No, Really – Programming is Hard and CS Flipped Classrooms are Complicated: ITICSE 2016 Award-Winning Papers
I only recently started digging into the papers from the ITICSE 2016 conference (see Table of Contents link at ACM Digital Library here). There were two papers that caught my attention.
First, the best paper award went to one of my former PhD students, Brian Dorn: An Empirical Analysis of Video Viewing Behaviors in Flipped CS1 Courses, by Suzanne L. Dazo, Nicholas R. Stepanek, Robert Fulkerson, and Brian Dorn. Brian has this cool piece of technology where students can view videos, annotate them, be challenged to answer questions from specific places, and have discussions. They used this for teaching a flipped CS1 class, where students were required to watch videos before class and then engage in more active learning opportunities in class. The real trick, as you might imagine and that the paper goes into detail on, is getting students to watch the video. I liked both the techniques for prodding students to watch videos and the fascinating results showing the relationship between watching the videos and learning.
ITICSE 2016 recognized two “commended” papers this year. I haven’t found the listing of which papers they were, but I did learn that one of them is Learning to Program is Easy by Andrew Luxton-Reilly. I enjoyed reading the paper and recommend it — even though I disagree with his conclusions, captured in the paper title. He does a good job of exploring the evidence that programming is hard (and even uses this blog as a foil, since I’ve claimed several times that programming is hard), and overall, is a terrific synthesis of a bunch of computing education papers (40 references is a lot for a six page ITICSE paper).
His argument that programming is easy has two parts:
- First, children do it. As he says in the abstract, “But learning to program is easy — so easy that children can do it.” That’s a false comparison — what children do in programming is not the same definition of “programming” that is in most of the literature that Andrew cites. The evidence that programming is hard is coming mostly from higher-ed CS classes. What is going on in introductory University CS classes and what children do is dramatically different. We saw that in the WIPSCE 2014 Fields and Kafai paper, and those results were recently replicated in a recent ICER 2016 paper. These are two different activities.
- Second, what higher-education CS teachers expect at the end of the first course is too much. He presents significant evidence that what CS teachers expect is achieved by students, but at the end of the second course. The paper from Morrison, Decker, and Margulieux supports the argument that students think and work very differently and much more successfully by the end of the second CS course than in the first course.
I see Andrew’s argument as evidence that programming is hard. The problem is that Andrew doesn’t define the target. What level of ability counts as “programming”? I believe that level of ability described by the McCracken Working Group, by the FCS1/SCS1 exams, and by most teachers as the outcomes from CS1 (these are all cited by Andrew’s paper) is the definition of the lowest level of “programming ability.” That it takes two courses to reach that level of ability is what I would call hard.
I’ve been reading a terrific book, Proust and the Squid: The Story and Science of the Reading Brain by Maryanne Wolf. It’s the story of how humans invented reading, how we teach reading, and how reading changes our brains (physically and in terms of cognitive function). Oral language is easy. We are literally wired for that. Reading is hard. We are not wired for that, and much of the invention of reading is about inventing how to teach reading. Unless you can teach reading to a significant part of your population, you don’t develop a literate culture, and your written language doesn’t succeed.
Much of the invention of written language is about making it easier to learn and teach because learning to read is so hard. Have you ever thought about why our Latin alphabet is ordered? Why do we talk about the “ABC”‘s and sing a song about them? We don’t actually need them to be ordered to read. Ordering the alphabet makes it easier to memorize, and learning to read is a lot about memorization, about drill-and-practice to make the translation of symbols to sounds to words to concepts effortless (or at least, all System 1 in Kahneman terms). This makes it easier, but the task of learning to read and write is still a cognitively complex task that takes a significant amount of time to master. It’s hard.
Programming is hard like written language is hard. It’s not possible to program unless you know how to read. Programming is particularly hard because the concepts that we’re mapping to are unfamiliar, are not part of our daily experience. We only see it as easy because we have expert blind-spot. We have already learned these concepts and made those mappings. We have constructed understandings of iteration and conditional execution and variable storage. It is difficult for experts to understand how hard it is to develop those concepts. The evidence of children programming suggests that most children who program don’t have those concepts.
I remain unconvinced by Andrew’s argument, but I recommend the paper for a great summary of literature and an interesting read.
Making learning effective, efficient, and engaging: An Interview With an Educational Realist and Grumpy Old Man, Paul Kirschner
I am a fan of Paul Kirschner‘s work. This interview is great with useful insights about education — deep and pragmatic thinking.
I want to fundamentally understand how people can learn in effective, efficient, and enjoyable ways, and how you can teach and design learning materials to achieve this objective. If a learner doesn’t enjoy the learning experience, even if it’s effective and/or efficient, they won’t do it. The same is true for teaching: that is it must also be effective, efficient, and enjoyable for the teacher because if a teacher doesn’t enjoy the teaching process, even if it’s effective and/or efficient, they won’t do it.
