Wednesday 18 September 2013

Webinar on new report on Building an Operating System for Computer Science Education

This is related to the report that CSTA blogged on recently. There will be a webinar for those interested in asking questions about it.Join us for a special one-hour webinar presentation about high school computer science education!Expanding computer science education is of vital importance to our nation’s future. If we are going to address the grand challenge of growing computer science education across the country, we need to develop a greater understanding of how to prepare, develop and support computer science teachers of all levels and advocate for expansion and improvement.

Over the last 18 months, The University of Chicago’s Center for Elementary Mathematics and Science Education (CEMSE) and Urban Education Institute (UEI) has carried out national study of the computer science education community—including professional development providers, teachers, administrators and other stakeholders—to inform the growth and spread of high school computer science education in the United States.Join us for a special conversation to learn about the results of that study, ask questions, and share your thoughts about the future of computer science education on Wednesday, September 25th at 3:00 pm (Central Time).

Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

We measure educational productivity wrong: Not numbers-served but learning

The Washington Post series on “The Tuition is Too Damn High” has been fascinating, filled with interesting data, useful insights, and economic theory that I hadn't met previously. The article linked below is about “Baumol’s cost disease” which suggests an explanation for why wages might increase when productivity does not. It’s an explanation that some have used to explain the rise in tuition, which Post blogger Dylan Matthews rejects based on the data (in short: faculty salaries aren't really rising — the increase in tuition is due to other factors). But I actually had a concern about an earlier stage in his argument. It’s absolutely true that our labor intensive methods do not lead to an increase in productivity in terms of number of students, while MOOCs and similar other methods can. However, we can gain productivity in terms of quality of learning and retention. We absolutely have teaching methods, well-supported with research, that lead to better learning and more retention — we can get students to complete more classes with better understanding. In the end, isn't THAT what we should be measuring as productivity of an educational enterprise, not “millions of customers served” (even if they don’t complete and don’t learn)?

Performing a string quartet will always require two violinists, a violist and a cellist. You can’t magically produce the same piece with just two people. Higher education, for at least the past few millennial, has seemed to fall in this category as well. “What just happened in my classroom is not very different from what happened in Plato’s academy,” quips Archibald. We've gotten better at auditorium-building, perhaps, but lecturers generally haven’t gotten more productive.

Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

Doctoral Consortium at the Australasian Computing Education Conference

Great to see happening! The SIGCSE Doctoral Consortium is associated with ICER, which was just in San Diego, and then will be in Glasgow, and then will be in Omaha, and then will be in Melbourne. It’s good to have a DC for Australasian doctoral students before then. This is a call for participation in the Doctoral Consortium (DC) (http://elena.aut.ac.nz/homepages/ace2014/doctoral-consortium.html) for the 16th Australasian Computing Education Conference (ACE 2014), a conference on research and innovation in computing education in its various aspects, at all levels and in all contexts(http://elena.ait.ac.nz/homepages/ace2014/). The conference will be held in Auckland, New Zeal and at the Auckland University of Technology in conjunction with Australian Computer Science Week (ACSW) (http://www.aut.ac.nz/study-at-aut/study-areas/computing–mathematical-sciences/beyond-the-classroom/acsw-2014). The Doctoral Consortium with be held on Monday January 20th 2014 (prior to ACE 2014).

The DC will provide an opportunity for a group of PhD students to discuss and explore their research interests and career objectives with a panel of established researchers in computing education research. The DC is sponsored by the Software Engineering Laboratory of Auckland University of Technology and the School of Computing and Mathematical Sciences. The sponsorship covers full registration for the DC for up to 10 attendees, and for those wishing to stay on to attend the full set of ACSW conferences the $NZD165.00 sponsorship can be applied as partial payment of the full $NZD300.00 student registration fee.The DC is open to students who are currently enrolled in any stage of doctoral studies with a focus on computing education research. The number of participants is limited to 10. Senior researchers in the field will provide feedback and suggestions for improvement of the students research. Each applicant should submit an application that includes the following information in one PDF file:

 Curriculum Vita Research summary, including motivation, background and literature to contextualize the research, research questions, methodologies used or planned, and any results obtained to date.Questions related to the research that the applicant would like to discuss and get feedback on at the doctoral consortium The research summary should be 1-3 pages long, depending on the stage of the research. This summary will be made available to other participants of the doctoral consortium to allow them to provide feedback and prepare questions on the research.

Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

Computer Science Education Week is Dec 8–14, 2013

The dates for CSEd Week are good to know, but the “Hour of Code” from Code.orgis an interesting new initiative.What is Computer Science Education Week?Computer Science Education Week (CSEd Week) is the annual awareness program for computer science education. It is organized each year by the Computing in the Core coalition and Code.org. It is a call to action to raise awareness (particularly in the K-12 environment) about the importance of computer science education and its connection to careers in computing and other fields. CSEd Week is held in recognition of the birthday of computing pioneer Admiral Grace Murray Hopper (December 9, 1906).

What is an Hour of Code?It’s a 1 hour intro to computer science and programming, to give beginners a taste and to demystify “code”. For existing CS teachers, it can be anything you want – get creative. For everybody else, we’ll provide self-guided tutorials anybody can do, with just a web-browser or smartphone, or even unplugged, no experience needed. Note: HTML does not count as an Hour of Code.

Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

Eye Movements in Programming Education: Interesting new workshop

Eye Movements in Programming Education: Analyzing the expert’s gaze
Workshop at the 13th KOLI CALLING INTERNATIONAL CONFERENCE ON COMPUTING EDUCATION RESEARCH

Joensuu, Finland, November 13th – November 14th, 2013

Computer Science Education Research and Teaching mainly focus on writing code, while the reading skills are often taken for granted. Reading occurs in debugging, maintenance and the learning of programming languages. It provides the essential basis for comprehension. By analyzing behavioral data such as gaze during code reading processes, we explore this essential part of programming.This first workshop gives participants an opportunity to get insights into code reading with eye movement data. However, as this data only reflects the low level behavioral processes, the challenge to tackle is how to make use of this data to infer higher order comprehension processes. We will take on this challenge by working on a coding scheme to analyze eye movement data of code reading. The links between low and high level behaviors will help computing science educators to design, realize and reflect on the teaching of code reading skills.

Furthermore, we aim to open discussion about the ways of explicit teaching of readership skills in computing education. Therefore we will discuss the role of reading skills in teaching programming, facilitated by position papers of each participant.To participate send a mail to treacherousness@few-Berliner. It is possible to participate independent of attending Kali Calling. Participants will get eye movement data of reading and comprehension processes of expert programmers, and a coding scheme for annotating the process. You will annotate the video, and reflect on the (perceived) intentions behind the visible pattern. Applying and refining the coding scheme on the data gives insight into the higher order comprehension strategies of the reader.

A short individual reflection and position paper of the results and perspectives for teaching programming is required by the participants [max. 2-3 pages]. As a result, participants will jointly prepare a paper with the data and the refined coding scheme.

Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

Thursday 18 April 2013

Computer Science as a great target for Science Careers

Nice interview with Ed Lazowska of U-W in Science about the state of computer science education and research. The below section is getting picked up elsewhere as an argument for CS as a great choice for students interested in a career in science.

I would have to say “about right.” Ph.D. production in computer science is far lower than in fields with far fewer employment opportunities. And Ph.D.s in computer science have a broad range of employment opportunities that take full advantage of their training. In most other STEM [science, technology, engineering, and mathematics] fields, the vast majority of graduates at all levels take jobs unrelated to their field of study. In computer science, the opposite is true: The vast majority of graduates at all levels take jobs that are in their “sweet spot.” Google hires roughly the same number of graduate students as undergraduate students from the University of Washington. Microsoft also hires a large number of our best Ph.D. students, both for Microsoft Research [MSR] and for the development organization.I do think we need to be cautious. We need to avoid the overproduction—and, honestly, exploitation—that characterizes other fields. Hopefully we’ll be smart enough to learn from their behavior.

Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

Tuesday 16 April 2013

Congratulations to Eric Roberts on the 2012 ACM Karl V. Karlstrom Outstanding Educator Award!

2012 Karl V. Karlstrom Outstanding Educator Award: Eric Roberts, Stanford University
For his outstanding contributions to computing education over decades, through international leadership and intellectual contributions in developing effective computing curriculaEric Roberts has been a truly outstanding educator for decades, starting as the first computer scientist at Wellesley College in 1980. He has personally taught thousands of computer scientists, and reached many more through his textbooks and curriculum development. His textbooks are exemplary; the first, Thinking Recursively, was named in a 1998 CACM survey article as one of the core texts that every computer science educator should know. He built an organization of professional lecturers at Stanford that has become a model for effective teaching of computer science at universities across the country.

Eric has shown exceptional leadership in computing education, made all the more effective because of the obvious priority he placed on being an outstanding educator. He devotes enormous time and energy to drawing attention to and addressing problems in our community, such as underrepresentation of women in computing and the need to devote more resources to computing education during times of enrollment surge. His principles and values have made him a respected voice in the computing education community.Eric s leadership is international in scope. He co-chaired the ACM Education Board for several years, and was one of the founding co-chairs of the ACM Education Council. From 1999 to 2005, he worked to develop a computing curriculum for public high schools in Bermuda. This program was the first national computing curriculum to be certified by an international standard board.

Eric s work on Computing Curriculum 2001 exemplifies his leadership. He drew together diverse constituencies and stakeholders in a multi-year process. He was the principal author of the final report. The report is a significant intellectual achievement that has served educators around the world as they consider what every computing student needs to learn.

Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

Thursday 11 April 2013

Guided Computer Science Inquiry in Data Structures Class

Inquiry-based learning is the best practice for science education. Education activities focus on a driving question that is personally meaningful for students, like “Why is the sky blue?” or “Why is the stream by our school so acidic (or basic)?” or “What’s involved in building a house powered entirely by solar power?” Answering those questions leads to deeper learning about science. Learning sciences results support the value of this approach. It’s hard for us to apply this idea from science education and teach an introductory computing course via inquiry, because students may not have many questions that relate to computer science when they first get started. Questions like “How do I make an app to do X?” or “How do I use Snap on my laptop?” are design and task oriented, not inquiry oriented. Answering them may not lead to deeper understanding of computer science. Our everyday experience of computing, through (hopefully) well-designed interfaces, hides away the underlying computing. We only really start to think about computing at moments of breakdown (whatHeidigger called “present-at-hand”). ”Why can’t I get to YouTube, even though the cable modem light is on?” and “How does a virus get on my computer, and how can it pop up windows on my screen?” It’s an interesting research project to explore what questions students have about computing when they enter our classes.

I realized this semester that I could prompt students to define questions for inquiry-based learning in a second computer science class, a data structures course. I’m teaching our Media Computation Data Structures course this semester. These students have seen under the covers and know that computing technology is programmed. I can use that to prompt them about how new things work. What I particularly like about this approach is how it gets me out of the “Tour of the Code” lecturing style.Here’s an example. We had already created music using linked lists of MIDI phrases. I then showed them code for creating a linked list of images, then presented this output.


I asked students, “What do you want to know about how this worked?” This was the gamble for me — would they come up with questions? They did, and they were great questions. ”Why are the images lined up along the bottom?” “Why can we see the background image?”I formed the students into small groups, and assigned them one of the questions that the students had generated. I gave them 10 minutes to find the answers, and then report back. The discussion around the room was on-topic and had the students exploring the code in depth. We then went through each group to get their answers. Not every answer was great, but I could take the answer and expand upon it to reach the issues that I wanted to make sure that we highlighted. It was great — way better and more interactive than me paging through umpteen Powerpoint slides of code. Then I showed them this output from another linked list of images.


Again, the questions that the students generated were terrific. ”What data are stored in each instance such that some have positions and some are just stacked up on the bottom?” and “Why are there gaps along the bottom?” Still later in the course, I showed them an animation, rendered from a scene graph, and I showed them the code that created the scene graph and generated the animation. Now, I asked them about both the animation code and the class hierarchy that the scene graph nodes was drawing upon. Their questions were both about the code, and about the engineering of the code — why was it decomposed in just this way?


(We didn’t finish answering these questions in a single class period, so I took pictures of the questions so that I could display them and we could return to them in the next class.) I have really enjoyed these class sessions. I’m not lecturing about data structures — they’re learning about data structures. The students are really engaged in trying to figure out, “How does that work like that?” I’m busy in class suggesting where they should look in the code to get their questions answered. We jointly try to make sense of their questions and their answers. Frankly, I hope to never again have to show sequences of Power point slides of code ever again.

Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

Six Reasons Why Computer Science Education is Failing Students

Interesting arguments from the CEO of Live Code. These two points are particularly interesting. The first is: What sits between Scratch, or Alice, or App Inventor, and professional-class languages like JavaScript or C++? I would put Python in there, but I still see that the Scratch->Python gap is a big one. The second paragraph is really striking, and I’d like to see more evidence. Does Israel’s great CS ed system lead to the strong start up culture, or is it because Israelis value technology that they have both a great start up culture and a great CS Ed system? Up to about age 13 there are some excellent tools in widespread use, most notable among them being the free and popular Scratch from MIT and MIT App Investor However students outgrow Scratch by around age 13 and schools often don’t make a good choice of language for the next phase in a child’s education. Many traditional programming languages such as JavaScript or C++ are completely inappropriate for this age group. Students struggle to understand what they are learning and often spend their lessons searching for a missing symbol. The current generation of students use smartphones and so selecting a tool that allows them to create their own apps is another great opportunity to make learning directly relevant to them. Our own Live Code platform provides a handy solution to these issues by allowing students to create their own mobile apps using a programming language that is close to English.

