What does it mean to reach “all” in #CS4All? Qualify your Quantifiers | blog@CACM

I’ve raised the concern before that the CS for All effort might mean “CS for only the rich” (see post here). Our data from Georgia suggest that few students are actually getting access to CS education, even if there is a CS teacher in the school (see post here).  Kathi Fisler, Shriram Krishnamurthi, and Emmanuel Schanzer offer a Blog@CACM post where they consider how we make sure that #CS4All is equitable.

Mandating every child take a computing class is a great way to ensure everyone takes CS, but very few states, cities, or even school districts are in a position to hire enough dedicated CS teachers or offer dedicated CS classes to reach every child. Recent declarations from several major districts that “every child will learn to code” often place impossible burdens on schools. Similarly, few schools can afford to offer CS programs that require cutting-edge computers, expensive consumables, or technology that requires significant maintenance.

To truly achieve CS4All Students in a sustainable way, equity and scale are issues that must be built in by design. Similarly, initiatives have to think about differently-abled users from scratch, not just bolt them on as an afterthought. Accessibility needs to be designed into software, curriculum, and pedagogy from the earliest stages.

The “move fast and break things” culture of computing is no help here. Right now, computing education has enormous attention. That day will pass. By the time we get around to focusing on equity, we may have depleted the energy left to overhaul computing curricula. Instead, we have to think this through at the very outset. Another computing principle is that products typically get one shot at gaining users’ attention. For the foreseeable future, this is that one shot for computing education.

Source: Qualify your Quantifiers | blog@CACM | Communications of the ACM

Call for Participants: ICER Doctoral Consortium, Sept 8th, Melbourne, Australia

The ICER 2016 Doctoral Consortium provides an opportunity for doctoral students studying computing education to explore and develop their research interests in a supportive workshop environment with a panel of established researchers. We invite students to apply for this opportunity to share their work with students in a similar situation as well as senior researchers in the field.

Applicants to the Doctoral Consortium should have begun their research, but should not have completed it.  We want people who have questions to raise with their peers and the more senior mentors, and who still have time to respond to and use the feedback in their research.

DC Co-Chairs for 2016:

Anthony Robins, University of Otago, New Zealand

Ben Shapiro, University of Colorado, USA

Contact us at: icerdc2016@gmail.com

The DC has the following objectives:

• Provide participants a supportive setting for feedback on their research
• Offer participants comments and fresh perspectives from outside their own institution
• Promote the development of a supportive community of scholars
• Support a new generation of researchers with information and advice on research and academic career paths
• Contribute to the conference goals through interaction with other researchers and conference events

The DC will be held on Thursday, September 8, 2016 (prior to the main ICER conference, in Melbourne, Australia). Students at any stage of their doctoral studies are welcome to apply and attend. The number of participants is limited to 15. Applicants who are selected will receive a limited partial reimbursement of travel, accommodation and subsistence (i.e., food) expenses of $600 (USD).  An extra $200 may be available for participants with travel expenses greatly exceeding the standard support.

Process Timeline:

• Friday 20th May – initial submission
• Friday 3rd June – notification of acceptance
• Friday 17th June – camera ready copy due

You can find more information on applying athttps://icer.hosting.acm.org/icer-2016/doctoral-consortium/

Top business leaders, 27 governors, urge Congress to boost computer science education – The Washington Post

I saw on Facebook that Hadi Partovi was at Congress.  Now I see why — there’s an effort underway to get Congress to fund more in CS education.  I’m wondering what they want to get funded.  Incentives for teachers? Professional development? Pre-service education?  Does someone know the details?

Despite this groundswell, three-quarters of U.S. schools do not offer meaningful computer science courses. At a time when every industry in every state is impacted by advances in computer technology, our schools should give all students the opportunity to understand how this technology works, to learn how to be creators, coders, and makers — not just consumers. Instead, what is increasingly a basic skill is only available to the lucky few, leaving most students behind, particularly students of color and girls.

How is this acceptable? America leads the world in technology. We invented the personal computer, the Internet, e-commerce, social networking, and the smartphone. This is our chance to position the next generation to participate in the new American Dream.

Source: Top business leaders, 27 governors, urge Congress to boost computer science education – The Washington Post

Transfer of learning: Making sense of what education research is telling us

I enjoy reading “Gas station without pumps,” and the below-quoted post was one I wanted to respond to.

Two of the popular memes of education researchers, “transferability is an illusion” and “the growth mindset”, are almost in direct opposition, and I don’t know how to reconcile them.

One possibility is that few students actually attempt to learn the general problem-solving skills that math, CS, and engineering design are rich domains for.  Most are content to learn one tiny skill at a time, in complete isolation from other skills and ideas. Students who are particularly good at memory work often choose this route, memorizing pages of trigonometric identities, for example, rather than learning how to derive them at need from a few basics. If students don’t make an attempt to learn transferable skills, then they probably won’t.  This is roughly equivalent to claiming that most students have a fixed mindset with respect to transferable skills, and suggests that transferability is possible, even if it is not currently being learned.

