Posts tagged ‘ICER’

Award-winning papers at ICER 2020 explore new directions and point towards the next work to do

The 2020 ACM SIGCSE International Computing Education Research Conference was in August (see website here), hosted in Dunedin, New Zealand — but was unfortunately entirely virtual. I became so much more aware of the affordances of face-to-face conferences when attending one of my favorite conferences all through my screen. The upside of the all-virtual format is that all the talks are available on YouTube (see ICER 2020 channel here). Here are my comments on the three papers receiving awards — see them listed here.

What Do We Think We Think We Are Doing?: Metacognition and Self-Regulation in Programming. (Paper link)

This is the paper that I have read and re-read the most since the conference. The authors review what the literature tells us about metacognition in programming. Metacognition is thinking about thinking, like “Did I really understand that? Maybe I should re-read this. Or maybe I should write down my thoughts so I can reflect on them I’m not sure that I’m making progress here. Taking a walk would probably help me clear my head and focus.”

One of their findings that is most intriguing is “Metacognitive knowledge is difficult to achieve in domains about which the learner has little content knowledge.” In other words, you can’t teach students metacognition and self-regulation first, and then teach them something using those new thinking skills. Learners have to know some of the domain first. Now why is that?

Here’s a hypothesis: Metacognition and self-regulation are hard. They take a lot of cognitive load. You have to pay attention to things that are invisible (your own memory and thoughts) and that’s hard. Trying to learn or problem-solve at the same time that you’re monitoring yourself and thinking about your own learning — super hard. Maybe you have to know enough about the domain for some of that activity to be automatized, so that you don’t have to pay as much attention to it in order to do it.

So the biggest hole I see in this paper (which given that it’s a review paper, probably means that the hole is in the literature) is that it does not consider enough factors like gender, race, disability, or SES (e.g., wealth). (Gender gets mentioned when reporting Alex Lishinski’s great work, but only with respect to self-efficacy.) My hypothesis is that the story is more complicated when you consider non-dominant groups. If you don’t think you belong, that takes more of your attention, which takes attention away from your learning — and leaves even less attention for metacognition and self-regulation. If you’re worrying about your screen reader working or where you’re going to get dinner tonight, how do you also have attention left over for monitoring your learning?

The biggest unique opportunity I see in thinking about metacognition and programming is in thinking about debugging. Like psychology or veterinarian science, but unlike most other fields, a lot of a computer scientists’ job is in understanding the “thinking” (behavior, processing, whatever) of another agent. When you’re debugging your program, isn’t that a kind of metacognition. “Okay, what is the computer doing here? How is it interpreting what I wrote? Oh wait, is that what I wanted to write? Is that what I wanted to happen?” The complexity of mapping your thoughts and intentions to what you wrote to what the computer did is enormous. Now, debug someone else’s code — you’ve got what you want in mind, you’re constructing a model of mind of whoever wrote the code before you (did they know what they were doing? is this code brilliant or broken?), and you’re trying to figure out how the computational agent is “thinking about” the code. There’s some seriously complex metacognition going on there.

Exploring Student Behavior Using the TIPP&SEE Learning Strategy. (See paper here.)

No surprise that Diana Franklin’s CANON Lab at U. Chicago continues to do terrific and award-winning work. I’m excited about the TIPP&SEE learning strategy. A commonly found problem in computer science education is that students are bad at Explain In Plain English (EIPE) problems (e.g., see this SIGCSE 2012 paper on the topic). EIPE problems are a measure of students being able to step back from the structure and behavior of code to describe its function or purpose. Katie Cunningham has been exploring how some students focus more on the purpose of the programming, and others get stuck in the code and can’t see the purpose of the program. The TIPP&SEE learning strategy explicitly addresses these problems. Students are guided through how to understand a programming project and relating code to purpose.

This award-winning paper (which follows on their SIGCSE 2020 paper) shows us that students using the TIPP&SEE approach perform better than students who don’t. They get more of their programs done. The SIGCSE 2020 paper shows that they learn more.

The papers totally convince me that this strategy works. The next question is that I want to know is how and why. The SIGCSE paper does some qualitative work, but it’s pretty big n — 184 students. With this kind of scale, the programs are given and the problems are given. There’s not as much opportunity for the detailed cognitive interviews to figure out how the students are thinking about interpreting programs. What happens when these students just go to the Scratch website to look at something that they want to reuse? Do they use TIPP&SEE? Do they understand the programs that they just happen to come across? What happens when they want to build something, where they provide their purpose? Can they draw on TIPP&SEE and succeed? This is not a critique of the papers — they’re great and make real contributions. I’m thinking about what I want to know next.

Hedy: A Gradual Language for Programming Education. (See program link here.)

Easily my favorite paper at ICER 2020 this year. Felienne is doing what I am trying to do. Let’s invent new more usable programming languages! I am happy that she got this paper published, because (selfishly) it gives me hope that I can get my new work published. I am thrilled that the ICER community valued this paper so highly that it received a John Henry award.

The basic idea is to create a sequence of programming languages, where advancing levels have most of the elements of the previous level but include new elements. Her earliest level has no punctuation — no quotes, no semi-colons, no curly-braces. I recently built a task-specific programming language that had the same attribute, and one of the students I’m working with looked at it and asked, “Wait — you can have programming languages without all that punctuation? Well, then, why do we have so much when it scares people off?” Great question! When do we need all that extra punctuation, and where can we avoid it?