I firmly believe that a strengthening computer science education program has to be one of the most obvious and cost effective things we can do to ensure future economic prosperity. Israel has the highest rate of start up per ca pita anywhere and that in part stems from its strong computer science education program. Estonia, another country with both a strong tech sector and economy, recently announced a plan to expand teaching of computer science to all primary school children. Do we want to be left in the dust by these countries, or left unable to compete with the growing economies of India and China? What is it going to take to get computer science education moved up the agenda in the USA and here in the UK?

Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

Opening a Gateway for Girls to Enter the Computer Field – NYTimes.com

All these efforts to draw in more girls to computing are great, but the last sentence is a big deal. How do we keep them? How do we help girls to survive the thousand paper cuts? Girls Who Code is among the recent crop of programs aiming to close the gender gap in tech by intervening early, when young women are deciding what they want to study. With names like Hackbright Academy, Girl Develop It, Black Girls Code and Girls Teaching Girls to Code, these groups try to present a more exciting image of computer science. The dearth of women in the tech industry has been well documented. Even though women represent more than half the overall work force, they hold less than a quarter of computing and technical jobs, according to the National Center for Women and Information Technology based at the University of Colorado, Boulder. At the executive and founder levels, women are even scarcer.

Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

Requiring Computing Education: An Impractical Path to Computing Literacy

My thinking on computing education has been significantly influenced by a podcast about hand-washing and financial illiteracy. I suspect that education is an ineffective strategy for achieving the goal of Computing Literacy for Everyone. I have a greater appreciation for work like Alan Kay’s on STEPS, Andy Ko’s work on tools for end-user programming, and the work on Racket.On Hand-Washing and Financial Illiteracy. I have been listening to Freakonomics podcasts on long drives. Last month, I listened to “What do hand-washing and financial illiteracy have in common?” I listened to it again over the next few days, and started digging into the literature they cited. At hospitals, hand-washing is far less common than our knowledge of germ theory says it ought to be. What’s most surprising is that doctors, the ones with the most education in the hospital, are the least likely to wash their hands often enough. The podcast describes how one hospital was able to improve their hand-washing rates through other behavioral methods, like shaming those who didn’t wash their hands and providing evidence that their hands were likely to be filled with bacteria. More education doesn’t necessarily lead to behavioral change.

Much more important was the segment on financial illiteracy. First, they present the work of Annamuria Lusardia who has directly measured the amazing financial illiteracy in our country. There is evidence that much of the Great Recession was caused by poor financial decisions by individuals. Less than a third of the over-50-year-old Americans that Lusardia studied could correctly answer the question, “If you put $100 in a savings account with 2% annual interest, at the end of five years you will have (a) less than $102, (b) exactly $102, or (c) more than $102?” More mathematics background did lead to more success on her questions, but she calls for a much more concerted effort in financial education. Her arguments are supported by some pretty influential officials, like Fed Reserve Chair Ben Bernanke and former Secretary of the Treasury Paul O’Neill. It makes sense: If people lack knowledge, we should teach them.Lauren Willis strongly disagrees, and she’s got the data to back up her argument. She has a 2008 paper with the shocking title, Against Financial Literacy Educationthat I highly recommend. She presents evidence that financial literacy education has not worked — not that it couldn’t work, but it isn’t working. She cited several studies that showed negative effects of financial education. For example, high school students who participated in the Jump$start program become much more confident about their ability to make financial decisions, and yet made worsedecisions than those students who did not participate in the program.

The problem is that financial decisions are just too complicated, and education (especially universal education) is expensive to do well (though Willis doesn’t offer an estimated cost). Educational curricula (even if tested successful) is not always implemented well. The gap between education in teen years and making decisions in your 40′s and 50′s is huge. Instead of education, we should try to prevent damage from ignorance. Willis suggests that we should create a cadre professional of financial advisors and make them available to everyone (for some “pro bono”), and that we should increase regulation of financial markets so that there are fewer riskier investments for the general public. It costs the entire society enormously when large numbers of people make poor financial decisions, and it’seven more expensive to provide enough education to prevent the cost of all that ignorance.This was a radical idea for me. Education is not free, and sometimes it’s cheaper to prevent the damage of ignorance than correcting the ignorance.Implications for Computing Literacy Education. I share the vision of Andy DiSessa and others of computing as a kind of literacy, and a goal of “Computing for All” where everyone has the knowledge and facility to build programs (for modeling, simulations, data analyses, etc.) for their needs. Let’s call that a goal of “Universal Computing Literacy,” and we can consider the costs of using education to reach that goal, e.g., “Universal Computing Education to achieve Universal Computing Literacy.”

The challenge of computing literacy may be even greater than the challenge of financial literacy. People know even less about computing than they do about finance. We don’t know the costs of that ignorance, but we do know that it has been difficult and expensive to provide enough education to correct that ignorance. Computing may be even more complicated than finance. Willis talks about the myriad terms that people need to know to make good financial decisions (like “adjustable rate mortgages”), but they are at least compounds of English words! I attended a student talk this week, where terms like “D3” and “GreaseMonkey” were bandied about like they were common knowledge. We invent so much language all the time. What could we possibly teach today in high school that will be useful in even five or ten years, when those students are in their careers, to be able to build something that they could use? “We’ll teach conceptual knowledge so that they can pick up any tools or languages they need.” We have no idea how to achieve this goal in computer science. It takes an enormous effort to develop transferable knowledge, and progress in computing changes the target task all the time. I have a PhD in Computer Science, and it takes me way too much effort to pick up JavaScript libraries that I might want to use — far more effort than a non-technical person will be willing to put in.

The problem is that education is often inefficient and ineffective. Jeremy Roschelle pointed out that education improvements rarely impact economic outputs. Greg Wilson shared a great paper with me in response to some tweets I sent about these ideas. Americans have always turned to education to solve a wide variety of ills, but surprisingly, without much evidence of efficacy. We can teach kids all about healthy eating, but we still have a lot of obesity. Smokers often know lots of details about how bad smoking is for them. Education does not guarantee a change of behavior. This doesn’t mean that education could not be made more effective and more efficient. But it might be even more expensive to fix education than to deal with ignorance. Universal education is always going to be expensive, and some things are worth it. Text illiteracy and innumeracy are very expensive for our society. We need to address those, and we’re not doing a great job at that yet. Computing education to achieve real literacy is just not as important. I am no longer convinced that providing computing education to everyone is going to be effective to reach the goal of making everyone computing literate, and I am quite convinced that it will be very expensive. Requiring computing education for STEM professionals at undergraduate level may still be cost-effective, because those are the professionals most likely to see the value of computing in their careers, which reduces the costs and makes the education more likely to be effective.

