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Sally Fincher and I are organizing this year’s Doctoral Consortium for students working in computing education. Do come join us in Glasgow!
ICER DC Call for Proposals
The ICER 2014 Doctoral Consortium provides an opportunity for doctoral students to explore and develop their research interests in a 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. We welcome submissions from students at any stage of their doctoral studies.
Sally Fincher, University of Kent at Canterbury
Mark Guzdial, Georgia Institute of Technology
Contact us at: firstname.lastname@example.org
What is the Doctoral Consortium?
The DC has the following objectives:
- Provide a supportive setting for feedback on students’ research and research direction
- Offer each student comments and fresh perspectives on their work from researchers and students 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 Sunday, August 10 2014. Students at any stage of their doctoral studies are welcome to apply and attend. The number of participants is limited to 12. Applicants who are selected will receive a limited partial reimbursement of travel, accommodation and subsistence (i.e., food) expenses of $600 (USD).
Preparing and Submitting your Consortium Proposal Extended Abstract
Candidates should prepare a 2-page research description covering central aspects of your PhD work, which follows the structure, details and format specified in the ICER Doctoral Consortium submission template Word<http://icer.hosting.acm.org/wp-content/uploads/2013/05/ICER2013-dc-template.doc> / LaTeX<http://icer.hosting.acm.org/wp-content/uploads/2013/05/ICER2013_dc_template.zip>.
Key points include:
- Your situation, i.e., the university doctoral program context in which your work is being conducted.
- Context and motivation that drives your dissertation research
- Miniature Background/literature review of key works that frames your research
- Hypothesis/thesis and/or problem statement
- Research objectives/goals
- Your research approach and methods, including relevant rationale
- Results to date and your argument for their validity
- Current and expected contributions
Appendix 1. A letter of nomination from your primary dissertation advisor, that supports your participation in the DC, explains how your work connects with the ICER community, and describes the expected timeline for your completion of your doctorate.
Appendix 2. Your concise current Curriculum Vita (1–2 pages)
Once you have assembled – and tested – the PDF file, the entire submission file should be emailed to email@example.com no later than 17:00 PDT on 21 May 2014. When submitting the applications, please put “ICER DC 2014 – <Last Name>” in the Subject line.
Friday 21st May – initial submission
Monday 2nd June – notification of acceptance
Monday 16th June – camera ready copy due
Doctoral Consortium Review Process
The review and decision of acceptance will balance many factors. This includes the quality of your proposal, and where you are within your doctoral education program. It also includes external factors, so that the group of accepted candidates exhibit a diversity of backgrounds and topics. Your institution will also be taken into account, where we are unlikely to accept more than two students from the same institution. Confidentiality of submissions is maintained during the review process. All rejected submissions will be kept confidential in perpetuity. Upon Acceptance of your Doctoral Consortium Proposal Authors will be notified of acceptance or rejection on 2 June 2014, or shortly after.
Authors of accepted submissions will receive instructions on how to submit publication-ready copy (this will consist of your extended abstract only), and will receive information about attending the Doctoral Consortium, about preparing your presentation and poster, about how to register for the conference, travel arrangements and reimbursement details. Registration benefits are contingent on attending the Doctoral Consortium.
Please note that submissions will not be published without a signed form releasing publishing copyright to the ACM. Attaining permissions to use video, audio, or pictures of identifiable people or proprietary content rests with the author, not the ACM or the ICER conference.
Before the Conference
Since the goals of the Doctoral Consortium include building scholarship and community, participants will be expected to read all of the Extended Abstracts of your colleagues prior to the beginning of the consortium with a goal of preparing careful and thoughtful critique. Although many fine pieces of work may have to be rejected due to lack of space, being accepted into the Consortium involves a commitment to giving and receiving thoughtful commentary.
At the Conference
All participants are expected to attend all portions of the Doctoral Consortium. We will also be arranging an informal Welcome Dinner for participants and discussants on Saturday August 9, 2014 before the consortium begins. Please make your travel plans to join us this evening to get acquainted.
Within the DC, each student will present his or her work to the group with substantial time allowed for discussion and questions by participating researchers and other students. Students will also present a poster of their work at the main conference. In addition to the conference poster, each student should bring a “one-pager” describing their research (perhaps a small version of the poster using letter or A4 paper) for sharing with faculty mentors and other students.