The next stage is to explore how we design languages like these. (I’m biased since this is where I’m spending most of my research time on these days.) Why do we choose those language features? Why the keywords print, ask, echo, assign, if, else, and repeat? How do we design and iteratively develop the language? How do we know that people can do things that they want to do with this language? My answer to this is participatory design with teachers, but there are many other viable answers. Felienne provides good design rationale for Hedy’s language features, based in literature from computing education and natural language acquisition. In a process of user experience (UX) design, we’d also user iterative development including testing with real users. This paper shows us use at large scale, and a big chunk of her paper describes what people did with it. It’s fascinating work, but we don’t talk to any of them. We don’t know what they liked, what they disliked, what they found frustrating, and what they were able to do. We need to move programming language design closer to user experience design — UX for PX.

All three papers are terrific contributions to the research community, and I plan to cite and built on them all. I’m eager to see what comes next!


Sidebar: I am a member of the ICER Steering Committee (which has no role in reviewing papers or in picking awards), and I was a metareviewer for ICER 2020. I am speaking here just for myself as a reader and attendee.

September 28, 2020 at 7:00 am 2 comments

Why some students do not feel that they belong in CS, and how we can encourage the sense that they do belong

One of my favorite papers at ICER 2019 was by Colleen Lewis and her colleagues, and is available on her website. I’ll quote her first:

Does a match between a students’ values of helping society and their perception of computing matter? Yes! A mismatch between a students’ goals of helping society and their perception of computing predicts a lower sense of belonging. And students from groups who – on average – are more likely to want to help society (women, Black students, Latinx students, and first-generation college students), this may be particularly problematic! (pdf)

  • Lewis, C. M., Bruno, P., Raygoza, J., & Wang, J. (2019). Alignment of Goals and Perceptions of Computing Predicts Students’ Sense of Belonging in Computing.Proceedings of the International Computer Science Education Research Workshop. Toronto, Canada.

I want to expand a bit on that paragraph. I often get the question, “Why aren’t more women and URM students going into CS?” We’re seeing female students and students of color leaving/avoiding CS at many stages, e.g., Barb’s deep analysis of AP CS*. Colleen and her collaborators are giving us one answer.

We know that students who have a sense of belonging in computing are more likely to stay in computing. Colleen et al. found that students who found that their values were supported in computing were more likely to feel a sense of belonging. So, if what you want to do with your life matches computing, you’re more likely to stick around in computing. This is the “alignment of goals” and “perceptions of computing” part of the title.

Next step: Students from demographic groups underrepresented in computing were more likely to value community and helping society than other students. These are their goals. Do students see that their goals align with their perception of computing? If so, then you have an increased sense of belonging. Colleen and her colleagues found that If the students who valued community perceived that they could use computing to support communal values, then they were more likely to stick around.

This result is obviously explanatory. It helps us to understand who stays in computing. It also suggests interventions. Want to retain more under-represented students in your CS classes? Help them to see that they can pursue their values in computing. Help them to update their perceptions so that they see the alignment of their goals with computing goals.

But what if you (as the teacher) don’t? This paper suggests future research questions. What if your CS class is entirely de-contextualized and doesn’t say anything about what the students might do with computing? What perceptions do the students bring to the CS class if nobody helps them to see the possibilities in computing? Which student goals align with these perceived goals of computing? We might guess what the answers might be, but it really does call for some explicit research. What are students’ goals and perceptions of computing in most CS classes today?


* Check out Barb’s blog at https://cs4all.home.blog/. As I’m writing this, Barb is finishing up the 2019 AP analysis. The gap between white and Black student pass rates on AP CSP is enormous, far larger than the gap on AP CS A. I’m hoping that she has updates there by the time this post appears.

December 9, 2019 at 7:00 am 2 comments

Social studies teachers programming, when high schools choose to teach CS, and new models of cognition and intelligence in programming: An ICER 2019 Preview

My group will be presenting two posters at ICER this year.

  • Bahare Naimipour (Engineering Education Research PhD student at U-Michigan) will be presenting our participatory design session with social studies educators, Helping Social Studies Teachers to Design Learning Experiences Around Data–Participatory design for new teacher-centric programming languages. We had 18 history and economics teachers building data visualizations in either Vega-Lite or JavaScript with Google Charts. Everyone got the starter visualization running and made changes that they wanted in less than 20 minutes. Those who started in Vega-Lite also tried out the JavaScript code, but only about 1/4 of the JS groups moved to Vega-Lite successfully.
  • Miranda Parker (Human-Centered Computing PhD student at Georgia Tech) will be presenting her quantitative model explaining about half of the variance in whether Georgia high schools taught CS in 2016, A Statewide Quantitative Analysis of Computer Science: What Predicts CS in Georgia Public High School. The most important factor was whether the school taught CS the year before, suggesting that overcoming inertia is a big deal — it’s easier to sustain a CS program than start one. She may talk a little about her new qualitative work, where she’s studying four schools as case studies about their factors in choosing to teach CS, or not.

Barbara is co-author on a paper, A Spaced, Interleaved Retrieval Practice Tool that is Motivating and Effective, with Iman Yeckehzaare and Paul Resnick . This is about a spaced practice tool that 32% of the students in an introductory programming course used more than they needed to, and the number of hours of use had a measurable positive effect on the final exam grade.

All of our other papers were rejected this year, but we’re in good company — the accept rate was around 18%. But I do want to talk about a set of papers that will be presented by others at ICER 2019. These are papers that I heard about, then I asked the authors for copies. I’m excited about all three of them.

How Do Students Talk About Intelligence? An Investigation of Motivation, Self-efficacy, and Mindsets in Computer Science by Jamie Gorson and Eleanor O’Rourke (see released version of the paper here)

One of the persistent questions in computing education research is why growth mindset interventions are not always effective (see blog post here). We get hard-to-interpret results. I met Jamie and Nell at the Northwestern Symposium on Computer Science and the Learning Sciences in April (amazing event, see here for more details). Nell worked with Carol Dweck during her graduate studies.