I have written about this Dagstuhl Seminar (see earlier post). The formal report is now available.
This seminar discussed educational outcomes for first-year (university-level) computer science. We explored which outcomes were widely shared across both countries and individual universities, best practices for assessing outcomes, and research projects that would significantly advance assessment of learning in computer science. We considered both technical and professional outcomes (some narrow and some broad) as well as how to create assessments that focused on individual learners. Several concrete research projects took shape during the seminar and are being pursued by some participants.
Probably my favorite session from the CRA Snowbird conference this last summer (see agenda with links to all talks) was a session on creating Colleges or Schools of Computer Science. Should we? Why?
The most compelling two talks in the session were from Randy Bryant and Rich LeBlanc, because they were so similar in structure. They both argued that you don’t make the argument for a high-level College or School of Computing because you’re big and important. You make it because you have a driving definition of computing that makes it unique.
- Randy told the story of how CMU’s School of Computer Science was driven by the original definition of computer science from Newell and Simon, and how that definition was broader than most people’s definition of CS today. I recently blogged on that definition.
- Rich told the story of how Georgia Tech’s College of Computing was driven by the ACM report The Future of Computing (led by Peter Denning) which showed how Computing crossed science, mathematics, and engineering. Of course, Rich’s story was particularly powerful for me because I lived that definition — that was the vision that drove the College of Computing when I first got here in 1993. Rich told the story of how that definition convinced faculty and administrators at Georgia Tech that Computing couldn’t be contained within the Colleges of Engineering or Science. It needed to be its own entity. (I may also be biased because Rich quoted me from this blog🙂
Many of the people in the audience wanted to know, “How can I turn my Department into a School or College?” One audience member said, “My CS department is the biggest one in the College of Engineering. How do I break apart into my own College.” All the panelists told him, “You can’t.” No Dean will allow its biggest department to leave — that would be crazy. Some participants (from U. Michigan and U. Washington, in particular) pointed out why they don’t have a College or School of Computing — they have successful multi-department collaborations that make it unnecessary. A new College or School is expensive. Don’t do it unless you have to.
Every University Student should Learn to Program: Guzdial Arguing for CS for All in Higher Education
A colleague recently approached me and said, “It would be useful if Universities got involved in this CS for All effort. All Universities should offer courses aimed at everyone on campus. There should be a systematic effort to get everyone to take those classes.”
I agree, and have been making this argument for several years now. I spent a few minutes gathering the papers, blog posts, and book where I’ve made that argument over the last decade and a bit.
In 2002, Elliot Soloway and I argued in CACM that we needed a new way to engage students in intro programming: Teaching the Nintendo Generation to Program.
In 2003, I published the first paper on Media Computation: A media computation course for non-majors.
In 2004, Andrea Forte led the team studying the Media Computation class at GT:Computers for communication, not calculation: Media as a motivation and context for learning and A CS1 course designed to address interests of women.
In 2005, Andrea Forte and I presented empirical evidence about the courses we’d designed for specific audiences: Motivation and nonmajors in computer science: identifying discrete audiences for introductory courses. I published a paper in CACM about how the courses came to be at Georgia Tech: Teaching computing to everyone.
In 2008, I offered the historical argument for teaching everyone to program: Paving the Way for Computational Thinking.
We’ve published several papers about our design process: Imagineering inauthentic legitimate peripheral participation: an instructional design approach for motivating computing education and Design process for a non-majors computing course.
My 2013 ICER paper was a review of a decade’s worth of research on Media Computation: Exploring hypotheses about media computation
My keynote at VL/HCC 2015 was on how computing for all is a requirement for modern society: Requirements for a computing-literate society
My 2015 book is, to a great extent: an exploration of how to achieve CS for All: Learner-Centered Design of Computing Education: Research on Computing for Everyone.
In blog posts, it’s been a frequent topic of conversation:
- In 2011, I argued that it makes more sense to require CS at universities before pushing into K-12, because then all pre-service teachers have some CS which makes later PD much easier and cheaper: https://computinged.wordpress.com/2015/11/30/require-cs-at-universities-before-k-12-computational-community-for-everyone/ and https://computinged.wordpress.com/2011/05/17/if-you-want-cs-in-high-school-require-cs-in-college/
- In 2013, I pointed out that CS is becoming increasingly valuable outside of CS: https://computinged.wordpress.com/2013/12/10/why-are-english-and-lots-of-other-majors-studying-computer-science/
- One of my earlier Blog@CACM posts was on how students learn things in MediaComp that informs them about their world, not just about CS: http://cacm.acm.org/blogs/blog-cacm/26343-media-computation-for-creativity-and-surprises/fulltext
- On how CS is a value-added to a liberal education: http://cacm.acm.org/blogs/blog-cacm/101738-computer-science-as-value-added-to-a-liberal-education/fulltext
I don’t know how to convince University CS departments to do just about anything, but here are my contributions to the dialogs that I hope are happening at Colleges and Universities worldwide about how to prepare students to engage in computational literacy.