Teaching and testing techniques are often designed to foster an isolation of ideas, focusing on one idea at a time to reduce student confusion. Unfortunately, transferable learning comes not from practice of ideas in isolation, but from learning to retrieve and combine ideas—from doing multi-step problems that are not scaffolded by the teacher.

Source: Transfer of learning | Gas station without pumps

The problem with “transferability” is that it’s an ill-defined term.  Certainly, there is transfer of skill between domains.  Sharon Carver showed a long time ago that she could teach debugging Logo programs, and students would transfer that debugging process to instructions on a map (mentioned in post here).  That’s transferring a skill or a procedure.  We probably do transfer big, high-level heuristics like “divide-and-conquer” or “isolate the problem.”  One issue is whether we can teach them.  John Sweller says that we can’t — we must learn them (it’s a necessary survival skill), but they’re learned from abstracting experience (see Neil Brown’s nice summary of Sweller’s SIGCSE keynote).

Whether we can teach them or not, what we do know is that higher-order thinking is built on lots of content knowledge. Novices are unlikely to transfer until they know a lot of stuff, a lot of examples, a lot of situations. For example, novice designers often have “design fixation.”  They decide that the first thing they think of must be the right answer.  We can insist that novice designers generate more designs, but they’re not going to generate more good designs until they know more designs.  Transfer happens pretty easily when you know a lot of content and have seen a lot of situations, and you recognize that one situation is actually like another.

Everybody starts out learning one tiny skill at a time.  If you know a lot of skills (maybe because you have lots of prior experience, maybe because you have thought about these skills a lot and have recognized the general principles), you can start chunking these skills and learning whole schema and higher-level skills.  But you can’t do that until you know lots of skills.  Students who want to learn one tiny skill at a time may actually need to still learn one tiny skill at a time. People abstract (e.g., able to derive a solution rather than memorize it) when they know enough content that it’s useful and possible for them to abstract over it.  I completely agree that students have to try to abstract.  They have to learn a lot of stuff, and then they have to be in a situation where it’s useful for them to abstract.

“Growth mindset” is a necessity for any of this to work.  Students have to believe that content is worth knowing and that they can learn it.  If students believe that content is useless, or that they just “don’t do math” or “am not a computer person” (both of which I’ve heard in just the last week), they are unlikely to learn content, they are unlikely to see patterns in it, and they are unlikely to abstract over it.

Kevin is probably right that we don’t teach problem solving in engineering or computing well.  I blogged on this theme for CACM last month — laboratory experiments work better for a wider range students than classroom studies.  Maybe we teach better in labs than in classrooms?  The worked examples effect suggests that we may be asking students to problem solve too much.  We should show students more completely worked out problems.  As Sweller said at SIGCSE, we can’t expect students to solve novel problems.  We have to expect students to match new problems to solutions that they have already seen.  We do want students to solve problems, too, and not just review example solutions. Trafton and Reiser showed that these should be interleaved: Example, Problem, Example, Problem… (see this page for a summary of some of the worked examples research, including Trafton & Reiser).

When I used to do Engineering Education research, one of my largest projects was a complete flop.  We had all this prior work showing the benefits of a particular collaborative learning technology and technique, then we took it into the engineering classroom and…poof! Nothing happened.  In response, we started a project to figure out why it failed so badly.  One of our findings was that “learned helplessness” was rampant in our classes, which is a symptom of a fixed mindset.  “I know that I’m wrong, and there’s nothing that I can do about it.  Collaboration just puts my errors on display for everyone,” was the kind of response we’ve got. (See here for one of our papers on this work.)

I believe that all the things Kevin sees going wrong in his classes really are happening.  I believe he’s not seeing transfer that he might reasonably expect to see.  I believe that he doesn’t see students trying to abstract across lower-level skills.  But I suspect that the problem is the lack of a growth mindset.  In our work, we saw Engineering students simply give up.  They felt like they couldn’t learn, they couldn’t keep up, so they just memorized.  I don’t know that that’s the cause of the problems that Kevin is seeing.  In my work, I’ve often found that motivation and incentive are key to engagement and learning.

LaTICE 2017 in Saudi Arabia: Women must cover up

At the end of LaTICE 2016, the Vice-Rector of Al-Baha University in Saudi Arabia (see information here) welcomed attendees to LaTICE 2017. After the presentation about Al-Baha University, Sahana Murthy of IIT-Bombay stood up and asked, “Can I come to LaTICE 2017 dressed as I am right now, in Indian clothes?” The Vice-Rector replied, “No.” All women, including foreigners, will be required to cover their hair at LaTICE 2017.