Jamie and Nell found mixed mindsets among the CS students that they studied. Some of the students they studied had growth mindsets about intelligence, but their talk about programming practices showed more fixed mindset characteristics. Other students self-identified as having some of both growth and fixed mindset beliefs.

In particular, some students talked about intelligence in CS in ways that are unproductive when it came to the practice of programming. For example, some students talked about the best programmers as being able to write the whole code in one sitting, or never getting any errors. A more growth mindset approach to programming would be evidenced by talking about building programs in pieces, expecting errors, and improving through effort over time.

This is a really helpful finding. It gives us new hypotheses to explore about why growth mindset interventions haven’t been as successful in CS as in other disciplines. Few disciplines have this strong distinction between their knowledge and their practice as acutely as we do in CS. It’s no wonder that we see these mixed mindsets.

Toward Context-Dependent Models of Productive Knowledge in Programming Cognition, by Brian A. Danielak

I’ve known Brian since he was a PhD student, and have been hoping that he’d start to publish some of his dissertation work. I got to read one chapter of it, and found it amazingly insightful. Brian explained how what we might see as a “random walk” of syntax was actually purposeful and rational behavior. I was excited to hear about this paper, and I enjoyed reading it.

It’s such an unusual paper for ICER! It’s empirical, but has no methods section. A big part of it is connecting to prior literature, but it’s not about a formal literature review.

Brian is making an argument about how we characterize knowledge and student success in CS. He points out that we often talk about students being wrong and having misconceptions, which is less productive than figuring out what they understand and where their alternative conceptions work or fail. I see his work following on to the work of Rich et al. (mentioned in this blog post) on CS learning trajectories. There are so many things to learn in CS, and sometimes, just getting started on the trajectory is a big step.

Spatial Encoding Strategy Theory: The Relationship between Spatial Skill and STEM Achievement by Lauren Margulieux.

Lauren is doing some impressive theoretical work here. She’s considering the work exploring the relationship between spatial reasoning and CS learning/performance, then constructs a theory explaining the observed results. Since it’s Lauren, the theory is thorough and covers well the known results in this space. I wrote her that I didn’t think that theory explains things that we expect are related to spatial reasoning, but we don’t yet have empirical evidence to support it. For example, when programmers simulate a program in their mind, their mental models may have a spatial component to them, but I don’t know of empirical work that explores that dimension of CS performance. But again, since it’s Lauren, I wouldn’t be surprised if her presentation addresses this point, beyond what was in the paper. (Also, read Lauren’s own summary of the paper here.)

I am looking forward to the discussion of these papers at ICER!

August 12, 2019 at 7:00 am 1 comment

Come hang out with Wil and me to talk about new research ideas! ACM ICER 2019 Work in Progress Workshop

Wil Doane and I are co-hosting the ACM ICER 2019 Work in Progress workshop that Colleen Lewis introduced at ICER 2014 in Glasgow (my report on participating). Colleen and I co-hosted last year.

It really is a “hosting” job more than an “organizing” or “presenting” role.  I love Colleen’s informal description of WiP, “You’re borrowing 4 other smart people’s brains for an hour. Then you loan them yours.”  The participants do the presenting. For one hour, your group listens to your idea and helps you think through it, and then you pass the baton. The whole organizing task is “Let’s put these 4 people together, and those 4 people together, and so on. We give them 4 hours, and appropriate coffee/lunch breaks.” (Where the value “4” may be replaced with “5” or “6”.)

Another useful description of WiP is “doctoral consortia for after-graduation.”  Doctoral consortia are these great opportunities to share your research ideas and get feedback on them.  Then there’s this sense that you graduate and…not have those ideas anymore? Or don’t need to share them or get feedback on them?  I’ve expressed concern previously about the challenges of learning when you’re no longer seen as a learner. Of course, PhD graduates are supposed to have new research ideas, which go into proposals and papers. But how do you develop ideas when you’re at the early stages, when they’re not ready for proposals or papers?  That’s what the WiP is about.

The WiP page is here (and quoted in part below). To sign up, you just fill out this form, and later give us a drafty concept paper to share with your group.

The WIP Workshop (formerly named the Critical Research Review) is a dedicated 1-day workshop for ICER attendees to provide and receive friendly, constructive feedback on works-in-progress. To apply for the workshop you will specify a likely topic about which you’ll request feedback. WIP participants will be assigned to thematic groups with 4-6 participants.

Two weeks before ICER, participants will submit to the members of their group a 2-4 page primer document to help prepare for the session and identify the types of feedback sought. At WIP, depending upon group size, each participant will have 45-75 minutes to provide context, elicit advice, support, feedback, and critique. Typically, one of the other group members acts as a notetaker during an individual’s time in order to allow the presenter to engage fully in the discussion.

WIP may be the right experience for you, if you would like to provide and receive constructive advice, support, feedback, or critique on computing education research issues such as:

  • A kernel of a research idea
  • A grant proposal
  • A rejected ICER paper
  • A study design
  • A qualitative analysis approach
  • A quantitative analysis approach
  • A motivation for a research project
  • A theoretical framing
  • A challenge in a research project

The goal of the workshop is to provide a space where we can receive support and provide support. The workshop is intended for active CS education researchers. PhD students are instead encouraged to apply for the Doctoral Consortium, held on the same day as WIP.