After the Conference
Accepted Doctoral Consortium abstracts will be distributed in the ACM Digital Library, where they will remain accessible to thousands of researchers and practitioners worldwide.
AUTHORS TAKE NOTE: The official publication date is the date the proceedings are made available in the ACM Digital Library. This date will be one week prior to the first day of the conference. The official publication date affects the deadline for any patent filings related to published work.
I don’t believe the main propositions of the article below. Not all STEM education will lead to more women discovering an interest in IT. Putting computing as a mandatory subject in all schools will not necessarily improve motivation and engagement in CS, and it’s a long stretch to say that that will lead to more people in IT jobs.
I addressed the quote below, by Ashley Gavin, in my Blog@CACM post for this month: The Danger of Requiring CS in US K-12 Schools.
“You make it an option, the girl is not going to take it. You have to make it mandatory and start it at a young age,” says Ashley Gavin, curriculum director at Girls Who Code, a nonprofit working to expose more girls to computer science at a young age that has drawn support from leading tech firms such as Google, Microsoft and Intel.
“It’s important to start early because, most of the fields that people go into, they have exposure before they get to college. We all study English before we get to college, we all study history and … social studies before we get to college,” Gavin says. “No one has any idea what computer science is. By the time you get to college, you develop fear of things you don’t know. Therefore early exposure is really important.”
Susan H. Rodger, recipient of the Karl V. Karlstrom Outstanding Educator Award for contributions to the teaching of computer science theory in higher education, and the development of computer science education in primary and secondary schools. She and her students developed JFLAP (Java Formal Languages and Automata Package), an interactive software tool that allows students to construct and test examples of automata and grammars. These concepts are foundational to the design of software components, such as compiler parts. Intended primarily for undergraduate students or as an advanced topic for high school, JFLAP is used worldwide in computer science theory, compiler, and discrete mathematics courses. Through workshops for faculty development, Rodger’s work contributed to the creation of a professional community around the use of visualizations to teach algorithms. She also leads efforts to introduce the programming language Alice in primary and secondary schools. Rodger is a professor of the practice of computer science at Duke University. Currently chair of the ACM Special Interest Group on Computer Science Education (SIGCSE), she is a board member of CRA-W and a member of the ACM Education Policy Committee. The Karlstrom Award recognizes educators who advanced new teaching methodologies; effected new curriculum development in Computer Science and Engineering; or contributed to ACM’s educational mission.
My colleague, Amy Bruckman, considers in her blog how HCI design principles lead us to question whether MOOCs can achieve their goals.
Can a MOOC teach course content to anyone, anywhere? It’s an imagination-grabbing idea. Maybe everyone could learn about topics from the greatest teachers in the world! Create the class once, and millions could learn from it!
It seems like an exciting idea. Until you realize that the entire history of human-computer interaction is about showing us that one size doesn’t fit all.
The California state legislature is attempting to affect change to computer science education in California, and for all the right reasons. They’re getting the message that computer science is what drives innovation and economic growth in California, and that the demand for computer science graduates in California far exceeds supply. There are simply not enough students prepared or preparing to join this high tech workforce. They’re also starting to understand that computer science needs to count for something other than an elective course for more schools to offer it and for more students to take it – especially girls and underrepresented students of color. What they may not quite understand yet is that there aren’t enough teachers prepared to teach computer science in K-12, although one assemblyman spoke of the need for a single subject teaching credential in computer science, so maybe someday we’ll get there … baby steps!
So, it was exciting in Sacramento last week as the Assembly and Senate Education Committees passed a handful of CS-related bills with flying colors and broad bi-partisan support! ACCESS (the Alliance for California Computing Education in Students and Schools) was on hand to help provide analysis and information. Many thanks to Josh Paley, a computer science teacher at Gunn High School in Palo Alto and a CSTA advocacy and leadership team member, who provided substantive testimony on two priority bills*. Josh provided compelling stories of students who had graduated and gone on to solve important problems using their CS skills. Amy Hirotaka, State Policy and Advocacy Manager, of Code.org, Andrea Deveau, Executive Director of TechNet, and Barry Brokaw, lobbyist for Microsoft also testified on these bills. It was also exciting to see a wide range of organizations supporting this important discipline.