That exchange was a central topic of conversation for the rest of the conference and in social media for me. I heard some female computing education researchers say that they would attend anyway. Many I heard from expressed outrage. Several were angry that the organizing committee for LaTICE would even place the conference in Saudi Arabia under these restrictions.

I spoke to Neena Thota about LaTICE 2017 (seen below after my keynote).  She was one of the Chairs for LaTICE 2016 (faculty at Uppsala University and University of St. Joseph in Macau) who went to Saudi Arabia in preparation for the conference.  She felt respected there and taken seriously as a scholar, but she did have to cover-up.  Neena doesn’t expect that the rules for women in Saudi Arabia (see the Wikipedia page here about them) will change for a long time.  Do we simply ignore the scholars there and ostracize them, for rules over which they may have no control?  As in Qatar, computer science students in Saudi Arabia are majority female.

The question is no longer rhetorical for me. I was invited to attend the Program Committee meeting at LaTICE 2016 as a non-voting observer, and I have been invited to serve on the PC for LaTICE 2017. I have already had several people warn me that I should not participate. They urged me to shun the conference publicly, in order to send a clear message against the treatment of women in Saudi Arabia.

I’ve been thinking about this, and discussing it with women in my life (my wife, my daughters, and my colleagues).  I’m not female, and I can’t fully understand my own biases as a male, so I sought advice from women in my life and very much appreciate all the comments I received. I’ve decided that I will serve on the LaTICE 2017 program committee.

I understand the reasons of anyone who chooses not to participate.  Those who choose not to review are sending a message that LaTICE should never have gone to a place that restricts the rights of women.  I can understand why women, especially from the West, might choose not to attend. I don’t think foreign women should go there, unless they’re willing to abide by the laws and customs of the place they’re visiting.

Here are my reasons for thinking it worthwhile to engage in LaTICE 2017:

1. The female Computing students and faculty in Saudi Arabia might not otherwise be able to attend a conference like LaTICE. Unless LaTICE goes there, they do not get the opportunity to hear other perspectives, to share their practices, and to participate in a community of education scholars. By participating in the PC, I get to share what I know about computing education with the community of scholars in Saudi Arabia, both female and male.
2. As an education researcher, I know that learning and change occurs from active dialogue, not from passive silence. I doubt that I can change much in Saudi Arabia, either by my engagement or my public refusal to engage. This semester our seminar on Learning Sciences and Technologies at Georgia Tech read Paulo Freire’s Pedagogy of the Oppressed. Freire points out that privileged people can’t solve the problems of the less-privileged, nor can the privileged even “help” the less-privileged. All that any of us can do is to create dialogue which creates opportunities for learning for everyone. Freire explicitly includes teachers in that everyone. Teachers ought to aim to learn from students. Dialogue requires engagement.  Reading papers and responding to them with my comments creates dialogue.
3. Finally, I want to be engaged because of what I will learn. I’m curious. I learned more about India from attending LaTICE 2016 (see the first and second blog posts in this series). I would like to learn more about Saudi Arabia. It makes me a more informed and effective researcher when I am more aware of other contexts.

Neeti Pathak, one of the students with whom I work, pointed out that there is interplay between religion and culture in Saudi Arabia. I also look to my own faith in thinking about LaTICE 2017. Pope Francis, the leading figure in my faith, recently made a proclamation encouraging the Church to be more welcoming, even to those that the Church may have once ostracized (see NYTimes piece). That’s a proclamation that relates to LaTICE 2017. Everyone gains by engaging, even with those whose activities and rules we might not like.

I’m not willing to ostracize a whole country, even if they have rules and customs that I think are wrong. I’m not confident that I understand the issues in Saudi Arabia. I’m not confident that my views on them are more than my Western biases interpreting customs and values I don’t understand. I don’t feel justified in making a statement against LaTICE 2017. I see value in engaging in dialogue.

I shared earlier versions of this post with several colleagues, who are angry with me for the stance I’m taking. These are complicated issues. I am sure that there are many more perspectives that I have not yet considered. I welcome further discussion in the comments, including telling me why I’m wrong.

The Indian Education Context is Completely Different from the US Education Context

At LaTICE 2016, I attended a session on teacher professional development. I work at preparing high school CS teachers. I felt like I’d be able to relate to the professional development work. I was wrong.

One of the large projects presented at LaTICE 2016 was the T10kT project (see link here) whose goal is to use technology to train 10,000 teachers. What I didn’t realize at first was that the focus is on higher-education teachers, not high school teachers. The only high school outreach activity I learned about at LaTICE 2016 was from the second keynote, on an Informatics Olympiad from Madhavan Mukund (see slides here) which is only for a select group of students.

