Archive for May, 2015

California’s multi-million dollar online ed flop is a blow for MOOCs: What happened?

I don’t think that MOOCs are a good solution for required classes.  I agree with the idea that MOOCs are for people who want to learn something because they’re interested in it, and that completion rates don’t matter there.

That suggests that we shouldn’t use MOOCs where (a) the students don’t know what they need to know and (b) completion rates matter.

  • Thus, don’t use MOOCs for intro courses (as we learned at GT with English composition and physics) where students don’t know that they really need this knowledge to go on, and the completion rates are even worse than in other MOOCs. The combination hurts the students who want to go on to subsequent courses. Using MOOCs to provide adults with content that might be covered in an intro course isn’t the same thing. For example, an intro to programming course for adults who want to understand something about coding, but not necessarily continue in CS studies, makes sense for a MOOC. If they’re not trying to prepare for a follow-on course, then the completion rate doesn’t really matter.  If the MOOC learners are adults who are foraging for certain information, then the even-lower completion rate in intro-content MOOCs makes sense.  There may only be a small part of that content that someone doesn’t already know.
  • Thus, don’t use MOOCs to teach high school teachers about CS, where they don’t know what CS they need to know, they’re uncertain about becoming CS teachers, and a lack of completion means that the teachers who don’t complete (90-95% of enrollees) don’t know the curriculum that they’re supposed to teach. Using MOOCs to provide existing CS teachers with new opportunities to learn is a good match for the student audience to the affordances of the medium. Trying to draw in new CS teachers (when they are so hard to recruit) via MOOCs makes little sense to me.

Setting aside my concerns about MOOCs, it’s not exactly clear what’s going on in the below article.  I get that it’s not good that California had to just forgive the loan of $7M USD, and that they will likely to continue to lose money.  I get that the quote below says, “we got extremely little in return.”  I don’t see what was the return.  I don’t see how many students actually participated (e.g., we’re told that there was only 250 non-UC students, but not how many UC students participated), and if the courses they created could continue to be used for years after, and so on.  It doesn’t look good, but there’s not enough information here to know that it was bad.

“We spent a lot of money and got extremely little in return,” said Jose Wudka, a physics professor at UC-Riverside who previously chaired the Systemwide Committee on Educational Policy of the Academic Senate, which represents faculty in the UC System.

The project, which cost $7 million to set up at a time when the state was cutting higher-education funding, aspired to let students take courses across campuses.

via California’s multi-million dollar online education flop is another blow for MOOCs – The Hechinger Report.

May 29, 2015 at 8:37 am 2 comments

Why We Need Learning Engineers and Faculty who Know Learning Sciences

I agree strongly with the idea of “learning engineers.” Having learning engineers doesn’t relieve faculty who teach from the responsibility to learn more about learning sciences (see my blog post about testing teachers about PCK). Just teaming up subject-matter experts with learning engineers does not inform a teacher’s day-to-day and in-class decision-making.  The general theme below is one I strongly agree with — we should rely more on evidence-based and research-based teaching.

We are missing a job category: Where are our talented, creative, user-­centric “learning engineers” — professionals who understand the research about learning, test it, and apply it to help more students learn more effectively?

Jobs are becoming more and more cognitively complex, while simpler work is disappearing. (Even that old standby, cab driving, may one day be at risk from driverless cars from Google!) Our learning environments need to do a better job of helping more people of all ages master the complex skills now needed in many occupations.

I am not suggesting that all subject-matter experts (meaning faculty members) need to become learning engineers, although some might. However, students and faculty members alike would benefit from increased collaboration between faculty members and learning experts — specialists who would respect each other’s expertise — rather than relying on a single craftsman in the classroom, which is often the case in higher education today.

via Why We Need Learning Engineers – The Digital Campus – The Chronicle of Higher Education.

May 27, 2015 at 8:46 am Leave a comment

The Invented History of ‘The Factory Model of Education’: Personalized Instruction and Teaching Machines aren’t new

When I was a PhD student taking Education classes, my favorite two-semester sequence was on the history of education.  I realized that there wasn’t much new under the sun when it comes to thinking about education.  Ideas that are key to progressive education movements date back to Plato’s Republic: “No forced study abides in a soul…Therefore, you best of men, don’t use force in training the children in the studies, but rather play. In that way you can also better discern what each is naturally directed toward.”  Here we have learning through games (but not video games in 300BC) and personalized instruction — promoted over 2400 years ago.  I named my dissertation software system Emile after Rousseau’s book with the same name whose influence reached Montessori, Piaget, and Papert decades later.