All of the following CS-related bills passed out of committee, all but one with unanimous approval:
1) AB 1764* (Olsen and Buchanan) would allow school districts to award students credit for one mathematics course if they successfully complete one course in computer science approved by the University of California as a “category c” (math) requirement for admissions. Such credit would only be offered in districts where the school district requires more than two courses in mathematics for graduation, therefore, it does not replace core math requirements.
2) AB 1539* (Hagman) would create computer science standards that provide guidance for teaching computer science in grades 7-12.
3) AB 1540 (Hagman) establishes greater access to concurrent enrollment in community college computer science courses by high school students.
4) AB 1940 (Holden) establishes a pilot grant program to support establishing or expanding AP curriculum in STEM (including computer science) in high schools with such need (passed with two noes).
5) AB 2110 (Ting) requires computer science curriculum content to be incorporated into curriculum frameworks when next revised.
6) SB1200 (Padilla) would require CSU and request UC to establish a uniform set of academic standards for high school computer science courses, to satisfy the “a-g” subject requirements, as defined, for the area of mathematics (“c”) for purposes of recognition for undergraduate admission at their respective institutions.
7) ACR 108 (Wagner) would designate the week of December 8, 2014, as Computer Science Education Week (passed on consent).
AB 1530 (Chau), to be heard by the Assembly Education Committee on April 23, would encourage the Superintendent of Public Instruction to develop or, as needed, revise a model curriculum on computer science, and to submit the model curriculum to the State Board of Education for adoption (specifically focuses on grades 1-6).
Anyone really interested in hearing the bill presentation, testimony and supporters can see it here:
Senate Education Committee: http://calchannel.granicus.com/MediaPlayer.php?view_id=7&clip_id=2012
Assembly Education Committee: http://calchannel.granicus.com/MediaPlayer.php?view_id=7&clip_id=2019
I’ll plan another update once these bills move further.
Really interesting idea — Code.org’s Pat Yongpradit sent a note to all of CSTA, asking CS teachers to help provide hints for Code.org tutorials. By reaching out to CSTA, they’re doing better than crowd-sourcing. They’re CS-teacher-sourcing.
We’ve had millions of students try the Code.org tutorials. They’ve submitted over 11 million unique computer programs as solutions to roughly 100 puzzles.
We’ve mapped out which submissions are errors (ie they don’t solve the puzzle), and which are sub-optimal solutions (they solve the puzzle, but not efficiently).
Today, erroneous user submissions receive really unhelpful error feedback, such as “You’re using the right blocks, but not in the right way”. We want your help improving this, by providing highly personal feedback to very specific student errors. Watch the video below to see what we mean.
Important article that gets at some of my concerns about using MOOCs to inform education research. The sampling bias mentioned in the article below is one of my responses to the claim that we can inform education research by analyzing the results of MOOCs. We can only learn from the data of participants. If 90% of the students go away, we can’t learn about them. Making claims about computing education based on the 10% who complete a CS MOOC (and mostly white/Asian, male, wealthy, and well-educated at that) is bad science.
Cheerleaders for big data have made four exciting claims, each one reflected in the success of Google Flu Trends: that data analysis produces uncannily accurate results; that every single data point can be captured, making old statistical sampling techniques obsolete; that it is passé to fret about what causes what, because statistical correlation tells us what we need to know; and that scientific or statistical models aren’t needed because, to quote “The End of Theory”, a provocative essay published in Wired in 2008, “with enough data, the numbers speak for themselves”.
Unfortunately, these four articles of faith are at best optimistic oversimplifications. At worst, according to David Spiegelhalter, Winton Professor of the Public Understanding of Risk at Cambridge university, they can be “complete bollocks. Absolute nonsense.”
Last month, Steve Cooper organized a remarkable workshop at Stanford on the Future of Computing Education Research. The question was, “How do we grow computing education research in the United States?” We pretty quickly agreed that we have a labor shortage — there are too few people doing computing education research in the US. We need more. In particular, we need more CS Ed PhD students. The PhD students do the new and exciting research. They bring energy and enthusiasm into a field.
We also need these students to fit into Computing departments, where that could be Computer Science, or Informatics, or Information Systems/Technology/Just-Information Departments/Schools/Colleges. Yes, we need a presence in Education Schools at some point, to influence how we develop new teachers, but that’s not how we’ll best push the research.
How do we get there?