May 31, 2019 at 7:00 am Leave a comment

Growth mindset matters for individual human performance, with a more indirect connection to academic success

One of the most talked-about papers at ICER 2018 was this one, “Fixed versus Growth Mindset Does not Seem to Matter Much: A Prospective Observational Study in Two Late Bachelor level Computer Science Courses.” The claim was that fixed and growth mindset did not have much impact on student course performance.  One of the authors wrote a blog post summarizing the paper.
In my opinion, they got growth/fixed mindset theory wrong.  The mistake is in the first line of the abstract, “Psychology predicts that a student’s mindset—their implicit theory of intelligence—has an effect on their academic performance.”  Growth and fixed mindset have an effect on individual student development. There is an indirect effect on academic performance which is more complex. Grades are not the same as measuring learning. Grades are typically a measure of mastery of concepts.
The presentation of the paper had this amazing graph (picture I took below).  Most students fail in the courses they studied.  Look at the big peaks in the distribution on the left. Those are all the fails.
IMG_0863
In Freakonomics, there’s a chapter on why, if drug dealers make so much money, why do so many of them live with their mothers?  (The chapter is reprinted here.) The answer is that drug dealing (like professional sports or acting) is a “lottery” — many people try and make no money, and very few people get to the top and make lots of money.  All those high school and college football players who are waking up early to pump weights have a growth mindset — they believe that their effort will take them to the NFL.  However, the vast majority are *wrong*. They won’t make it.  There is no apparent connection between growth mindset and success.
That’s how I saw the ICER paper on fixed and growth mindset.  If the outcome variable is academic success, growth mindset isn’t going to always pay-off. Sometimes the deck is stacked against you, and even if you think you can win, you won’t.
However, if the outcome variable is individual development, growth mindset will likely beat fixed mindset.  If you believe you can get better, you might. If you don’t believe you can get better, you won’t. A good outcome variable would be learning gain, measured pre-test to post-test.  In this study, most students had a growth mindset, so they probably wouldn’t have seen much variation (between growth and fixed) even if they measured learning.
The students thought if they worked harder, they could do better. And they probably did all do better (from a learning perspective). They just weren’t going to win in this lottery.
It’s a different question whether a given intervention to improve mindset might lead to improved academic performance.  If you improve learning, and academic performance is reflective of learning, then there should be a connection IF it’s possible to change mindset with an intervention. Duckworth and Dweck have shown that they have successfully intervened to change students’ mindset and consequently improve academic performance, and that work was recently replicated (see post here).  The efforts to intervene on mindset in CS have had mixed success (see my blog post here on that). But it’s one thing to say that fixed vs growth mindset does not seem to matter much (the title of the paper presented at ICER), and another to say that a given mindset intervention did not result in academic performance increase. The first claim is about theory, and the second is about designing interventions with a multi-step causal chain. I don’t buy the former claim, but completely agree that the latter is a complex and interesting issue to explore.

September 7, 2018 at 7:00 am 5 comments

Adaptive Parsons problems, and the role of SES and Gesture in learning computing: ICER 2018 Preview

 

Next week is the 2018 International Computing Education Research Conference in Espoo, Finland. The proceedings are (as of this writing) available here: https://dl.acm.org/citation.cfm?id=3230977. Our group has three papers in the 28 accepted this year.

“Evaluating the efficiency and effectiveness of adaptive Parsons problems” by Barbara Ericson, Jim Foley, and Jochen (“Jeff”) Rick

These are the final studies from Barb Ericson’s dissertation (I blogged about her defense here). In her experiment, she compared four conditions: Students learning through writing code, through fixing code, through solving Parsons problems, and through solving her new adaptive Parsons problems. She had a control group this time (different from her Koli Calling paper) that did turtle graphics between the pre-test and post-test, so that she could be sure that there wasn’t just a testing effect of pre-test followed by a post-test. The bottom line was basically what she predicted: Learning did occur, with no significant difference between treatment groups, but the Parsons problems groups took less time. Our ebooks now include some of her adaptive Parsons problems, so she can compare performance across many students on adaptive and non-adaptive forms of the same problem. She finds that students solve the problems more and with fewer trials on the adaptive problems. So, adaptive Parsons problems lead to the same amount of learning, in less time, with fewer failures. (Failures matter, since self-efficacy is a big deal in computer science education.)

“Socioeconomic status and Computer science achievement: Spatial ability as a mediating variable in a novel model of understanding” by Miranda Parker, Amber Solomon, Brianna Pritchett, David Illingworth, Lauren Margulieux, and Mark Guzdial

(Link to last version I reviewed.)

This study is a response to the paper Steve Cooper presented at ICER 2015 (see blog post here), where they found that spatial reasoning training erased performance differences between higher and lower socioeconomic status (SES) students, while the comparison class had higher-SES students performing better than lower-SES students. Miranda and Amber wanted to test this relationship at a larger scale.

Why should wealthier students do better in CS? The most common reason I’ve heard is that wealthier students have more opportunities to study CS — they have greater access. Sometimes that’s called preparatory privilege.

Miranda and Amber and their team wanted to test whether access is really the right intermediate variable. They gave students at two different Universities four tests:

  • Part of Miranda’s SCS1 to measure performance in CS.
  • A standardized test of SES.
  • A test of spatial reasoning.
  • A survey about the amount of access they had to CS education, e.g., formal classes, code clubs, summer camps, etc.

David and Lauren did the factor analysis and structural equation modeling to compare two hypotheses: Does higher SES lead to greater access which leads to greater success in CS, or does higher SES lead to higher spatial reasoning which leads to greater success in CS? Neither hypothesis accounted for a significant amount of the differences in CS performance, but the spatial reasoning model did better than the access model.

There are some significant limitations of this study. The biggest is that they gathered data at universities. A lot of SES variance just disappears when you look at college students — they tend to be wealthier than average.

Still, the result is important for challenging the prevailing assumption about why wealthier kids do better in CS. More, spatial reasoning is an interesting variable because it’s rather inexpensively taught. It’s expensive to prepare CS teachers and get them into all schools. Steve showed that we can teach spatial reasoning within an existing CS class and reduce SES differences.