Audrey Watters takes current education reformers to task in the article linked below.  Today’s reformers don’t realize the history of the education system, that many of the idea that they are promoting have been tried before. Our current education system was designed in part because those ideas have already failed.  In particular, the idea of building “teaching machines” as a response to “handicraft” education was suggested over 80 years ago.  Education problems are far harder to solve than today’s education entrepreneurs realize.

Many education reformers today denounce the “factory model of education” with an appeal to new machinery and new practices that will supposedly modernize the system. That argument is now and has been for a century the rationale for education technology. As Sidney Pressey, one of the inventors of the earliest “teaching machines” wrote in 1932 predicting “The Coming Industrial Revolution in Education,”

Education is the one major activity in this country which is still in a crude handicraft stage. But the economic depression may here work beneficially, in that it may force the consideration of efficiency and the need for laborsaving devices in education. Education is a large-scale industry; it should use quantity production methods. This does not mean, in any unfortunate sense, the mechanization of education. It does mean freeing the teacher from the drudgeries of her work so that she may do more real teaching, giving the pupil more adequate guidance in his learning. There may well be an “industrial revolution” in education. The ultimate results should be highly beneficial. Perhaps only by such means can universal education be made effective.

via The Invented History of ‘The Factory Model of Education’.

The reality is that technology never has and never will dramatically change education (as described in this great piece in The Chronicle).  It will always be a high-touch endeavor because of how humans learn.

Education is fundamentally a human activity and is defined by human attention, motivation, effort, and relationships.  We need teachers because we are motivated to make our greatest efforts for human beings with whom we have relationships and who hold our attention.

In the words of Richard Thaler, there are no Econs (see recommended piece in NYTimes).

May 25, 2015 at 7:30 am 5 comments

Two weeks in Germany: Human-centered software development and STEM Ed PhD students and Risk

I’m leaving May 24 for a two week trip to Germany. Both one week parts are interesting and worth talking about here. I’ve been reflecting on my own thinking on the piece between, and how it relates to computing education themes, too.

I’m attending a seminar at Schloss Dagstuhl on Human-Centric Development of Software Tools (see seminar page here). Two of the seminar leaders are Shriram Krishnamurthi of Bootstrap fame who is a frequent visitor and even a guest blogger here (see post here) and Amy Ko whose seminal work with Michael Lee on Gidget has been mentioned here several times (for example here). I’ve only been to Dagstuhl once before at the live-coding seminar (see description here) which was fantastic and has influenced my thinking literally years later. The seminar next week has me in the relative-outsider role that I was at the live-coding seminar. Most of the researchers coming to this event are programming language and software engineering researchers. Only a handful of us are social scientists or education researchers.

The Dagstuhl seminar ends Thursday after lunch. Saturday night, I’m to meet up with a group in Oldenburg Germany and then head up Sunday to Stadland (near the North Sea) for a workshop where I will be advising STEM Education PhD students. I don’t have a web link to the workshop, but I do have a page about the program I’ll be participating in — see here. My only contact there is Ira Diethelm, whom I’ve met several times and saw most recently at WIPSCE 2014 in Berlin (see trip report here). I really don’t know what to expect. Through the ICER DC and WIPSCE, I’ve been impressed by the Computing Education PhD students I’ve met in Germany, so I look forward to an interesting time. I come back home on Friday June 5 from Bremen.

There’s a couple day gap between the two events, from Thursday noon to Saturday evening. I got a bunch of advice on what to do on holiday. Shriram gave me the excellent advice of taking a boat cruise partway north, stopping at cities along the way, and then finishing up with a train on Saturday. Others suggested that I go to Cologne, Bremen, Luxembourg, or even Brussels.

I’ve decided to take a taxi to Trier from Dagstuhl, tour around there for a couple days, then take a seven hour train ride north on Saturday. Trier looks really interesting (see Tripadvisor page), though probably not as cool as a boat ride.

Why did I take the safer route?

The science writer, Kayt Sukel, was a once student of mine at Georgia Tech — we even have a pub together. I am so pleased to see the attention she’s received for her book Dirty Minds/This is Your Brain on Sex. She has a new book coming out on risk, and that’s had me thinking more about the role of risk in computing education.

In my research group, we often refer to Eccles model of academic achievement and decision making (1983), pictured below. It describes how students’ academic decisions consider issues like gender roles and stereotypes (e.g., do people who are like me do this?), expectation for success (e.g., can I succeed at this?), and the utility function (e.g., will this academic choice be fun? useful? money-making?). It’s a powerful model for thinking about why women and under-represented minorities don’t take computer science.

eccles-model

Eccles’ model doesn’t say much about risk. What happens if I don’t succeed? What do I need to do to reduce risk? How will I manage if I fail?  How much am I willing to suffer/pay for reduced risk?