India has 500 universities, and over 42,000 higher education institutions. They have an enormous problem trying to maintain the quality of their higher-education system (see more on the Wikipedia page). They rely heavily on video, because videos can be placed on a CD or USB drive and mailed. The T10kT instructors can’t always rely on Internet access even to higher-education institutions. They can’t expect travel even to regional hubs because many of the faculty can’t travel (due to expense and family obligations).

As can be seen in the slide above, they have a huge number of participants.  I asked at the session, “Why?”  Why would all these higher-education faculty be interested in training to become better teachers?  The answer was that participants get certificates for participating in T10kT, and those certificates do get considered in promotion decisions.  That’s significant, and something I wish we had in the US.

I tried to get a sense for how many primary and secondary schools there are in India, and found estimates ranging from 740K to 1.3M. Compulsory education was only established in 2010 (goes to age 14), and is not well enforced. I heard estimates that about 50% of school-age children go to school because only enrollment is checked, not attendance.

Contrast this with the CS10K effort in the United States. There are about 25-30,000 high schools in the US. Having 10K CS teachers wouldn’t reach every school, but it would make a sizable dent. A goal to get 10K CS teachers in Indian high schools would be laughable. When you increase the number of high schools by two orders of magnitude, 10,000 teachers barely moves the needle. Given the difficulty of access and uncertain Internet, it’s certainly not cheaper to provide professional development in India. They have an enormous shortage of teachers — not just CS. They lack any teachers at all in many schools. The current national focus is on higher-education because the secondary and primary school problems are just so large.

Alan Kay has several times encouraged me to think about how to provide educational technology to support students who do not have access to a teacher. I resisted, because I felt that any educational technology was a poor substitute for a real teacher. Now I realize what a privilege it is to have any teacher at all, and how important it is to think about technology-based guided learning for the majority of students worldwide who do not have access to a teacher.

How do we do it?  How do we design technology-based learning supports for Indian students who may not have access to a teacher?  I attended a session on IITBx, the edX-hosted MOOCS developed by IIT-Bombay. I tweeted:

#latice2016 Bravo! #iitb MOOCs are made explicitly for India. Advice for US/UK-providers on how to make their MOOCs more useful in India?

— Mark Guzdial (@guzdial) April 3, 2016

One of the IIT-Bombay graduate students responded:

I believe the ans is to have MOOCs made in India and for India keeping the local context in mind #latice2016 https://t.co/Bs9wLFMGmJ

— akothiyal (@akothiyal) April 3, 2016

Here’s the exchange as a screencap, just in case the Twitter feed doesn’t work right above:

I’m sure that Aditi (whose work was described in the previous blog post) is right. Developers in the US can’t expect to build technologies for India and expect them to work, not without involving Indian learners, teachers, and researchers. One of the themes in my book Learner-Centered Design of Computing Education is that motivation is everything in learning, and motivation is tied tightly to notions of identity, community of practice, and context. I learned that I don’t know much about any of those things for India, nor anywhere else in the developing world. The problems are enormous and worth solving, and US researchers and developers have a lot to offer — as collaborators. In the end, it requires understanding on the ground to get the context and motivation right, and nothing works if you don’t get that right.

LATICE 2016 in Mumbai: An exciting, vibrant conference with great students

I was at the Learning and Teaching in Computing Education (LaTICE 2016) conference in Mumbai in early April. It was one of my most memorable and thought-provoking trips. I have had few experiences in Asia, and none in India, so I was wide-eyed with amazement most of my time there. (Most of the pictures that I am including in this series of blog posts are mine or come from the LaTICE 2016 gallery.)

I was invited to join discussants at the LaTICE Doctoral Consortium on the day before the conference. LaTICE was hosted at IIT-Bombay, and IIT-Bombay is home to the Inter-disciplinary Program in Educational Technology (see link here). The IPD-ET program is an impressive program. Only five years old, it already has 20 PhD students. The lead faculty are Sahana Murthy and Sridhar Iyer who are guiding these students through interesting work. (Below picture shows Sahana with the DC co-chairs, Anders Berglund from Uppsala University and Tony Clear from Auckland University of Technology.) The Doctoral Consortium had students from across India and one from Germany. Not all were IDP-ET students, but most were.

Talking to graduate students was my main activity at LaTICE 2017. Aman Yadav (from Michigan State, in the back of the below picture) and I missed a lot of sessions as we met with groups of students. I don’t think I met all the IDP-ET students, but I met many of them, and wrestled with ideas with them. I was pleased that students didn’t just take me at my word — they asked for explanations and references. (I ripped out half of the pages of my notebook, handing out notes with names of papers and researchers.) I feel grateful for the experience of hearing about so many varied projects and to talk through issues with many students.

