would find useful, and CS faculty are most likely going to assess in terms of code quality (e.g., modularization, appropriate variable and function/module names, commenting, code style, etc.), which I consider pretty unimportant for as a measure for the non-CS students’ experience in the first course.

What do you think?  How would you know if your intro course was meeting non-CS students’ needs?

November 9, 2018 at 7:00 am 13 comments

What would convince faculty in other disciplines that programming is useful?

Recently I came across an article from the journal Issues in Information Systems, “Faculty perspectives on the information technology and analytics requirements of business students.” The authors surveyed 204 business faculty from 20 different universities.  They found that “[N]early a third of respondents (32.6%) felt that computer programming skills should not be required at all. Interestingly, the same number (32.6%) also believe that Calculus should not be required of business students.”  Below is the table with the results.  About a third of faculty actually thought that all business students should take a three credit hour course in programming, but a third also felt that it shouldn’t be required at all. Details in the table below:

business-faculty

I’ve been working with the Georgia Department of Education on a new kind of pre-calculus course that uses computing to demonstrate the pre-calc concepts in a variety of contexts, e.g., scalar multiplication of a vector by reducing red in all the pixels in a picture, matrix multiplication by doing transforms of objects in 3-D space, periodicity of functions (like trigonometric functions) to generate sounds, etc. We did a careful mapping of each pre-calculus learning objective to relevant computing demonstrations, with multiple possible computing contexts for each pre-calculus learning objective. The course was rejected by the mathematics oversight board today. They didn’t buy it at all.  Among the responses: “The description of the course states that it is ‘designed to prepare students for calculus and other college level mathematics courses,’ which they believe it does not” and “Members feel that computer science is not mathematics and should not be replacing a mathematics course.”

I’m struck by these two stories.  For me, programming is this useful new notation that can enhance learning in many disciplines.  I’m swayed by the results with Bootstrap and with the CT-STEM effort at Northwestern. I hadn’t realized the extent to which the teachers in the non-CS disciplines were not buying the story.

  • Business faculty are clearly dubious about the benefits of programming for business students.  I wonder if they’ve done the studies about how many business school graduates use programming (from SQL queries and spreadsheet macros, to data analysis and even modeling and simulation) in their daily jobs.
  • Mathematics faculty are clearly dubious that (a) programming to apply mathematics topics leads to more mathematics learning and (b) computer science is even related to mathematics.

These create an interesting set of research questions to me. Why are faculty in non-CS disciplines dubious about the advantages of programming for their students?  What do they think programming is?  Maybe they’re right — maybe “programming” as we are currently defining it isn’t worth the credit hours for their students. How could we re-define programming (and programming languages and tools) to make it more useful?

October 26, 2018 at 7:00 am 34 comments

US National Science Foundation increases emphasis on broadening participation in computing

The computing directorate at the US National Science Foundation (CISE) has increased its emphasis on broadening participation in computing (BPC).  (See quote below and FAQ here.) They had a pilot program where large research grants were required to include a plan to increase the participation of groups or populations underrepresented or under-served in computing. They are now expanding the program to include medium and large scale grants. The idea is to get more computing researchers nationwide focusing on BPC goals.

CISE recognizes that BPC requires an array of long-term, sustained efforts, and will require the participation of the entire community. Efforts to broaden participation must be action-oriented and must take advantage of multiple approaches to eliminate or overcome barriers. BPC depends on many factors, and involves changing culture throughout academia—within departments, classrooms, and research groups. This change begins with enhanced awareness of barriers to participation as well as remedies throughout the CISE community, including among principal investigators (PIs), students, and reviewers. BPC may therefore involve a wide range of activities, examples of which include participating in professional development opportunities aimed at providing more inclusive environments, joining various existing and future collective impact programs to helping develop and implement departmental BPC plans that build awareness, inclusion, and engagement, and conducting outreach to underrepresented groups at all levels (K-12, undergraduate, graduate, and postgraduate).

In 2017, CISE commenced a pilot effort to increase the community’s involvement in BPC, by requiring BPC plans to be included in proposals for certain large awards [notably proposals to the Expeditions in Computing program, plus Frontier proposals to the Cyber-Physical Systems and Secure and Trustworthy Cyberspace (SaTC) programs]. By expanding the pilot to require that Medium and Large projects in certain CISE programs [the core programs of the CISE Divisions of Computing and Communication Foundations (CCF), Computer and Network Systems (CNS), and Information and Intelligent Systems (IIS), plus the SaTC program] have approved plans in place at award time in 2019, CISE hopes to accomplish several things:

  • Continue to signal the importance of and commitment to BPC;
  • Stimulate the CISE community to take action; and
  • Educate the CISE community about the many ways in which members of the community can contribute to BPC.

The long-term goal of this pilot is for all segments of the population to have clear paths and opportunities to contribute to computing and closely related disciplines.

Read more at https://www.nsf.gov/pubs/2018/nsf18101/nsf18101.jsp

August 31, 2018 at 7:00 am 1 comment

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