That’s certainly playing into my thinking about my in-between days in Germany. I don’t speak German. If I get into trouble in those in-between days, I know nobody I could call for help. I still have another week of a workshop with a keynote presentation after my couple days break. I’ve already booked a hotel in Trier. I plan on walking around and taking pictures, and then I will take a train (which I’ve already booked, with Shriram’s help) to Oldenburg on Saturday. A boat ride with hops into cities sounds terrific, but more difficult to plan with many more opportunities for error (e.g., lost luggage, pickpockets). That’s managing risk for me.

I hear issues of risk coming into students’ decision-making processes all the time, combined with the other factors included in Eccles’ model. My daughter is pursuing pre-med studies. She’s thinking like many other pre-med students, “What undergrad degree do I get now that will be useful even if I don’t get into med school?” She tried computer science for one semester, as Jeanette Wing recommended in her famous article on Computational Thinking: “One can major in computer science and go on to a career in medicine, law, business, politics, any type of science or engineering, and even the arts.” CS would clearly be a good fallback undergraduate degree. She was well-prepared for CS — she had passed the AP CS exam in high school, and was top of her engineering CS1 in MATLAB class.  After one semester in CS for CS majors, my daughter hated it, especially the intense focus on enforced software development practices (e.g., losing points on homework for indenting with tabs rather than spaces) and the arrogant undergraduate teaching assistants. (She used more descriptive language.) Her class was particularly unfriendly to women and members of under-represented groups (a story I told here). She now rejects the CS classroom culture, the “defensive climate” (re: Barker and Garvin-Doxas). She never wants to take another CS course. The value of a CS degree in reducing risks on a pre-med path does not outweigh the costs of CS classes for her. She’s now pursuing psychology, which has a different risk/benefit calculation (i.e., a psychology undergraduate degree is not as valuable in the marketplace as a CS undergraduate degree), but has reduced costs compared to CS or biology.

Risk is certainly a factor when students are considering computer science. Students have expectations about potential costs, potential benefits, and about what could go wrong. I read it in my students’ comments after the Media Computation course.  “The course was not what I expected! I was expecting it to be much harder.” “I took a light load this semester so that I’d be ready for this.”  Sometimes, I’m quite sure, the risk calculation comes out against us, and we never see those students.

The blog will keep going while I’m gone — we’re queued up for weeks. I may not be able to respond much to comments in the meantime, though.

May 22, 2015 at 7:48 am 6 comments

Congratulations to Bill Wulf, 2014 ACM Karl V. Karlstrom Outstanding Educator Awardee

William Wulf is the 2014 recipient of the ACM Karl V. Karlstrom Outstanding Educator Award

Wulf is recognized for contributions as a teacher, author, and national leader who focused attention and changed the national education agenda and in the process supported the needs of underserved and under-represented students. As Assistant Director of the National Science Foundation’s Directorate for Computer and Information Science & Engineering (CISE), he understood the role NSF played in supporting science and engineering in the US for both basic research and operation of several high performance computing centers and networks. As President of the US National Academy of Engineering, he advocated for advances in engineering education and technical literacy. Wulf is professor emeritus of Computer Science at the University of Virginia. An ACM Fellow, he received the 2011 ACM Distinguished Service Award.

via Karl V. Karlstrom Outstanding Educator Award – ACM Award.

May 20, 2015 at 7:42 am Leave a comment

Using Learning Sciences to Inform Cyber Security Education

I’m giving the keynote talk at the 2015 International Security Education Workshop at Georgia Tech today. I’ve never spoken on cyber security before, so the talk was challenging and fun to put together. I used some of the learning sciences research we’ve done in computing education to draw connections to cyber security education. The lessons I highlight are:

  • Context matters.  People only learn when they understand why the learning is useful.
  • Identity matters.  People who reject computer science (and that’s most people) will likely reject cyber security education, even if they need to know it.  The cyber security learning that they need to know has to meet their identity and expectations. Don’t expect them to change who they are and what they think is important.
  • Structure matters.  Teaching something well, like using subgoal labeling, can dramatically improve learning.

 

(Click on the image below to get to the Slideshare site)

First slide of talk

May 18, 2015 at 9:30 am 6 comments

JES 5.02 Now Released, and Media Computation 4th Edition Slides Available

JES 5.02 is now released at https://github.com/gatech-csl/jes/releases/tag/5.020.  I have links to all the main downloads at http://mediacomputation.org .