Roy Pea came to the event. He could only spare a few hours for us, and he only gave a brief 10 minute talk, but it was one of the highlights of the two days for me. He encouraged us to think about Learning Sciences as a model. Learning Science grew out of cognitive science and computer science. It’s a field that CS folks recognize and value. It’s not the same as Education, and that’s a positive thing for our identity. He told us that the field must grow within Computing departments because Domain Matters. The representations, the practices, the abstractions, the mental models — they all differ between domains. If we want to understand the learning of computing, we have to study it from within computing.
I asked Roy, “But how do we influence teacher education? I don’t see learning science classes in most pre-service teacher development programs.” He pointed out that I was thinking about it all wrong. (Not his words — he was more polite than that.) He described how learning sciences has influenced teacher development, integrated into it. It’s not about a separate course: “Learning science for teachers.” It’s about changing the perspective in the existing classes.
Ken Hay, a learning scientist (and long-time friend and colleague) who is at Indiana University, echoed Roy’s recommendation to draw on the learning sciences as a model. He pointed out that Language Matters. He said that when Indiana tried to hire a “CS Education Researcher,” faculty in the CS department said, “I teach CS. I’m a CS Educator. How is s/he different than me?”
We started talking about how “Computer Science Education Research” is a dead-end name for the research that we want to situate in computing departments. It’s the right name for the umbrella set of issues and challenges with growing computing education in the United States. It includes issues like teacher professional development and K-12 curricula. But that’s not what’s going to succeed in computing departments. It’s the part that looks like the learning sciences that can find a home in computing departments. Susanne Hambrusch of Purdue offered a thought experiment that brought it home for me. Imagine that there is a CS department that has CS Ed Research as a research area. They want to list it on their Research web page. Well, drop the word “Research” — this is the Research web page, so that’s a given. And drop the “CS” because this is the CS department, after all. So all you list is “Education.” That conveys a set of meanings that don’t necessarily belong in a CS department and don’t obviously connect to our research questions.
In particular, we want to separate (a) the research about how people learn and practice computing from (b) making teaching and learning occur better in a computing department. (a) can lead to (b), but you don’t want to demand that all (a) inform (b). We need to make the research on learning and practice in computing be a value for computing departments, a differentiator. “We’re not just a CS department. We embrace the human side and engage in social and learning science research.” Lots of schools offer outreach, and some are getting involved in professional development. But to do those things informed by learning sciences and informing learning sciences (e.g., can get published in ICER and ICLS and JLS and AERA) — that’s what we want to encourage and promote.
I was in a breakout that tried to generate names. Michael Horn of Northwestern came up with several of my favorites. Unfortunately, none of them were particularly catchy:
- Learning Sciences of Computing
- Learning Sciences for Computing
- Computational Learning and Practice (sounds too much like machine learning)
- Learning Sciences in Computing Contexts
- Learning and Practice in Computing
- Computational Learning and Literacy
We do have a name for a journal picked out that I really like: Journal of Computational Thinking and Learning.
I’d appreciate your thoughts on these. What would be a good name for the field which studies how people learn computing, how to improve that learning, how professionals practice computing (e.g., end-user programming, computational science & engineering), and how to help novices join those professional communities of practice?
I can’t remember the last time I learned so much and had my preconceived notions so challenged in just two days. I have a lot more notes on the workshop, and they may make it into some future blog posts. Kudos to Steve for organizing an excellent workshop, and my thanks to all the participants!
Yup, Herminia has the problem right — if CS MOOCs are even more white and male than our face-to-face CS classes, and if hiring starts to rely on big data from MOOCs, we become even less diverse.
But that’s just the tip of the iceberg. One of the developments that will undoubtedly cement the relationship between big data and talent processes is the rise of massive open online courses, or MOOCs. Business schools are jumping into them whole hog. Soon, your MOOC performance will be sold to online recruiters taking advantage of the kinds of information that big data allows—fine distinctions not only on content assimilation but also participation, contribution to, and status within associated online communities. But what if these new possibilities—used by recruiters and managers to efficiently and objectively get the best talent—only bake in current inequities? Or create new ones?
If states offer career and technical education in pathways (typically 3-4 courses) with a pathway completion exam, they are eligible for Perkins legislation funding to pay for staff and equipment. If AP CS is one of those courses, it’s easier to build the pathway (2-3 courses to define, rather than 3-4) and the pathway is more likely to lead to college-level CS, if a student so chooses. But as the below report mentions, many states believe that Perkins legislation disallows the AP to count. It can, and here’s the report describing how.