Barb sees a benefit in Universal Computing Education, but not to achieve Universal Computing Literacy. We need to make computing education available everywherefor broadening participation in computing. To get computing into every school, Barb argues that we have to make it required for everyone. Without the requirement, schools won’t go to the effort of including it. Without a requirement, female and URM students who might not see themselves in computing, would never even give it a chance. In response to my argument about cost, she argues that the computing education for everyone doesn’t have to be effective. We don’t have to achieve lifelong literacy for everyone. Merely, it has to give everyone exposure, to give everyone the opportunity to discover a love for computing. Those that find that love will educate themselves and/or will pursue more educational opportunities later. I heard Mike Eisenberg give a talk once many years ago, where he said something that still sticks with me: that the point of school is to give everyone the opportunity to find out what they’re passionate about. For that reason, we have to give everyone the chance to discover computing, and requiring it may be the only way to reach that goal. It’s always possible that we’ll figure out to educate more effectively at lower cost. For example, integrating computing literacy education into mathematics and science classes may be cheaper — students will be using it in context, teachers in STEM are better prepared to learn and teach computing, and we may improve mathematics and science teaching along the way. My argument about being too expensive is based on what we know now how to do. Economic arguments are often changed by improved science (see Malthus).

As Willis suggests for financial literacy, we in computing literacy are probably going to be more successful for less cost by focusing on the demand side of the equation. We need to make computing easier, and make tools and languages that are more accessible, as Alan Kay, Andy Ko, and the Racket folks are doing. We have to figure out how to change computing so that it’s possible to learn and use it over an entire career, without a PhD in Computer Science. We have to figure out how to get these tools into use so that students see use of such tools as authenticand not a “toy.” “Computing for All” is an important goal. “Access to Computing Education for All” is critical. “Universal Computing Education to achieve Universal Computing Literacy” is likely to be ineffective and will be very expensive. On the other hand, requiring computing education may be the only way to broaden participation in computing.

Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

No Dynabook Yet: An Interview with Computing Pioneer Alan Kay

Nice interview with Alan Kay, with nice links to Enlargeable and Knowledge Navigator videos. Here’s the segment where Alan describes why the iPad is not a Dyna book. The interesting thing about this question is that it is quite clear from the several early papers that it was an ancillary point for the Dyna book to be able to simulate all existing media in an editable/author able form in a highly portable networked (including wireless) form. The main point was for it to be able to qualitatively extend the notions of “reading, writing, sharing, publishing, etc. of ideas” literacy to include the “computer reading, writing, sharing, publishing of ideas” that is the computer’s special province.

For all media, the original intent was “symmetric authoring and consuming”. Isn't it crystal clear that this last and most important service is quite lacking in today’s computing for the general public? Apple with the iPad and iPhone goes even further and does not allow children to download an E toy made by another child somewhere in the world. This could not be farther from the original intentions of the entire ARPA-IPTO/PARC community in the ’60s and ’70s.

Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

Monday 1 April 2013

Beware of the High Cost of ‘Free’ Online Courses – NYTimes.com

It nice to see someone with a background in management making this argument, that the costs of MOOCs may be greater than the benefits. Give-away pricing in education, Mr. Cusumano warns, may well be a comparable misstep. The damage would occur, he writes in the article, “if increasing numbers of universities and colleges joined the free online education movement and set a new threshold price for the industry — zero — which becomes commonly accepted and difficult to undo.”

In our conversation, I offered the obvious counterargument. Why should education necessarily be immune from this digital, Darwinian wave, when other industries are not? Isn’t this just further evidence of the march of disruptive progress that ultimately benefits society?Mr. Cusumano has heard this reasoning before, and he is unconvinced. In the article, he explains, “I am mostly concerned about second- and third-tier universities and colleges, and community colleges, many of which play critical roles for education and economic development in their local regions and communities.” “In education,” Mr. Cusumano adds, “‘free’ in the long run may actually reduce variety and opportunities for learning as well as lessen our stocks of knowledge.”

Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

Addressing Computer Science Student Misconceptions with Contrasts

I have wanted to figure out how to use in my class the interesting findings about the use of video to address science misconceptions. The idea is that you want to use real student misunderstandings and contrast them with better, more powerful ways of understanding something. The challenge for me has been how to get those misunderstandings in class. I don’t want to call on someone that I know has a misconception and have him lay out his explanation — just to pounce on it to say, “And that’s wrong!” Then I realized my chance this last week. I was grading the second midterm, and saw all these surprising misconceptions made evident in the students answers. Normally, the class time after a midterm is about going over the midterm answers. I decided instead to make it about the misconceptions.

I built a Powerpoint slide deck filled with these contrasting bits of code (like the contrasting explanations in the science videos) and with alternative code for answering the same problem. I tried to disguise the code so as not to embarrass any particular student. For example, I changed variable names — and since students expect that changing variable names should make plagiarized code impossible to detect, that should be enough, right? I formed students into pairs, and then put up the slides and asked for them to respond or to answer a question in their pair. For example, I noticed that several students seemed to confuse IF and WHEN. So I put up this slide.

I asked students to punch into their clickers what they thought “A” would print out. And yes, about 20% of the students guessed something other than “1.” I executed “A” as a way of checking the answer. I then had students answer for “B.” I could hear lots of discussion suggesting that students were seeing the difference between IF and WHILE. I put code up like this:

I had each group discuss what would be the output of this code, then took suggestions of the output from around the room. I wrote them on the board, and then had pairs vote on which answer they most agreed with. By the time we voted, everyone got it right — just generating the options, and hearing the discussion as each option went up, they figured out what the best answer was. I really liked hearing students “discovering” invariants as they talked, e.g., “The loop can never end, because you never change node1a in the loop!” I have no real evidence of learning here — we’ll see how things go in the class. I do have a sense that this was a more fruitful activity for a most-midterm discussion than just me giving the answers and telling them why the wrong answers were wrong. That recitation of sins usually just results in students coming up to me with, “You only give me 5 points for this, but based on the discussion, I think I deserved 7.” This way, the discussion was punctuated more often with “Ohhhh — now I get it!”

Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

Saturday 30 March 2013

CERIAS: Some thoughts on “cybersecurity” professionalization and education

Relates to the issue of when an employee needs college, and when they don’t. For Cyber security, they do. Relates to the growing needs in cyber security in the UK and in the US. Too many (current) educational programs stress only the technology — and many others include significant technology training components — because of pressure by outside entities, rather than a full spectrum of education and skills. We have a real shortage of people who have any significant insight into the scope of application of policy, management, law, economics, psychology and the like to cyber security  although arguably, those are some of the problems most obvious to those who have the long view. (BTW, that is why CERIAS was founded 15 years including faculty in nearly 20 academic departments: “ cyber security” is not solely a technology issue; this has been recognized by several other universities that are treating it more holistically.) These other skill areas often require deeper education and repetition of exercises involving abstract thought. It seems that not as many people are naturally capable of mastering these skills. The primary means we use to designate mastery is through post secondary degrees, although their exact meaning does vary based on the granting institution.

Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

Free Early-Career Learning Sciences Workshop at CMU LearnLab


Call for Participation 2nd Annual Learning Science Workshop Research and Innovation for Enhancing Achievement and Equity http://www.learnlab.org/opportunities/summerworkshop.php June 22-23 Carnegie Mellon University Pittsburgh PA Applications Due May 5, 2013

*No Cost To Attend*

Overview

Learn Lab, an NSF Science of Learning Center (SLC) at Carnegie Mellon and the University of Pittsburgh, has an exciting summer research opportunity available to early career researchers in the fields of psychology, education, computer science, human-computer interfaces and language technologies. The workshop is targeted to senior graduate students, post-docs and early career faculty. The workshop seeks broad participation, including members of underrepresented groups as defined by NSF (African American, Hispanic, Native American) who may be considering a research or faculty position in the learning sciences. This two-day workshop immediately precedes the Learn Lab Summer School (www.learnlab.org/opportunities/summer/). Our research theme is there search and innovation for enhancing achievement and equity, including these five areas:

* Enhancing Achievement through Educational Technology and Data Mining. Using domain modeling, and large data sets to discover when learning occurs and to provide scaffolding for struggling students. Seehttp://www.learnlab.org/research/wiki/index.php/Computational_Modeling_and_Data_Mining.

* 21st Century Skills, Dispositions, and Opportunities. Re-examining the goals of education and assessment and considering transformative changes in how and where learning occurs.

* Opening Classroom Discourse. Studying how classroom talk contributes to domain learning and supports equity of learning opportunity. See LearnLab’s Social-Communicative Factors thrustwww.learnlab.org/research/wiki/index.php/Social_and_Communicative_Factors_in_Learning.

* Course-Situated Research. Running principle-testing experiments while navigating the complex waters of real-world classrooms. Seewww.learnlab.org/research/wiki/index.php/In_vivo_experiment.

* Motivation Interventions for Learning. Implementing theory based motivational interventions to target at risk populations to improve robust student learning. Seehttp://www.learnlab.org/research/wiki/index.php/Metacognition_and_Motivation

The substantive focus of the workshop is the use of current research and innovations to enhance achievement and equity at all levels of learning. Activities will include demonstrations of the diverse set of ongoing learning sciences research projects at LearnLab, and poster presentations or talks by participants. Participants will also meet with LearnLab faculty in research groups and various informal settings. We will provide information about becoming a part of the Carnegie Mellon or University of Pittsburgh learning science community. In addition to these substantive themes, the workshop will provide participants with opportunities for professional development and the chance to gain a better understanding of the academic career ladder. These include mentoring that focuses on skills, strategies and “insider information” for career paths. Sessions will include keynote speakers and LearnLab senior faculty discussing professional development topics of interest to the attendees. These may include the tenure and promotion process, launching a research program, professionalism, proposal writing, among other topics. There is no cost to attend this workshop

We are very pleased to announce that the workshop will have two distinguished keynote speakers: Nora S. Newcombe, Ph.D. is the James H. Glackin Distinguished Faculty Fellow and Professor of Psychology at Temple University. Dr. Newcombe is the PI of the Spatial Intelligence and Learning Center (SILC), headquartered at Temple and involving Northwestern, the University of Chicago and the University of Pennsylvania as primary partners. Dr. Newcombe was educated at Antioch College, where she graduated with a major in psychology in 1972; and at Harvard University, where she received her Ph.D. in Psychology and Social Relations in 1976. She taught previously at Penn State University.A nationally recognized expert on cognitive development, Dr. Newcombe’s research has focused on spatial development and the development of episodic and autobiographical memory. Her work has been federally funded by NICHD and the National Science Foundation for over 30 years. She is the author of numerous scholarly chapters and articles on aspects of cognitive development, and the author or editor of five books, including Making Space: The Development of Spatial Representation and Reasoning (with Janellen Huttenlocher) published by the MIT Press in 2000.

Tammy Clegg, Ph.D. is an assistant professor in the College of Education with a joint appointment in the College of Information Studies at the University of Maryland. She received her PhD in Computer Science at Georgia Tech in 2010 and her Bachelor of Science in Computer Science from North Carolina State University in 2002. From 2010-2012 Tamara was a postdoctoral fellow at the University of Maryland with the Computing Innovations Fellows program. Her work focuses on developing technology to support life-relevant learning environments where children engage in science in the context of achieving goals relevant to their lives. Kitchen Chemistry is the first life-relevant learning environment she designed along with colleagues at Georgia Tech. In Kitchen Chemistry, middle-school children learn and use science inquiry to make and perfect dishes. Clegg uses participatory design with children to design these new technologies. Her work currently includes creating new life-relevant learning environments (e.g., Sports Physics, Backyard Biology) to understand how identity development happens across these environments. From this analysis, she aims to draw out design guidelines for life-relevant learning activities and technology in various contexts (e.g., sports, gardening).

About LearnLab

LearnLab is funded by the National Science Foundation (award number SBE-0836012). Our center leverages cognitive theory and computational modeling to identify the instructional conditions that cause robust student learning. Our researchers study robust learning by conducting in vivo experiments in math, science and language courses. We also support collaborative primary and secondary analysis of learning data through our open data repository LearnLab DataShop, which provides data import and export features as well as advanced visualization, statistical, and data mining tools. To learn more about our cognitive science theoretical framework, read our Knowledge-Learning-Instruction Framework. The results of our research are collected in our theoretical wiki which currently has over 400 pages. It also includes a list of principles of learning which are supported by learning science research. The wiki is open and freely editable, and we invite you to learn more and contribute.

Application Process

Applicants should email their CV, this demographic form, a proposed presentation title and abstract, and a brief statement describing their research interests to Jo Bodnar (jobodnar@cs.cmu.edu) by May 5, 2013. Please use the subject Application for LearnLab Summer Workshop 2013. Upon acceptance, we will let you know if you have been selected for a talk or poster presentation.

Costs

There is no registration fee for this workshop. However, attendance is limited so early applications are encouraged. Scholarships for travel are available. Scholarships will be awarded based on your application, including your research interests, future plans, and optional recommendation letter.


Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

Cybersecurity as a motivation for drawing high schoolers into CS

We’ve talked about the UK and the US worrying about having enough cyberwarriors to deal with future cybersecurity issues. CMU is helping to build a game to entice high school students into computing, with cybersecurity as the focus.Carnegie Mellon University and one of the government’s top spy agencies want to interest high school students in a game of computer hacking.

Their goal with “Toaster Wars” is to cultivate the nation’s next generation of cyber warriors in offensive and defensive strategies. The free, online “high school hacking competition” is scheduled to run from April 26 to May 6, and any U.S. student or team in grades six through 12 can apply and participate.David Brumley, professor of computer science at Carnegie Mellon, said the game is designed to be fun and challenging, but he hopes participants come to see computer security as an excellent career choice.

Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

Feds give nudge to competency-based education: Beyond the Credit Hour

Of all the open learning movement initiatives, this may be the most important. The credit hour is a poor measure of learning-attained. It’s too large a grain size to be important as a measure of instruction. Moving to competencies (whatever that may end up being) is a move in the right direction, in terms of facilitating our ability to measure the amount of learning and the amount of teaching effort involved in an education program.
The U.S. Department of Education has endorsed competency-based education with the release today of a letter that encourages interested colleges to seek federal approval for degree programs that do not rely on the credit hour to measure student learning.