This is a maintenance release.  Thanks to Nina Koch’s student, Henry, we have fixes for colorizing and some other problems.  Henry has also written code to allow for capture of keystrokes and mouse movement in a picture window, so that you could build some simple games.  I’ll save that for JES 5.10.
I don’t do JES releases that often, so in-between, I forget how painful cross-platform development is. JES is all written in Java and Jython, which means we “write once, test everywhere.”  I developed mostly on Mac OS X, but makensis (for Windows Installers) doesn’t work on Mac OS X.  So I ran Ubuntu in VirtualBox to build the Windows installer and test the Linux version.  But I still had to test on Windows (e.g., to make sure that the Sound Explorer was fixed).  It’s all details —  ls here vs. dir/w there, can’t install Oracle Java from Ubuntu installer, remembering sudo apt-get, Lenovo toolkit updater interrupting installations…
From Github:
 Fixes in JES 5.02 (with many thanks to HenryStevens and sportsracer48 for fixes):
  • Fixes a problem if you quit during raw_input that you can’t do another raw_input
  • Makes the Sound explorer fully functional again
  • Fixes colorizing
  • Makes autosave work again
  • Fixes a threading error if you hit return too rapidly in the Command Area
  • Unicode characters in input file get flagged. (Jython can’t execute a line with unicode characters on it.)

In addition,

You can find all the fourth edition Powerpoint slides (including a 68Mb zip of all of them) at http://coweb.cc.gatech.edu/mediaComp-teach/60. I’ve put some of the Peer Instruction question slides into the chapter PPT slide decks, but you can find more at the instructors-only website (see the Media Computation website for more on the teacher website).

Most of the changes are in the early chapters. Chapter 3 on text and language manipulation is all new. The latter chapter PPT slide decks have a few new slides in each deck, including:

  • Creating state-preserving versions of picture manipulation functions in Chapter 16 on Functional Programming
  • Subclassing Picture and Sound to move functions into methods in Chapter 17 on Object-oriented Programming
  • Recursive turtle patterns, which are possible with an improved Turtle class in JES 5 in Chapter 17

May 15, 2015 at 7:59 am 1 comment

How to Teach Computer Science with Media Computation

In the Preface to the new 4th ed book, I wrote a bit about what we know about how to teach computer science using Media Computation.  These are probably useful in most CS classes, even without Media Computation:

Over the last 10 years, we have learned some of the approaches that work best for teaching Media Computation.

  • Let the students be creative. The most successful Media Computation classes use open-ended assignments that let the students choose what media they use. For example, a collage assignment might specify the use of particular filters and com- positions, but allow for the student to choose exactly what pictures are used. These assignments often lead to the students putting in a lot more time to get just the look that they wanted, and that extra time can lead to improved learning.
  • Let the students share what they produce. Students can produce some beautiful pictures, sounds, and movies using Media Computation. Those products are more motivating for the students when they get to share them with others. Some schools provide online spaces where students can post and share their products. Other schools have even printed student work and held an art gallery.
  • Code live in front of the class. The best part of the teacher actually typing in code in front of the class is that nobody can code for long in front of an audience and not make a mistake. When the teacher makes a mistake and fixes it, the students see (a) that errors are expected and (b) there is a process for fixing them. Coding live when you are producing images and sounds is fun, and can lead to unexpected results and the opportunity to explore, “How did that happen?”
  • Pair programming leads to better learning and retention. The research results on pair programming are tremendous. Classes that use pair programming have better retention results, and the students learn more.
  • Peer instruction is great. Not only does peer instruction lead to better learning and retention outcomes, but it also gives the teacher better feedback on what the students are learning and what they are struggling with. We strongly encourage the use of peer instruction in computing classes.
  • Worked examples help with learning creativity. Most computer science classes do not provide anywhere near enough worked-out examples for students to learn from. Students like to learn from examples. One of the benefits of Media Computation is that we provide a lot of examples (we’ve never tried to count the number of for and if statements in the book!), and it’s easy to produce more of them. In class, we do an activity where we hand out example programs, then show a particular effect. We ask pairs or groups of students to figure out which program generated that effect. The students talk about code, and study a bunch of examples.

May 13, 2015 at 8:09 am 5 comments

Important paper at SIGCSE 2015: Transferring Skills at Solving Word Problems from Computing to Algebra Through Bootstrap

I was surprised that this paper didn’t get more attention at SIGCSE 2015.  The Bootstrap folks are seeing evidence of transfer from the computing and programming activities into mathematics performance.  There are caveats on the result, so these are only suggestive results at this time.