If you’re hearing this story in your state, be sure to send your department of education this report!
Career and Technical Education and Advanced Placement (July 2013, PDF)
Traditionally Advanced Placement® (AP) courses and exams have not been recommended for students in Career Technical Education (CTE) programs. This paper, jointly developed and released by NASDCTEc and the College Board aims to bust this myth by showing how AP courses and exams can be relevant to a student’s program of study across the 16 Career Clusters®.
I hadn’t heard about this theory before the below blog post — recommended reading. As usual, I appreciate Kevin’s analysis.
As parents and teachers we encourage children to pursue fields that they enjoy, that they are good at, and that can support them later in life. It may be that girls are getting the “that they are good at” message more strongly than boys are, or that enjoyment is more related to grades for girls. These habits of thought can become firmly set by the time students become men and women in college, so minor setbacks (like getting a B in an intro CS course) may have a larger effect on women than on men. I’m a little wary of putting too much faith in this theory, though, as the author exhibits some naiveté.
The story is interesting and disappointing. Why would GitHub go through all these contortions just because they had this one female engineer — and would have there been less drama and stress if there had been more than just one female engineer? The story has been updated in Sunday’s NYTimes.
The exit of engineer Julie Ann Horvath from programming network GitHub has sparked yet another conversation concerning women in technology and startups. Her claims that she faced a sexist internal culture at GitHub came as a surprise to some, given her former defense of the startup and her internal work at the company to promote women in technology.
In her initial tweets on her departure, Horvath did not provide extensive clarity on why she left the highly valued startup, or who created the conditions that led to her leaving and publicly repudiating the company.
Horvath has given TechCrunch her version of the events, a story that contains serious allegations towards GitHub, its internal policies, and its culture. The situation has greater import than a single person’s struggle: Horvath’s story is a tale of what many underrepresented groups feel and experience in the tech sector.
Hackathons seem the antithesis of what we want to promote about computer science. On the one hand, they emphasize the Geek stereotype (it’s all about caffeine and who needs showers?), so they don’t help to attract the students who aren’t interested in being labeled “geeky.” On the other hand, it’s completely against the idea of designing and engineering software. “Sure, you can do something important by working for 36 hours straight with no sleep or design! That’s how good software ought to be written!” It’s not good when facing the public (thinking about the Geek image) or when facing industry and academia.
So why try to make them “female-friendly”?
OK, so there are a number of valid reasons women tend to stay away from hackathons. But what can hackathon planners due to get more females to attend their events? I found some women offering advice on this subject. Here are some suggestions for making your hackathon more female-friendly.
Amy Quispe, who works at Google and ran hackathons while a student at Carnegie Mellon University, writes that having a pre-registration period just for women makes them feel more explicitly welcome at your event. Also, shy away from announcing that its a competition (to reduce the intimidation factor), make sure the atmosphere is clean and not “grungy” and make it easy for people to ask questions. “A better hackathon for women was a better hackathon for everyone,” she writes.
I recently watched the documentary Why we fight, and was struck by the prescience of President Eisenhower’s warning. So many of our educational decisions are made because of the harsh economic realities of today. How many of these are guns-for-butter choices might we have made differently if education was considered? Here in Georgia, computer science curricular decisions are being made with a recognition that there will be little or no funding available for teacher professional development — certainly not enough for every high school CS teacher in the state. What percentage of the DoD budget would it cost to provide professional learning opportunities to every CS teacher in the country? It’s certainly in the single digits.
Every gun that is made, every warship launched, every rocket fired signifies, in the final sense, a theft from those who hunger and are not fed, those who are cold and are not clothed.
This world in arms in not spending money alone.
It is spending the sweat of its laborers, the genius of its scientists, the hopes of its children.
The cost of one modern heavy bomber is this: a modern brick school in more than 30 cities.
It is two electric power plants, each serving a town of 60,000 population.
It is two fine, fully equipped hospitals.
It is some 50 miles of concrete highway.
We pay for a single fighter with a half million bushels of wheat.
We pay for a single destroyer with new homes that could have housed more than 8,000 people.
This, I repeat, is the best way of life to be found on the road the world has been taking.
This is not a way of life at all, in any true sense. Under the cloud of threatening war, it is humanity hanging from a cross of iron.
via Cross of Iron Speech.