I’m going to take my blog writer’s prerogative to talk about some of the IDP-ET students’ work that I’ve been thinking about since I got back. I’m not claiming that this is the best work, and I do offer apologies to the (many!) students whose work I’m not mentioning. These are just the projects that keep popping up in my (still not sleeping correctly) brain.

Aditi Kothiyal is interested in how engineers estimate. Every expert engineer does back-of-the-envelope estimation before starting a project. It’s completely natural for them. How does that develop? Can we teach that process to students? Aditi has a paper at the International Conference of the Learning Sciences this year on her studies of how experts do estimation. I find this problem interesting because estimation might be one of those hard-to-transfer higher-order thinking skills OR it could be a rule-of-thumb procedure that could be taught.

Shitanshu Mishra is exploring question-posing as a way to encourage knowledge integration. He’s struggling with a fascinating set of issues. Question-posing is a great activity that leads to learning, but is practiced infrequently in classroom, especially by the students who need it the most. Shitanshu has developed a guided process (think the whiteboards in Problem-Based Learning, or classroom rituals in Janet Kolodner’s Learning-By-Design, or Scardamalia & Bereiter’s procedural facilitation) which measurably helps students to pose good questions that encourage students to integrate knowledge. When should he guide students through his question-posing process? Is it important that students use his process on their own?

Yogendra Pal is asking a question that is very important in India whose answer may also be useful here in the US: How do you help students who grew up in a non-English language in adapting to English-centric CS? India’s constitution recognizes 22 languages, and has 122 languages spoken by many Indian citizens on a daily basis. Language issues are core to the Indian experience. CS is very English-centric, from the words in our programming languages, to the technical terms that don’t always map to other languages. Yogendra is working with students who only spoke Hindi until they got to University, where they now want to adapt to English, the language of the Tech industry. I wonder if Yogendra’s scaffolding techniques would help children of immigrant families in the US succeed in CS.

Rwitajit Majumdar is developing visualizations to track student behavior on questions over time. Originally, he wanted to help teachers get a sense of how their students move towards a correct understanding over multiple questions during Peer Instruction. Now, he’s exploring using his visualizations with MOOC data. I’m interested in his visualizations for our ebooks. He’s trying to solve an important problem. It’s one thing to know that 35% of the students got Problem #1 right, and 75% got (similar) Problem #2 right. But is it the same 25% of students who got both wrong? What percentage of students are getting more right, and are there any that are swapping to more wrong answers? Tracking students across time, across problems is an important problem.

Overall, the LaTICE conference was comparable to SIGCSE or ITiCSE. It was single track, though it’s been dual-track at some instances. LaTICE is mostly a practitioner’s conference, with a number of papers saying, “Here’s what I’m doing in my class” without much evaluation. I found even those interesting, because many were set in contexts that were outside my experience. There are some good research papers. And there are some papers that said some things that I felt were outright wrong. But because LaTICE is a small (< 200 attendees, I’d guess) and collegial conference, I had one-on-one conversations with all the authors with whom I disagreed (and many others, as well!) to talk through issues.

My keynote was based on my book, Learner-Centered Design of Computing Education: Research on Computing for Everyone. I talked about why it’s important to provide computing education to more than computing majors, and how computing education would have to change for different audiences. Slides are here: http://www.slideshare.net/markguzdial/latice-2016-learnercentered-design-of-computing-education-for-all

The most remarkable part of my trip was simply being in India. I’ve never been any place so crowded, so chaotic, so dirty, and so vibrant. I felt like I took my life in my hands whenever I crossed the street after noon on any day (and given the pedestrian accidents that some conference participants reported seeing, including one possible fatality, I likely was taking a risk). I went out for three runs around Mumbai and across campus (only in the morning when the traffic was manageable) and enjoyed interactions with cows and monkeys. I was shocked at the miles and miles of slums I saw when driving around Mumbai. I got stuck on one side of a major street without any idea how I could possibly get through the crowds and traffic to the other side — on a normal Sunday night. The rich colors of the Indian clothing palette were beautiful, even in the poorest neighborhoods. There was an energy everywhere I went in Mumbai.

I’ve not experienced anything like Mumbai before. I certainly have a new sense of my own privilege — about the things I have that I never even noticed until I was somewhere where they are not given. Given that India has 1.2 billion people and the US only has some 320 million, I’m wondering about how I define “normal.”

Spreadsheets as an intuitive approach to variables: I don’t buy it

A piece in The Guardian (linked by Deepak Kumar on Facebook) described how Visicalc became so popular, and suggests that spreadsheets make variables “intuitive.” I don’t buy it. Yes, I believe that spreadsheets help students to understand that a value can change (which is what the quote below describes). I am not sure that spreadsheets help students to understand the implications of that change. In SBF (Structure, Behavior, Function) terms, spreadsheets make the structural aspect of variables visible — variables vary. They don’t make evident the behavior (how variables connect/influence to one another), and they don’t help students to understand function of the variable or the overall spreadsheet. If we think about the misconceptions that students have about variables, the varying characteristic is not the most challenging one.