“Applying a Gesture Taxonomy to Introductory Computing Concepts” by Amber Solomon, Betsy DiSalvo, Mark Guzdial, and Ben Shapiro

(Link to last version I saw.)

We were a bit surprised (quite pleasantly!) that this paper got into ICER. I love the paper, but it’s different from most ICER papers.

Amber is interested in the role that gestures play in teaching CS. She started this paper from a taxonomy of gestures seen in other STEM classes. She observed a CS classroom and used her observations to provide concrete examples of the gestures seen in other kinds of classes. This isn’t a report of empirical findings. This is a report of using a lens borrowed from another field to look at CS learning and teaching in a new way.

My favorite part of of this paper is when Amber points out what parts of CS gestures don’t really fit in the taxonomy. It’s one thing to point to lines of code – that’s relatively concrete. It’s another thing to “point” to reference data, e.g., when explaining a sort and you gesture at the two elements you’re comparing or swapping. What exactly/concretely are we pointing at? Arrays are neither horizontal nor vertical — that distinction doesn’t really exist in memory. Arrays have no physical representation, but we act (usually) as if they’re laid out horizontally in front of us. What assumptions are we making in order to use gestures in our teaching? And what if students don’t share in those assumptions?

August 10, 2018 at 7:00 am 6 comments

A Place to Get Feedback and Develop New Ideas: WIPW at ICER 2018

Everybody’s got an idea that they’re sure is great, or could be great with just a bit of development. Similarly, everyone has hit a tricky crossroads in their research and could use a little nudge to get unstuck. The ICER Work in Progress workshop is the place to get feedback and help on that idea, and give feedback and help to others on their cool ideas. I did it a few years ago at the Glasgow ICER and had a wonderful day. You learn a lot, and you get a bunch of new insights about your own idea. As Workshop Leader (and the inventor of the ICER Work in Progress workshop series) Colleen Lewis put it, “You get the chance to borrow the brains of some really awesome people to work on your problem.”

Colleen is the Senior Chair again this year, and I’m the Junior Chair-in-Training.

The workshop is only one day and super-fun. If you’re attending ICER this year, please apply for the Work in Progress workshop! https://icer.hosting.acm.org/icer-2018/work-in-progress/ The application is due June 8 (it’s just a quick Google form).

Let Colleen or me know if you have questions!

May 30, 2018 at 7:00 am 2 comments

ICER 2018 Call for Participation (I’m co-chairing Works in Progress)

Do submit to ICER 2018 in Finland.  I particularly encourage you to join the Works in Progress workshop, for which I’ll be the junior co-chair as I learn the ropes from Colleen Lewis. I was a participant in the Works in Progress workshop in Glasgow and found it fun and useful.

ICER’18 – Call For Participation

The fourteenth 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
– design-based research, learner-centered design, and evaluation of educational technology supporting computing knowledge or skills development
pedagogical environments fostering computational thinking
learning sciences work in the computing content domain
psychology of programming
learning analytics and educational data mining in CS/IS content areas
learnability/usability of programming languages
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
rigorous replication of empirical work to compare with or extend previous empirical research results
systematic literature review on some topic related to computer science education


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
Papers are limited to 8 pages, excluding references, double-blind peer reviewed and published in the ACM digital library as part of the conference proceedings. Accepted papers are allotted time 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 (250 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 (i.e., 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 10th, 2018: Lauri.Malmi@aalto.fi or Ari.Korhonen@aalto.fi.


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

Important Deadlines and Dates


Research Papers

30 March, 2018 – – Abstract submission (250 words, mandator)
6 April, 2018 – – Full paper submission 
1 June – – Notification of acceptance 
15 June – -Final camera ready deadline
Other Submission Types
1 May – – Doctoral consortium submissions
8 June – – Lightning talk and Poster proposals
8 June – – Work in progress workshop application

Conference Schedule

Doctoral Consortium, Sunday, August 12, 2018
ICER Conference, Monday, August 13 – Wednesday August 15, 2018
Work in Progress Workshop, Wednesday evening, August 15 – Thursday, August 16, 2018
For more details, see the conference website:
 http://www.icer-conference.org

Conference Co-Chairs
Lauri Malmi, Aalto University, Finland (Lauri.Malmi@aalto.fi)
Ari Korhonen, Aalto University, Finland (Ari.Korhonen@aalto.fi
Robert McCartney, University of Connecticut, USA (robert.mccartney@uconn.edu)
Andrew Petersen, University of Toronto Mississauga, Canada (andrew.petersen@utoronto.ca)


AUTHORS TAKE NOTE: The official publication date is the date the proceedings are made available in the ACM Digital Library. This date will be up to two weeks prior to the first day of the conference. The official publication date affects the deadline for any patent filings related to published work.

January 15, 2018 at 7:30 am Leave a comment

Teachers are not the same as students, and the role of tracing: ICER 2017 Preview

The International Computing Education Research conference starts today at the University of Washington in Tacoma. You can find the conference schedule here, and all the proceedings in the ACM Digital Library here. In past years, all the papers have been free for the first couple weeks after the conference, so grab them while they are outside the paywall.

Yesterday was the Doctoral Consortium, which had a significant Georgia Tech presence. My colleague Betsy DiSalvo was one of the discussants. Two of my PhD students were participants:

We have two research papers being presented at ICER this year. Miranda Parker and Kantwon Rogers will be presenting Students and Teachers Use An Online AP CS Principles EBook Differently: Teacher Behavior Consistent with Expert Learners (see paper here) which is from Miranda C. Parker, Kantwon Rogers, Barbara J. Ericson, and me. Miranda and Kantwon studied the ebooks that we've been creating for AP CSP teachers and students (see links here). They're asking a big question: "Can we develop one set of material for both high school teachers and students, or do they need different kinds of materials?" First, they showed that there was statistically significantly different behaviors between teachers and students (e.g. different number of interactions with different types of activities). Then, they tried to explain why there were differences.