Learning Curves, Given vs Generated Subgoal Labels, Replicating a US study in India, and Frames vs Text: More ICER 2016 Trip Reports
My Blog@CACM post for this month is a trip report on ICER 2016. I recommend Andy Ko’s excellent ICER 2016 trip report for another take on the conference. You can also see the Twitter live feed with hashtag #ICER2016.
I write in the Blog@CACM post about three papers (and reference two others), but I could easily write reports on a dozen more. The findings were that interesting and that well done. I’m going to give four more mini-summaries here, where the results are more confusing or surprising than those I included in the CACM Blog post.
This year was the first time we had a neck-and-neck race for the attendee-selected award, the “John Henry” award. The runner-up was Learning Curve Analysis for Programming: Which Concepts do Students Struggle With? by Kelly Rivers, Erik Harpstead, and Ken Koedinger. Tutoring systems can be used to track errors on knowledge concepts over multiple practice problems. Tutoring systems developers can show these lovely decreasing error curves as students get more practice, which clearly demonstrate learning. Kelly wanted to see if she could do that with open editing of code, not in a tutoring system. She tried to use AST graphs as a sense of programming “concepts,” and measure errors in use of the various constructs. It didn’t work, as Kelly explains in her paper. It was a nice example of an interesting and promising idea that didn’t pan out, but with careful explanation for the next try.
I mentioned in this blog previously that Briana Morrison and Lauren Margulieux had a replication study (see paper here), written with Adrienne Decker using participants from Adrienne’s institution. I hadn’t read the paper when I wrote that first blog post, and I was amazed by their results. Recall that they had this unexpected result where changing contexts for subgoal labeling worked better (i.e., led to better performance) for students than keeping students in the same context. The weird contextual-transfer problems that they’d seen previously went away in the second (follow-on) CS class — see below snap from their slides. The weird result was replicated in the first class at this new institution, so we know it’s not just one strange student population, and now we know that it’s a novice problem. That’s fascinating, but still doesn’t really explain why. Even more interesting was that when the context transfer issues go away, students did better when they were given subgoal labels than when they generated them. That’s not what happens in other fields. Why is CS different? It’s such an interesting trail that they’re exploring!
Mike Hewner and Shitanshu Mishra replicated Mike’s dissertation study about how students choose CS as a major, but in Indian institutions rather than in US institutions: When Everyone Knows CS is the Best Major: Decisions about CS in an Indian context. The results that came out of the Grounded Theory analysis were quite different! Mike had found that US students use enjoyment as a proxy for ability — “If I like CS, I must be good at it, so I’ll major in that.” But Indian students already thought CS was the best major. The social pressures were completely different. So, Indian students chose CS — if they had no other plans. CS was the default behavior.
One of the more surprising results was from Thomas W. Price, Neil C.C. Brown, Dragan Lipovac, Tiffany Barnes, and Michael Kölling, Evaluation of a Frame-based Programming Editor. They asked a group of middle school students in a short laboratory study (not the most optimal choice, but an acceptable starting place) to program in Java or in Stride, the new frame-based language and editing environment from the BlueJ/Greenfoot team. They found no statistically significant differences between the two different languages, in terms of number of objectives completed, student frustration/satisfaction, or amount of time spent on the tasks. Yes, Java students got more syntax errors, but it didn’t seem to have a significant impact on performance or satisfaction. I found that totally unexpected. This is a result that cries out for more exploration and explanation.
There’s a lot more I could say, from Colleen Lewis’s terrific ideas to reduce the impact of CS stereotypes to a promising new method of expert heuristic evaluation of cognitive load. I recommend reviewing the papers while they’re still free to download.
Ruthe Farmer at the Office of Science and Technology Policy (OSTP) has been working furiously towards today’s announcements. The Obama Administration is aiming to achieve the goal of CSforAll, and with only a few months left before the new Administration takes off, they’re showing what they’ve put in place today. The full details on all the announcements are here. There’s a webcast at 1 pm EDT today here. The biggest deal to me is the establishment of the CSforAll Consortium (see website here) which is meant to carry on the initiative, no matter who wins in November.
To mark this progress, and celebrate new commitments in support of the President’s initiative, the White House is hosting a summit on Computer Science for All. Key announcements being made today include:
More than $25 million in new grants awarded from the National Science Foundation (NSF) to expand CS education;
A new CSforAll Consortium of more than 180 organizations, which will connect stakeholders with curriculum and resources, as well as track progress towards the goal of Computer Science for All; and
New commitments from more than 200 organizations, ranging from expanded CS offerings within the Girl Scouts of the USA that could reach 1.4 million girls per year, to Code.org supporting professional development for 40,000 additional teachers, to new collaborations to bring CS to students in a variety of settings from African-American churches to family coding nights to tribal Head Start programs to students as Chief Science Officers.