The Bootstrap folks have some evidence that their approach to teaching variables in Racket helps students understand variables better in algebra. It would be interesting to explore the use of spreadsheets in a similar curriculum — could spreadsheets help with algebra, too? I don’t expect that we’d get the same results, in part because spreadsheet variables don’t look like algebra variables. Surface-level features matter a lot for novices.

 

Years ago, I began to wonder if the popularity of spreadsheets might be due to the fact that humans are genetically programmed to understand them. At the time, I was teaching mathematics to complete beginners, and finding that while they were fine with arithmetic, algebra completely eluded them. The moment one said “let x be the number of apples”, their eyes would glaze and one knew they were lost. But the same people had no problem entering a number into a spreadsheet cell labelled “Number of apples”, happily changing it at will and observing the ensuing results. In other words, they intuitively understood the concept of a variable.

Source: Why a simple spreadsheet spread like wildfire | Opinion | The Guardian

Where CS Teachers See Bimodality: Guest Blog Post from Elizabeth Patitsas

When I saw Elizabeth’s debrief, I asked her if I could share it on here. She graciously prepared this guest blog post, with more detail to explain what she did. Thanks, Elizabeth!

About a year ago, I used a number of venues to recruit participants for an anonymous study about the distributions of grades in CS classes. The study involved a minor deception, and because we do not have the emails of all the participants, I’m posting the debrief openly.

——————————

Dear study participant,

You’re getting this email because about a year ago you participated in my research project, “An investigation of the grades distributions in university computer science”. In this project, I showed you a series of six histograms and I asked you how often you saw that distribution’s shape in your own teaching, as well as how you’d categorize the distribution (normal, bimodal, etc).

I’m writing to you know to let you know this study involved a minor deception. We were actually most interested whether you’d label some ambiguous distributions as “normal” versus “bimodal”. We’ve now completed our analysis, and we want to debrief you before we write up our results for publication.

As you may know, there is a common perception amongst CS educators that grades distributions are bimodal. However, upon statistical analysis of the grades distribution available to us, we discovered that most of our grades distributions pass statistical tests of normality and very few of them of them pass the statistical tests of bimodality (see link for more).

We were curious why the perception of bimodal grades is so prevalent, even when grades may actually be normally-distributed. Ahadi and Lister argued at ICER 2013 that the perception of bimodal grades comes from CS educators believing that some students possess an innate gift/talent to do computer science. Regular readers of Mark’s blog would know this as the “Geek Gene Hypothesis”: the notion that when it comes to CS, you either can get it, or you can’t.

Ahadi and Lister argued that when people see two “peaks” in their grades, it’s because they’re expecting to see two different populations to be represented: the students who get it, and the students who don’t. Usually, bimodal distributions represent data where you’ve sampled two different populations together at the same time. If CS grades were bimodal, that could imply we have two different populations of students (e.g. those who get it + those who don’t). Whereas if CS grades are normal, it would imply (but not prove) our students form a spectrum, where most students understand some — but not all — of the material.

In the study you participated in, all six histograms were randomly-generated normal distributions with a small sample size (and so looked noisy). We wanted to test two things:

1. RQ1. Are CS educators more likely to label ambiguous distributions as “bimodal” if they believe that some students are inherently predisposed to do well in CS?
2. RQ2. If we tell CS educators that it’s a commonly-held belief that CS grades are bimodal, will educators be more likely to label ambiguous distributions as “bimodal”?

For a random half of the participants, before you categorized the distributions, we asked you “It is a commonly-held belief that CS grades distributions are bimodal. Do you find this to be the case in your teaching?” The other half of the participants saw this question after categorizing the distributions. This priming was used to test RQ2.

If our consent form had said the true intent of the study, then all participants would have been primed, rather than a random half. Our minor deception about the purpose of the study was necessary to answer this research question.

We delayed the debriefing until after our analysis was complete, on the assumption you’d want to know the preliminary results of the study. We indeed found that participants who agreed more strongly with the statement “Some students are innately predisposed to do better at CS than others” were statistically significantly more likely to label ambiguous distributions as bimodal.

Participants who had been primed were more likely to label distributions as bimodal — and this effect was stronger if they also agreed that CS ability was innate.

To ensure participant anonymity, we did not collect names and emails on the SurveyMonkey survey. As a result we have no way to link your responses to your identity.

We will be submitting our results for publication in the coming months, and will happily disseminate the paper once it is published. If you’d like to receive a copy of the paper once it’s published, add your email to this form. If you have any further questions, please contact us.