We develop a model of teachers as expert learners (e.g., they know more knowledge so they can create more linkages, they know how to learn, they know better how to monitor their learning) and high school students as more novice learners. They dig into the log file data to find evidence consistent with that explanation. For example, students repeatedly try to solve Parsons problems long after they are likely to get it right and learn from it, while teachers move along when they get stuck. Students are more likely to run code and then run it again (with no edits in between) than teachers. At the end of the paper, they offer design suggestions based on this model for how we might develop learning materials designed explicitly for teachers vs. students.

Katie Cunningham will be presenting Using Tracing and Sketching to Solve Programming Problems: Replicating and Extending an Analysis of What Students Draw (see paper here) which is from Kathryn Cunningham, Sarah Blanchard, Barbara Ericson, and me. The big question here is: "Of what use is paper-and-pen based sketching/tracing for CS students?" Several years ago, the Leeds' Working Group (at ITiCSE 2004) did a multi-national study of how students solved complicated problems with iteration, and they collected the students' scrap paper. (You can find a copy of the paper here.) They found (not surprisingly) that students who traced code were far more likely to get the problems right. Barb was doing an experiment for her study of Parsons Problems, and gave scrap paper to students, which Katie and Sarah analyzed.

First, they replicate the Leeds' Working Group study. Those who trace do better on problems where they have to predict the behavior of the code. Already, it's a good result. But then, Katie and Sarah go further. For example, they find it's not always true. If a problem is pretty easy, those who trace are actually more likely to get it wrong, so the correlation goes the other way. And those who start to trace but then give up are even more likely to get it wrong than those who never traced at all.

They also start to ask a tantalizing question: Where did these tracing methods come from? A method is only useful if it gets used — what leads to use? Katie interviewed the two teachers of the class (each taught about half of the 100+ students in the study). Both teachers did tracing in class. Teacher A's method gets used by some students. Teacher B's method gets used by no students! Instead, some students use the method taught by the head Teaching Assistant. Why do some students pick up a tracing method, and why do they adopt the one that they do? Because it's easier to remember? Because it's more likely to lead to a right answer? Because they trust the person who taught it? More to explore on that one.

August 18, 2017 at 7:00 am 6 comments

Call for Nominations to Chair ICER 2019

SIGCSE is changing how they organize ICER.  Posted with Judy Sheard’s permission:

The ACM/SIGCSE International Computing Education Research conference (icer.acm.org) is the premier conference in the world focused on computer science education research, now in its 13th year. The leadership structure has recently been reorganized so that the the individual overseeing the selection of the program (the Program Chair) and the individual overseeing the running of the conference at a particular venue (the Site Chair) are to be held by different individuals.

We are currently seeking nominations for a Site Chair and a Program Chair for ICER 2019, to be held in North America.

Both appointments to Chair are for two years, called the “junior” and “senior” years, respectively. Site Chairs host the conference at their home institution during their senior year. Only one appointment for each role will be made each year, so that in any given year there is a junior and senior Site co-chair and a junior and senior Program co-chair. A nomination committee of the Program and Site chairs for the current year and the SIGCSE Board ICER liaison nominates the ICER Site chair and Program chair to start serving two years from the current year. The SIGCSE Board makes the appointments to both roles.

For both positions, the country of the home institution of each appointee will be rotated geographically by year as has been the tradition for ICER conference chairs, i.e.

  • Year 1: North America
  • Year 2: Europe
  • Year 3: North America
  • Year 4: Australasia

The criteria for appointees:

  • Program co-chair:
    1. Prior attendance at ICER
    2. Prior publication at ICER
    3. Past service on the ICER Program Committee
    4. Research excellence in Computing Education
    5. Collaborative and organizational skills sufficient to work on the Conference Committee and to share oversight of the program selection process.
  • Site chair:
    1. Prior attendance at ICER
    2. Collaborative and organizational skills sufficient to work on the Conference Committee and to oversee all of the local arrangements.
    3. Demonstrated interest in the computing education research community.

To nominate an individual, please include the individual’s CV and a cover letter explaining how the individual meets the criteria for the role. Self-nominations are welcomed. Please send nominations for the Site chair to the 2017 Site Chair, Donald Chinn (dchinn@uw.edu), and nominations for the Program chair to the 2017 Program Chair, Josh Tenenberg (jtenenbg@uw.edu). We also encourage informal expressions of interest to the individuals just mentioned.

March 13, 2017 at 7:00 am Leave a comment

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 Amy 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!

img_3874

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.

September 16, 2016 at 7:07 am 4 comments

Amy Ko’s sabbatical research pivot into Computing Education

Great blog post from Amy Ko on why she’s shifting into computing education research.  I hope lots of researchers come to a similar realization — that computing education is valuable, hard, and interesting.

After I stepped down as AnswerDash CTO and begin my post-tenure sabbatical, it became clear I had to pivot my research focus. No more developer tools. No more studies of productivity. I’m now much less interested in accelerating developers’ work, and much more interested shaping how developers (and developers-in-training) learn and shape their behavior.

Source: My sabbatical research pivot | Bits and Behavior

June 1, 2016 at 7:55 am Leave a comment

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/

April 27, 2016 at 7:42 am Leave a comment

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.

April 7, 2016 at 12:09 pm Leave a comment

A CS Education Research Class Syllabus

I’m teaching a graduate special-topics course on Computer Science Education Research this semester.  Several folks have asked me about what goes into a class like that.  Here’s the syllabus (from our “T-Square” Sakai site).  The references to “Guzdial” below are to my new book, Learner-Centered Design for Computing Education that I just turned in to Morgan & Claypool on Nov. 15. Should be available by the end of the year.