Department officials also said Monday that they will give a green light soon to Southern New Hampshire University’s College for America, which would be the first to attempt the “direct assessment” of learning – meaning no link to the credit hour – and also be eligible for participation in federal financial aid programs.via Feds give nudge to competency-based education | Inside Higher Ed.
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Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

Guided Computer Science Inquiry in Data Structures Class

Inquiry-based learning is the best practice for science education. Education activities focus on a driving question that is personally meaningful for students, like “Why is the sky blue?” or “Why is the stream by our school so acidic (or basic)?” or “What’s involved in building a house powered entirely by solar power?” Answering those questions leads to deeper learning about science. Learning sciences results support the value of this approach.It’s hard for us to apply this idea from science education and teach an introductory computing course via inquiry, because students may not have many questions that relate to computer science when they first get started. Questions like “How do I make an app to do X?” or “How do I use Snap on my laptop?” are design and task oriented, not inquiry oriented. Answering them may not lead to deeper understanding of computer science. Our everyday experience of computing, through (hopefully) well-designed interfaces, hides away the underlying computing. We only really start to think about computing at moments of breakdown (what Heidegger called “present-at-hand”). ”Why can’t I get to YouTube, even though the cable modem light is on?” and “How does a virus get on my computer, and how can it pop up windows on my screen?” It’s an interesting research project to explore what questions students have about computing when they enter our classes.

I realized this semester that I could prompt students to define questions for inquiry-based learning in a second computer science class, a data structures course. I’m teaching our Media Computation Data Structures course this semester. These students have seen under the covers and know that computing technology is programmed. I can use that to prompt them about how new things work. What I particularly like about this approach is how it gets me out of the “Tour of the Code” lecturing style.Here’s an example. We had already created music using linked lists of MIDI phrases. I then showed them code for creating a linked list of images, then presented this output.


I asked students, “What do you want to know about how this worked?” This was the gamble for me — would they come up with questions? They did, and they were great questions. ”Why are the images lined up along the bottom?” “Why can we see the background image?”I formed the students into small groups, and assigned them one of the questions that the students had generated. I gave them 10 minutes to find the answers, and then report back. The discussion around the room was on-topic and had the students exploring the code in depth. We then went through each group to get their answers. Not every answer was great, but I could take the answer and expand upon it to reach the issues that I wanted to make sure that we highlighted. It was great — way better and more interactive than me paging through umpteen PowerPoint slides of code.Then I showed them this output from another linked list of images.


Again, the questions that the students generated were terrific. ”What data are stored in each instance such that some have positions and some are just stacked up on the bottom?” and “Why are there gaps along the bottom?”Still later in the course, I showed them an animation, rendered from a scene graph, and I showed them the code that created the scene graph and generated the animation. Now, I asked them about both the animation code and the class hierarchy that the scene graph nodes was drawing upon. Their questions were both about the code, and about the engineering of the code — why was it decomposed in just this way?


(We didn't finish answering these questions in a single class period, so I took pictures of the questions so that I could display them and we could return to them in the next class.)
I have really enjoyed these class sessions. I’m not lecturing about data structures — they’re learning about data structures. The students are really engaged in trying to figure out, “How does that work like that?” I’m busy in class suggesting where they should look in the code to get their questions answered. We jointly try to make sense of their questions and their answers. Frankly, I hope to never again have to show sequences of PowerPoint slides of code ever again.

Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

Sunday 24 March 2013

Fostering Gender Diversity in Computing: March Issue of IEEE Computer

The March issue of IEEE Computer is going to be devoted to fostering gender diversity in computing. It looks like it’s going to be a great issue, including a piece by my school chair, Annie Anton.Why is this important to us? Computing and information technology are among the fastest growing U.S. industries: technical innovation plays a critical role in every sector of the U.S. and global economy, and computing ranks among the top 10 high-profile professions. However, as a nation, we are not prepared to attract and retain the professional workforce required to meet future needs. By 2018, US universities will produce only 52 percent of the computer science bachelor’s degrees needed to fill the 1.4 million available jobs.

A lack of diverse perspectives will inhibit innovation, productivity, and competitiveness. In addition to failing to attract new and diverse talent, industry is also losing trained professionals who are already interested in technology. While 74 percent of professional women report “loving their work,” 56 percent leave at the career “midlevel” point just when their loss is most costly to the company—this is more than double the quit rate for men.via Fostering Gender Diversity in Computing.


Deepa SinghBusiness DeveloperWeb Site:-http://www.gyapti.comBlog:- http://gyapti.blogspot.comEmail Id:-deepa.singh@soarlogic.com

Thy Employee is Not You: New Study Exposes Gender Bias in Tech Job Listings

I found the study linked below fascinating, in part because I saw myself making exactly these mistakes. I have absolutely described jobs in those masculine terms instead of the more neutral terms. I didn’t realize that those were terms that would dissuade females from applying. When we teach classes on designing user interfaces, a key idea that we want students to learn is that “Thy User is Not You.” Don’t design for yourself. Don’t judge the interface only from your own eyes. You can’t imagine how the user is really going to use your interface. Try it with real users. Get input from real users. You can’t design interfaces for yourself and expect them to be usable for others. (Just like you can’t develop educational software for the developed world and expect it to work in the developing world.)

I heard the same lesson in this study. If you want to hire employees different than you, find out what you need to put in your job ad to attract them. You do not know how they will read your ad. Get input from others (who see things differently than you), and use expert guidance. Thy employee is not you. The paper — which details a series of five studies conducted by researchers at the University of Waterloo and Duke University — found that job listings for positions in engineering and other male-dominated professions used more masculine words, such as “leader,” “competitive” and “dominant.” Listings for jobs in female-dominated professions — such as office administration and human resources — did not include such words.

A listing that seeks someone who can “analyze markets to determineappropriate selling prices,” the paper says, may attract more men than a list that seeks someone who can “understand markets to establish appropriate selling prices.” The difference may seem small, but according to the paper, it could be enough to tilt the balance. The paper found that the mere presence of “masculine words” in job listings made women less interested in applying — even if they thought they were qualified for the position.

Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

Friday 22 March 2013

We Need an Economic Study on Lost Productivity from Poor Computing Education

How much does it cost the American economy that most American workers are not computer literate? How much would be saved if all students were taught computer science? These questions occurred to me when trying to explain why we need ubiquitous computing education. I am not an economist, so I do not know how to measure the costs of lost productivity. I imagine that the methods would be similar to those used in measuring the Productivity Paradox.