Thank you,

Elizabeth Patitsas

Jesse Berlin

Michelle Craig

Steve Easterbrook

Treating computing as a real literacy: If learning to code were like learning to write…

I like what Amy Ko is doing in this blog post. She imagines if computing literacy were integrated into our daily lives, how would we introduce computing to children, how would we talk about it to our children?

I don’t buy the focus in this post on tokenizing and textual languages.  I would hope that children would talk about feedback, and iteration, and how to build conditionals that tested what you wanted (e.g., “How would you know if that was true?” — which might lead to some great discussions about truth and experiments and science, all by itself!).  The point of a piece like Amy’s is to have these discussions, to talk about what we’d integrate and where.

I’m reminded of Mike Horn’s work on computational sticker books (see link here).  Mike asks the question, “If computational literacy were integrated into our daily lives, how would parent and child do computation while reading a book at bedtime?”  Mike’s answer is computational sticker books. Doesn’t matter whether you agree with Mike about sticker books, the point is to wonder what a future world of computational integration might be like.

If learning to code were like learning to write, we’d next teach children how to read short books, giving them programs to read, exposing them to all of the computational possibilities of the language they were learning. “Madison, what did you choose for your book project this month? Oh, an Instagram post indexing algorithm, interesting! Are you liking it? What’s your favorite idiom?”

If learning to code were like learning to write, we’d ask children to start writing sentences, creating simple statements that accomplish small tasks. “Daniel, we keep forgetting to turn the light off in the garage. Can you log into the IoT portal and write a rule that turns it off every night at 9 pm?”

Source: If learning to code were like learning to write… | Bits and Behavior

Survey for CS Faculty on use of Evidence-based Instructional Practices: Guest blog post from Scott Grissom

Clearly an important topic — I’m sharing this here, with thanks to Scott.

The SIGCSE Committee on Evidence-based Instructional Practices is investigating the most commonly used teaching practices in CS education (such as classroom activities, student learning goals and assessment techniques). We are replicating a study from physics education that surveyed over 800 faculty. We have already used the validated instrument in a pilot study with twelve institutions and have received 160 responses so far.

Rather than simply send a survey link to this mailing list that might create a skewed sample, we are inviting entire CS departments to survey their members. Our goal is to survey instructors from many 2-year, 4-year, private, public, research and teaching institutions.

The project will allow us to accomplish three important objectives:

• Provide a baseline of instructional practices used in CS higher education.
• Compare CS instructional practices with other STEM disciplines.
• Inform efforts to reform CS education by increasing the adoption of evidence-based instructional practices.

WILL YOU INTRODUCE US TO YOUR COLLEAGUES?
Best survey practices have shown that an introduction from a trusted colleague increases response rates. This is where you can help! Are you willing to support the CS education community by introducing us to your colleagues and encouraging them to complete the survey?

Please contact Scott Grissom BY FRIDAY APRIL 15 if you are willing to help, and we will provide more information about what we will ask you to do.

Thanks,

SIGCSE Committee on Evidence-based Instructional Practices
– Scott Grissom, Grand Valley State University, grissom@gvsu.edu
– Sue Fitzgerald, Metropolitan State University, sue.fitzgerald@metrostate.edu
– Renée McCauley, College of Charleston, mccauleyr@cofc.edu
– Laurie Murphy, Pacific Lutheran University, lmurphy@plu.edu

Call for Participation: International Computing Education Research 2016 in Melbourne, Australia

The twelfth annual ACM International Computing Education Research (ICER) Conference aims to gather high-quality contributions to the computing education research discipline. We invite submissions across a variety of categories for research investigating how people of all ages come to understand computational processes and devices, and empirical evaluation of approaches to improve that understanding in formal and informal learning environments.

Research areas of particular interest include:

• discipline based education research (DBER) in computer science (CS), information sciences (IS), and related disciplines
• learnability/usability of programming languages and the psychology of programming
• pedagogical environments fostering computational thinking
• design-based research, learner-centered design, and evaluation of educational technology supporting computing knowledge development
• learning sciences work in the computing content domain
• learning analytics and educational data mining in CS/IS content areas
• informal learning experiences related to programming and software development (all ages), ranging from after-school programs for children, to end-user development communities, to workplace training of computing professionals
• measurement instrument development and validation (e.g., concept inventories, attitudes scales, etc) for use in computing disciplines
• research on CS/computing teacher thinking and professional development models at all levels

In addition to standard research paper contributions, we continue our longstanding commitment to fostering discussion and exploring new research areas by offering several ways to engage. These include a doctoral consortium for graduate students just prior to the conference, a work-in-progress workshop for researchers following the conference, and poster and lightning talks. This is in addition to the format of conference sessions, where all research paper presentations include time for discussion among the attendees followed by feedback to the paper presenters.

Submission Categories

ICER provides multiple options for participation, with various levels of discussion and interaction between the presenter and audience. These sessions also support work at various levels, ranging from formative work to polished, complete research results.