This class would look different if it was in Education, rather than in Computer Science.  For example, there might be less on tools.  The sessions where we consider how CS Ed Research appears at CHI and IDC may no longer be relevant.  Instead, I could imagine work contextualizing CS Education Research in mathematics education or science education.  I would expect to see sessions on equity, on teacher development, and on computing in schools.

 

CS8803: Computer Science Education Research

College of Computing Building Room 52, 9:35-10:55 T/Th

Teacher: Mark Guzdial, guzdial@cc.gatech.edu, TSRB 324/329

Office Hours:: By appointment

Course Overview: Introduction to computing education research (CER). History and influential early work. Learning goals for different populations, with particular attention to broadening participation in computing. Connections to research in learning sciences, educational psychology, science education. Design of research studies in CER, including Multi-Institutional Multi-National, laboratory, and classroom studies.

Textbook: We’ll be using readings from the ACM Digital Library (feely available on campus), and Guzdial’s new monograph Learner-Centered Design of Computing Education (draft available here in Resources, and eventually at the Morgan & Claypool site http://www.morganclaypool.com/toc/hci/1/1). We’ll use other readings that are available on the Web or via the Resources folder on T-Square.

Grades

  • 30%: Do 5 Reading Reflections. There are 6 opportunities for reading assignments. Students can skip one. Reading reflections are marked check or minus (something needs to be fixed). All reading reflections should be typed, with font >= 11 pt. No reading reflection should be longer than 3 pages typed and single spaced.
  • 15%: Class participation. Class time will be interactive, with little lecture. It’s a significant part of the learning in the class to participate. (The programming assignment is part of class participation.)
  • 10%: Research Study Re-Design. Redesign a research study from a published paper (referenced in Guzdial or published in ICER, SIGCSE, RESPECT, or ITICSE), to improve on the scope and findings. Due Oct 20.
  • 10% Where would you use this?. Try out any of Scratch, Alice, App Inventor, Snap, StarLogo, NetLogo, Blockly, or Pencil Code. Knowing what you know from class, would you recommend this environment? When? For whom? To learn what? Write a short (2-3 page) paper. Due Nov. 19.
  • 10%: Research Question White Paper. Write a short (3-4 pages) white paper defining a research question that’s worth exploring in CER. Explain why it’s an important, interesting, and answerable question. Identify the research community that you are speaking to with this research question. Think first section of an NSF proposal. Due Nov 12.
  • 25%: Research Study Design. Propose a study to explore the your unique research question. Think NSF proposal. Plan on 6-10 pages. 15% on paper due Nov 24. 10% on 10 minute presentation (5 minute Q&A) during last week of class.

Syllabus

Week 1

Aug 18: Introduction to class

  • Who are you and what is your experience with computing education?
  • Small Group Discussion: What do you want to know about computing education research? What do you think is unknown and worth exploring?

Aug 20: Computing for Everyone. Read Chapter 1 of Guzdial.

  • Come in with a quote that’s “interesting”
  • Pro/Con Debate: “We should teach computing to everyone.”

Week 2

Aug 25: Learning Sciences

Aug 27: The Challenges of Learning Programming. Read Chapter 2 of Guzdial.

  • Come in with a quote that’s “interesting”
  • Small group activity: What’s your hypothesis for why programming is hard? How would you test your hypothesis?
  • Reading Reflection: Using ideas and quotes from Chapter 1 and 2 of “How People Learn” to explain what’s hard about learning to program.

Week 3

Sep 1: Read Multi-institutional, multi-national studies in CSEd Research: some design considerations and trade-offs (ACM DL link)

  • Come in with a quote that’s “interesting”
  • Compare and contrast: Randomized-control trials (see definition) vs. longitudinal studies (see definition) vs. MIMN studies.
    • What are each good for?
    • Why not use more RCT and longitudinal studies in computing education?

Sep 3: Read Computational Thinking and Using Programming to Learn in Guzdial

  • Generate a list: What are examples of computational thinking?
  • Small group activity: Have you ever used programming to help you learn something else? What are the characteristics of when programming helps and when it gets in the way?

Week 4

Sep 8: Read the first Chapter of Changing Minds at this link and Weintrop and Wilensky from ICER 2015 (ACM DL link)

  • Generate a list: What are characteristics of programming environments that support learning?
  • Small group activity: How do characteristics of programming for software development and for learning differ?
  • Reading Reflection: Identify some testable claims about Boxer in diSessa’s chapter. How would you test that claim?

Sep 10: Read Media Computation and Contextualized Computing Education in Guzdial

  • Come in with a quote that’s “interesting”
  • A mini-lecture with peer instruction and prediction using Media Computation.
  • Reading Reflection: When might contextualized computing help, and where might it not?

Week 5

Sep 15: Write a program to create something of interest or answer a question of interest before coming to class.

  1. Either download JES (from Github link) and create a picture or sound that you find interesting.
  2. Or Download Python (recommend using the Enthought install) and use the Computational Freakonomics website and course notes to answer a question of interest.
  3. Or use the CSPrinciples Ebook Data Chapters to answer a question about pollution in states.

Be prepared to show what you made or what you learned in class.

Come to class ready to answer two questions:

  • Did this motivate you to learn more about CS or the context?
    • Where did programming get in the way, and where did it help?

Sep 17: Read Adults as Computing Learners in Guzdial.

  • Come in with a quote that’s “interesting”
  • Small group activity: What’s similar and dissimilar between the teachers and the graphic designers? Identify another class of adults who might need to learn computing. Which group are they more like?

Week 6

Sep 22: Read The state of the art in end-user software engineering (ACM DL link)

  • Come in with a quote that’s “interesting”
  • Build two lists: Features of a programming environment that support end-user programming and those that support learning about computing by end-user programmers.