We do have evidence that there are costs associated with people not understanding computing:
  • We know from Scaffidi, Shaw, and Myers that there are a great many end-user programmers in the United States. Brian Dorn’s research on graphic designersidentified examples of lost productivity because of self-taught programming knowledge. Brian’s participants did useless Google searches like “javascript <variablename>” because they didn’t know which variable or function names were meaningful and which were arbitrary. Brian saw one participant spend a half an hour studying a Web resource on Java, before Brian pointed out that he was programming in Javascript which was a different language. I bet that many end-users flail like this — what’s the cost of that exploration time?
  • Erika Poole documented participants failing at simple tasks (like editing Wikipedia pages) because they didn’t understand basic computing ideas like IP addresses. Her participants gave up on tasks and rebooted their computer, because they were afraid that someone would record their IP address. How much time is lost because users take action out of ignorance of basic computing concepts?
We typically argue for “Computing for All” as part of a jobs argument. That’s what Code.org is arguing, when they talk about the huge gap between those who are majoring in computing and the vast number of jobs that need people who know computing. It’s part of the Computing in the Core argument, too. It’s a good argument, and a strong case, but it’s missing a bigger issue. Everyday people need computing knowledge, even if they are not professional software developers. What is the cost for not having that knowledge?

Now, I expect Mike Byrne (and other readers who push back in interesting ways on my “Computing for Everyone” shtick) to point out that people also need to know about probability and statistics (for example), and there may be a greater cost for not understanding those topics. I agree, but I am even harder pressed to imagine how to measure that. One uses knowledge of probability and statistics all the time (e.g., when deciding whether to bring your umbrella to work, and whether you can go another 10K miles on your current tires). How do you identify (a) all the times you need that knowledge and (b) all the times you make a bad prediction because you don’t have the right knowledge? There is also a question of whether having the knowledge would change your decision-making, or whether you would still bepredictably irrational. Can I teach you probability and statistics in such a way that it can influence your everyday decision making? Will you transfer that knowledge? I’m pretty sure that once you know IP addresses and that Java is not the same as JavaScript, you won’t forget those definitions — you don’t need far-transfer for that to be useful. While it is a bit of a “drunk under the streetlight” argument, I can characterize the behaviors where computing knowledge would be useful and when there are costs for not having that knowledge, as in Brian and Erika’s work. I am trying to address problems that I have some idea of how to address.

Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

Worst practice in providing educational technology, especially to developing world

I followed an insightful chain of blog articles to this one. I started with Larry Cuban’s excellent piece about “No End to Magical Thinking When It Comes to High-Tech Schooling” which cited the quote below, but first when through a really terrific analysis of the explanations that educational technology researchers sometimes make when hardware in dumped in the developing world fails to have a measurable impact. I highly recommend the whole sequence for a deeper understanding of what real educational reform looks like and where technology can play a role.

1. Dump hardware in schools, hope for magic to happen This is, in many cases, the classic example of worst practice in ICT use in education. Unfortunately, it shows no sign of disappearing soon, and is the precursor in many ways to the other worst practices on this list. “If we supply it they will learn”: Maybe in some cases this is true, for a very small minority of exceptional students and teachers, but this simplistic approach is often at the root of failure of many educational technology initiatives.

Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

Thursday 21 March 2013

Why the MOOCopalypse is Unlikely

The article from The Chronicle referenced below helped convince me that the MOOCopalypse is unlikely to happen. The MOOC opalypseis the closing of most of American universities (“over half” said one of our campus leaders recently) because of MOOCs. The Chronicle piece is about the professors currently offering MOOCs, and the survey (at left) is only with MOOC providers.The first and greatest challenge to the MOOC opalypse is economic. It’s a huge cost to produce MOOCs — not just on the professors making the MOOCs, but on all their colleagues who have to cover the teaching and service that the MOOC-makers aren't providing. For what benefit? Most of the MOOC professors talk about the huge impact, about a “one to two to three magnitudes” greater impact. Not clear to me how universities can take that to the bank. Unlike fame from a great result or influential paper, MOOC fame doesn't obviously lead to greater funding opportunities.

There is currently no revenue from MOOCs. It is not reducing the number of students who need to be taught, nor the amount of service needed to run the place. It may be reducing the amount of research (and research funding) that the MOOC providers may have provided. MOOC professors who see that MOOCs may reduce the costs to students are consequently predicting fewer tuition dollars flowing into their institutions. Literally, I do not see that the benefits of MOOCs outweigh their costs.In all, the extra work took a toll. Most respondents said teaching a MOOC distracted them from their normal on-campus duties.“I had almost no time for anything else,” said Geoffrey Hinton, a professor of computer science at the University of Toronto.“My graduate students suffered as a consequence,” he continued. “It’s equivalent to volunteering to supply a textbook for free and to provide one chapter of camera-ready copy every week without fail.”via The Professors Behind the MOOC Hype – Technology – The Chronicle of Higher Education.The second reason why the MOOC opalypse is unlikely is because those predicting the closing of community colleges and state universities do not understand the ecology of these institutions and how they are woven into the fabric of their communities.
  • This year, I chair the computing and information system technologies (CIST) advisory board of local Chattahoochee Technical College. Most of the advisory board draws on local industry, the people who hire CTC’s graduates. They have a say in what gets taught, by describing what they need. How do you replicate that interchange with MOOCs?
  • I have had the opportunity to visit several institutions in the University System of Georgia through “Georgia Computes!” At Albany State University, they teach the standard computing courses, but the languages and tools they use are drawn from ones that the local industry needs. At Columbus State University, they teach content that local Fort Binning needs for the military personnel and employees. Courses are set up to meet the logistical needs of the military at Fort Binning. Why would the MOOC provider-professors at Stanford, MIT, Harvard, or Toronto want to meet any of those needs?
My third reason why I believe the MOOC opalypse is unlikely is based on a prediction about the technology. I do not believe that MOOCs are going to dramatically increase their completion rates (even with degree options and accreditation schemes like Accredible.com) ,and I do not believe that MOOCs will be successful in teaching the majority of students. Founders of higher education (e.g., parents and legislators) and consumers of higher education products (e.g., employers) are not going accept the closing of state universities in favor of an option that fewer students graduate from and that produces weaker graduates. We are already hearing the push back against the plans to move community college courses into MOOCs in The Chronicle. I can believe that some universities may close, but I cannot believe that we as a nation would willingly embrace the closing of a not-great but underfunded educational system for a markedly worse one.I’m reminded of the A Nation at Risk report and the claim ”If an unfriendly foreign power had attempted to impose on America the mediocre educational performance that exists today, we might well have viewed it as an act of war.” That report was about primary and secondary school education. The MOOC opalypse would be an act of war on higher education.

Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

Living with MOOCs: Surviving the Long Open Learning Winter

One of the positive benefits of MOOCs is that a lot of faculty and administration are exploring educational innovations with technology. When teachers explore how to facilitate learning, improved teaching and learning is likely to result. One of theproblems is that many of these teachers and administrators are deciding that MOOCs and other open learning resources are the best bets for addressing educational problems. They are buying into the belief that open learning is the best that there is (or, perhaps, the only thing that they found) and into the associated beliefs (e.g., that existing educational systems are ineffective and unsustainable, that “everyone already knows that a college degree means next to nothing“). Those of us who do educational technology research and don’t do MOOCs are likely in for a stretch where our work will be under-appreciated, or simply ignored. The AI community talks about their “AI Winter.” Let’s call this the Open Learning Winter.