Research Papers

8 page limit (plus up to 2 additional pages for references), double-blind peer reviewed and published in the ACM digital library as part of the conference proceedings. Accepted papers are allotted 30 minutes for presentation and discussion at the conference.

Doctoral Consortium

2 page extended abstract submission required and published in ACM digital library as part of the conference proceedings. Students will present their work to distinguished faculty mentors during an all-day workshop and during the conference in a dedicated poster session.

Lightning Talks and Posters

Abstract (300 words) submission required and made available on conference website, but not published in proceedings. Accepted abstracts for lightning talks will be given a 3-minute time slot for rapid presentation at the conference followed by a discussion period for all attendees. Posters may either accompany a lightning talk or may be proposed separately using the same abstract submission process.

Work in Progress Workshop

This one-day workshop is a venue to get sustained engagement with and feedback about early work in computing education. White paper submission required but not included in proceedings.

Co-located Workshops

Proposals for pre/post conference workshops of interest to the ICER community (ie, those that aim to advance computer science education research) are welcomed and encouraged. ICER local arrangements personnel will be available to assist with workshop logistics where possible. If interested, contact the conference chairs for more details by April 22nd 2016:judy.sheard@monash.edu

For more information about preparation and submission, please visit the page corresponding to the submission type of interest.

Important Deadlines and Dates

Research Papers
Abstract submission (mandatory)	Friday, April 15, 2016 at 11:59pm US Pacific Time
Full paper submission	Friday, April 22, 2016 at 11:59pm US Pacific Time
Notification of acceptance	Friday, June 3, 2016
Final camera ready deadline	Friday, June 17, 2016

Other Submission Types
Doctoral consortium submissions	Friday, May 20, 2016
Lightning talk and Poster proposals	Friday, June 17, 2016
Work in progress workshop application	Friday, June 17, 2016

Conference Schedule
Doctoral Consortium	Thursday, September 8, 2016
ICER Conference	Friday, September 9 – midday Sunday September 11, 2016
Work in Progress Workshop	Sunday September 11 – midday Monday September 12, 2016

More details can be found at the specific pages, linked above.

Steps to Help Foster a Preschooler’s Spatial Reasoning Skills: And Computer Science students, too?

I am a fan of the work at the Spatial Intelligence and Learning Center (see web page here). They’re in the final phases of the Center and are starting to publish wrap-up papers. Since spatial intelligence is likely predictive of success in computing (see paper from last year’s ICER), these are important ideas for us to think about in computing education, too.

Reading spatially challenging picture books is another way to engage children’s spatial thinking and expose them to spatial language.  Look for books that include pictures from various angles or perspectives, that contain maps and abundant spatial language, or whose illustration require close attention to decipher their meaning — such as wordless books. According to Newcombe, “Even though books only contain static pictures, they can help children understand spatial transformations, if adults read them with the children and stimulate their imagination.”

Source: Steps to Help Foster a Preschooler’s Spatial Reasoning Skills | MindShift | KQED News

Liberal arts colleges explore interdisciplinary pathways with CS: Great to see!

I’m excited by this initiative.  We need to see more CS + X kinds of programs.  Our Computational Media degree program has been a Computing + Digital Media program, and is wildly successful (see example post here).  The challenge is to engage faculty from across campus in the initiative.

Bates last September launched a similar project, called the Digital Course Design/Redesign Initiative, for faculty members interested in adding digital and computational tools or methods to existing courses. If it becomes popular among faculty members, the initiative could help realize Bates’s plans of having interdisciplinary pathways for its digital and computational studies majors. Auer, the Bates dean, acknowledged that building those pathways is “going to require deep consultation with the faculty” — as well as some new faculty members in other departments.

Source: Liberal arts colleges explore interdisciplinary pathways with computer science

We need to better justify CS for All

Brian Drayton has now written a couple of posts critical of the CS for All initiative (one is linked below, and here’s another one), and his points are well taken.  In my book on Learner-Centered Design of Computing Education, I consider several possible reasons for teaching CS to everyone.  I prefer the same ones that he does, and I agree that much of the initiative is poorly justified. I do not believe that we should put CS into all schools in order to make high school graduates “job-ready” (see the White House release using that phrase).

I agree that “everyone should code” is both unrealistic and poorly justified, as it has currently been advocated. I think we could make more progress (both in expanding people’s understanding of computer science or computation, and in empowering people to adopt such knowledge as a valuable tool for growth, creativity, and employment) if we did a better job envisioning what we’d like a classroom to look like that is deeply conversant with the tools and the insights of computer science in the same way that the classroom is already deeply infused with the tools and insights of literacy and numeracy.

Source: Topic: “Computing for all #2: Can we get off the pendulum?” – Topic Posts