Sep 24: Read Learner-Centered Computing Education for CS Majors by Guzdial

  • Come in with a quote that’s “interesting”
  • Small group activity: Come up with examples from your own experience of (a) CS education that you see as learner-centered and (b) CS education that was not learner-centered.
  • Reading Reflection: Contrast the adults in Chapter 5 and the non-majors in Chapter 6 with the CS majors in Chapter 7. What’s similar and what’s different about their learning and the support that they need?

Week 7

Sep 29: Read one of:

  • Spatial Skills Training in Introductory Computing (see ACM DL link)
  • Subgoals, Context, and Worked Examples in Learning Computing Problem Solving (see ACM DL link)
  • Boys’ Needlework: Understanding Gendered and Indigenous Perspectives on Computing and Crafting with Electronic Textiles (see ACM DL link)

Come to class ready (a) to summarize your paper and (b) to support/refute these three hypotheses:

  • We ought to add spatial skills training in all introductory CS courses.
  • We ought to use subgoal-labeled worked examples in all introductory CS courses.
  • We have to consider gender and cultural relevance in designing all introductory CS courses.
  • Reading Reflection: You are the Director of Georgia Tech’s Division of Computing Instruction. You may implement one change across all of your introductory courses, and you have very little budget. What will you change?

Oct 1: Read Towards Computing for All in Guzdial.

  • Come in with a quote that’s “interesting”
  • BIG list: What do we most need to know to advance computing for all? Where are the research gaps?
  • Everyone leave with a personal list of the top three research gaps that you find most interesting.
  • Reading Reflection: Pick any paper referenced in Guzdial that we did not read separately in this class. Read it and summarize it for me.

Week 8

Oct 6: Read Margulieux and Madden’s “Educational Research Primer” (in class Resources)

  • Small group activity: For your favorite research gaps, what research methods would you use to fill some of that gap?
  • Group activity list: What are the research methods that we need to learn more about?

Oct 8: RESEARCH METHODS: Based on the Oct 6 discussion, we’ll pick a paper or two to read here to inform our knowledge of research methods.

Newer Research

Week 9

Oct 13: No class! Fall Break.

Oct 15: RESEARCH METHODS: Based on the Oct 6 discussion, we’ll pick a paper or two to read here to inform our knowledge of research methods.

  • Discussion of Research Project: You don’t have to do it. You do have to design it.
    • First step: Define your question (due Nov 10), and make it answerable.
    • Second step: Tell us how you’d answer it.

Older Research

Week 10

Oct 20: Research Re-Design Due Here By 5 pm.

Oct 22: Read CE21 and IUSE proposals in Resources. (Note: They both weren’t funded in this form.)

  • Group Dissection:
    • What are the research questions?
    • What are the hypotheses?
    • What are the research methods?
  • Small group: Is this do-able? Would you give it a thumbs-up or a thumbs-down?

Week 11

Oct 27: What’s involved in reaching and studying populations at large-scale? Large scale: Read 37 Million Compilations: Investigating Novice Programming Mistakes in Large-Scale Student Data (ACM DL link) and Programming in the wild: trends in youth computational participation in the online scratch community (ACM DL link)

  • Come in with a quote that’s “interesting”
  • Two lists: What can we know from looking at these kinds of data, and what can’t we know?

Oct 29: What’s involved in reaching and studying populations at small-scale? Small scale interviews/phenomenography: Read Graduating students’ designs: through a phenomenographic lens (ACM DL link)

  • Come in with a quote that’s “interesting”
  • Small group discussion: What can we answer with a phenomengraphic approach that we can’t learn (easily) in other ways?

Week 12

Nov 3: What’s involved in reaching and studying populations in high school? In the High School: Read A Crafts-Oriented Approach to Computing in High School: Introducing Computational Concepts, Practices, and Perspectives with Electronic Textiles (ACM DL link)

  • Come in with a quote that’s “interesting”
  • Storytime: Sharing stories about getting into K-12 schools.

Nov 5: CS Education Research in CHI. Read Learning on the job: characterizing the programming knowledge and learning strategies of web designers (ACM DL link) and Programming in the pond: a tabletop computer programming exhibit (ACM DL link)

  • Come in with a quote that’s “interesting”
  • Group list: What makes a CHI paper different from an ICER paper?

Week 13

Nov 10: CS Education Research in IDC. Read Strawbies: explorations in tangible programming (ACM DL link) and “Let’s dive into it!”: Learning electricity with multiple representations (ACM DL link)

  • Come in with a quote that’s “interesting”
  • Group list: What makes an IDC paper different?

Nov 12: Research White Paper Due Here

CS Ed Research at Georgia Tech. Read one of Betsy DiSalvo’s papers — your choice.

  • Come in with a quote that’s “interesting”
  • Small group: Contrast Betsy’s research questions and methods with those of Mark’s and his students.

Week 14

Nov 17: CS Ed Research at Georgia Tech. Read Engaging underrepresented groups in high school introductory computing through computational remixing with EarSketch (ACM DL link) and EarSketch: A Web-based Environment for Teaching Introductory Computer Science Through Music Remixing (ACM DL link)

  • Group list:
    • What are the research questions for EarSketch?
    • What are the research hypotheses?
    • What are the research methods?

Nov 19: Try it out! Hand in your Where would you use this? papers before class. Come to class prepared to demo the environment you picked.

  • Debate: For a set of audiences and learning goals that we define in class, argue for your environment to meet that need.

Week 15

Nov 24: Research Design Paper Due Here.

Nov 26: No Class! Eat Turkey.

Week 16

Dec 1: Present Research Designs

Dec 3: Present Research Designs

November 18, 2015 at 8:22 am 3 comments

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