Regular readers of this blog (and I’m grateful that you are here!) know that I’ve been doing a good bit of traveling the last few months. From MIT and Stanford, to Indiana and SIGCSE, I’ve had the opportunity to hear lots of people talk about the educational innovations that they are exploring, why they have decided on MOOCs and other open learning resources, and what they think about those of us who are not building MOOCs. The below are paraphrased snippets of some of these conversations (i.e., some of the parts of these quotes are literally cut-and-paste from email/notes, while other parts are me condensing the conversation into a single quote representing what I heard):
  • “You do eBooks  Don’t you know about Connexions? Why not just do Connexions books? Do you think that student interactivity with the ebook reallymatters?”
  • ”You’re making ebooks instead of MOOCs? That’s really interesting. Are you building a delivery platform now? One that can scale to 100K students this Fall?” As if that’s the only thing that counts — when no one even considered that scale desirable even a couple years ago.
  • “Ebooks will never work for learning. You can’t ask them to read. Students only want video.”
  • “Anchored Collaboration sounds interesting. Can I do it with Piazza? No? Then it’s not really useful to anyone, is it?”
  • “Why should we want to provide resources to state universities? Don’t you know that all of their programs are going to die?”
  • NSF Program officer at CCC MROE Workshop, “We better figure out online education. All the state universities are going to close soon.”
These attitudes are not going to change quickly. People are investing in MOOCs and other open learning resources. While I do not believe that the MOOC Apocalypse will happen, people who do believe in it are making investments based on that belief. The MOOC-believers (perhaps MOOC Apocalypse survivalists?) are going to want to see their investments will pan out and will keep pursuing that agenda, in part due to the driving power of “sunk costs” (described in this well done Freakonomics podcast). That’s normal and reasonable, but it means that it will be a long time before some faculty and administrators start asking, “Is there anything other than MOOCs out there?” 

I think MOOCs are a fascinating technology with great potential. I do not invest my time developing MOOCs because I believe that the opportunity cost is too high. I have had three opportunities to build a MOOC, and each time, I have decided that the work that I would be giving up is more valuable to me than the MOOC I would be producing. I do not see MOOCs addressing my interests in high school teachers learning CS, or in end-users who are learning programming to use in their work, or in making CS more diverse. It may be that universities will be replaced by online learning, but I don’t think that they’ll all look like MOOCs. I’m working on some of those non-MOOC options.Researchers like me, who do educational technology but don’t do MOOCs, need to get ready to hunker down. Research funding may become more scarce since there are MOOCopalypse survivalists at NSF and other funding agencies. University administrators are going to be promoting and focusing attention on their pet MOOC projects, not on the non-believers who are doing something else. (Because we should realize that there won’t be anything else!) There will probably be fewer graduate students working in non-MOOC areas of educational technology. Most of the potential PhD students who contacted me during this last application cycle were clear about how important MOOCs were to them and the research that they wanted to do.We need to learn to live with MOOCs, even if we don’t do MOOCs. Here are a couple of the hunkering down strategies I've been developing:
  • While I don’t want to spend the time to build a MOOC, I am interested in being involved in analysis of MOOC data. It’s not clear how much data Coursers or Audacity will ever release (and why isn't edX releasing data — they’re a non-profit!), but I see a great value in understanding MOOCs. We might also learn lessons that can be applied in other areas of educational innovation with technology.
  • My colleagues involved in MOOCs at Georgia Tech have told me that we have the rights to re-use GT MOOC materials (e.g., all the video that has been collected). That might be a source of interesting materials for my research. For example, my colleague Jim Foley suggested that I might re-purpose video from a MOOC to create an eBook on the same content that might be usefully contrasted in a study.
I can’t predict just how long the Open Learning Winter might be. Given the height of the hype curve associated with MOOCs and the depth of the pockets of the early adopters, I suspect that it’s going to be quite a long, cold winter. Make sure that you have lots of jerky on-hand — and hope that it’s just winter and not an Ice Age.

Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

Tuesday 19 March 2013

True success of ‘robotics revolution’ hinges on training and education

I buy this argument, and it’s more subtle than the recent 60 Minutes piece. Does the influx of robotics lead to more or fewer jobs? 60 Minutes says fewer jobs. In contrast, Henrik Christensen says more jobs. The difference is education. There are fewer lower-education jobs, but more higher-education jobs. So unless you ramp up education, it is fewer jobs.

That’s not to say the transition to this brave new world of robotics will be painless. Short-term upheaval is inevitable. For Exhibit A, look at the jobless recovery we find ourselves in today: Increased productivity has driven economic growth, yet unemployment rates remain stubbornly high. But most insiders seem to agree that if we look past the short term, the medium- and long-term benefits of the robotics revolution appear to be positive, not just in terms of economic growth but for job creation, too.They also warn that the job creation part will require a keen focus on training and education for those low-skilled workers who get squeezed out of their jobs by robotics. Collectively, we ignore this warning at our own peril.

Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com

Saturday 16 March 2013

Percent of women graduates BS in CS: National, UW, GT


In the context of David Notkin’s receipt of the 2013 Computing Research Association A. Nico Habermann Award for outstanding contributions to supporting underrepresented groups in the computing research community, Lecia Barker of the National Center for Women & Information Technology (we hosted their Washington State Awards for Aspirations in Computing last weekend) sent us the chart to the right, comparing UW CSE’s performance to the national average in granting bachelors degrees to women.via UW CSE News » Women in Computer Science: UW CSE is a pacesetter.

It was really great to see these results in the U. Washington CSE News, but it got me to wondering: Did all the big R1 institutions rise like this, or was this unusual at UW? I decided to generate the GT data, too.I went to the GT Self-Service Institutional Research page and downloaded the degrees granted by college and gender in each of 2005, 2006, and on up to 2011. (All separate spreadsheets.) I added up Fall, Spring, and Summer graduates for each year, and computed the female percentage. Here’s all three data sets graphed. While GT hasn’t risen as dramatically as UW in the last two years (so UW really has done something remarkable!), but GT’s rise from 2005 far below the national average to above the national average in 2009 is quite interesting.

Why is UW having such great results? Ed Lazowska claimed at SIGCSE 2013 that it’s because they have only a single course sequence (“one course does fit all,” he insisted) and because they have a large number of female TAs. I don’t believe that. I predict that more courses would attract more students (see the “alternative paths” recommendation from Margolis and Fisher), and that female TA’s support retention, not recruitment. I suspect that UW’s better results have more to do with the fact that GT’s students declare their major on their application form, while UW students have to apply to enter the CSE program. Thus, (a) UW has the chance to attract students on-campus and (b) they have more applications than slots, so they can tune their acceptances to get the demographics that they value.

Percentage of females in BS CS graduates, by year, nationally, for U. Washington, and for Georgia Tech.

Deepa Singh
Business Developer
Email Id:-deepa.singh@soarlogic.com