What I’d like to see in follow-up studies is more analysis of the students.  The paper cited below describes the design of Bootstrap and why they predict impact on mathematics learning, and describes the pre-test/post-test evidence of impact on mathematics.  When Sharon Carver showed impact of programming on problem-solving performance (mentioned here), she looked at what the students did — she showed that her predictions were met.  Lauren Margulieux did think-aloud protocols to show that students were really saying subgoal labels to themselves when transferring knowledge (see subgoal labeling post).  When Pea & Kurland looked for transfer, they found that students didn’t really learn CS well enough to expect anything to transfer — so we need to demonstrate that they learned the CS, too.

Most significant bit: Really cool that we have new work showing potential transfer from CS learning into other disciplines.

Many educators have tried to leverage computing or programming to help improve students’ achievement in mathematics. However, several hopes of performance gains—particularly in algebra—have come up short. In part, these efforts fail to align the computing and mathematical concepts at the level of detail typically required to achieve transfer of learning. This paper describes Bootstrap, an early-programming curriculum that is designed to teach key algebra topics as students build their own videogames. We discuss the curriculum, explain how it aligns with algebra, and present initial data showing student performance gains on standard algebra problems after completing Bootstrap.

via Transferring Skills at Solving Word Problems from Computing to Algebra Through Bootstrap.

May 11, 2015 at 7:44 am 8 comments

New AAUW Report: Solving the Equation: The Variables for Women’s Success in Engineering and Computing

Important new report from the American Association of University Women (AAUW).  I particularly like the detailed analysis of what happened at Harvey Mudd, with a lot of credit to Christine Alvarado as well as the other excellent faculty who created initiatives there.  As Maria Klawe keeps saying, it wasn’t just her.

More than ever before, girls are studying and excelling in science and mathematics. Yet the dramatic increase in girls’ educational achievements in scientific and mathematical subjects has not been matched by similar increases in the representation of women working as engineers and computing professionals. Just 12 percent of engineers are women, and the number of women in computing has fallen from 35 percent in 1990 to just 26 percent today.

The numbers are especially low for Hispanic, African American, and American Indian women. Black women make up 1 percent of the engineering workforce and 3 percent of the computing workforce, while Hispanic women hold just 1 percent of jobs in each field. American Indian and Alaska Native women make up just a fraction of a percent of each workforce.

via Solving the Equation: The Variables for Women’s Success in Engineering and Computing : AAUW: Empowering Women Since 1881.

May 8, 2015 at 8:14 am Leave a comment

Computing education that everyone needs but isn’t about learning programming

My colleague, Amy Bruckman, wrote a blog post about the challenges that nonprofits face when trying to develop and maintain software.  She concludes with an interesting argument for computing education that has nothing to do with learning programming that everyone needs.  I think it relates to my question: What is the productivity cost of not understanding computing? (See post here.)

This is not a new phenomenon. Cliff Lampe found the same thing in a study of three nonprofits. At the root of the problem is two shortcomings in education. So that more small businesses and nonprofits don’t keep making this mistake, we need education about the software development process as part of the standard high-school curriculum. There is no part of the working world that is not touched by software, and people need to know how it is created and maintained. Even if they have no intention of becoming a developer, they need to know how to be an informed software customer. Second, for the people at web design firms who keep taking advantage of customers, there seems to be a lack of adequate professional ethics education. I teach students in my Computers, Society, and Professionalism class that software engineers have a special ethical responsibility because the client may not understand the problem domain and is relying on the knowledge and honesty of the developer. More people need to get that message.

via Dear Nonprofits: Software Needs Upkeep (Why we need better education about software development and professional ethics) | The Next Bison: Social Computing and Culture.

May 6, 2015 at 7:53 am 3 comments

Creating a SIG for Computer Science Education at AERA

Hilary Dwyer is starting an effort to create a special interest group at the American Educational Research Association (AERA) just for computer science education.  Please do complete her poll — especially if you are an AERA member.  We need 75 signatures on the poll to be able to create the SIG.

A few of us have been talking about creating a larger presence for computer science education within AERA.  In particular, we want to start a special interest group (SIG).  Please take a few moments to fill out the following survey.  I am trying to determine if there are enough people to start a SIG (we need 75), the benefits of doing so, and how a SIG would differ from ICER or SIGCSE.
Poll: Creating a SIG for Computer Science Education at AERA
Creating and sustaining a CS Education community within AERA could be exciting work to further establish the field.  I would appreciate a forum to talk with researchers from Education who study computer science — a view not necessarily captured well at other venues right now.  We could also tailor such a group to K-12 computer science and other research topics such as professional development and teacher learning.
Please email me with any questions or comments. Forward on to your co-authors, advisors, colleagues, and other AERA members that could be interested!
Many thanks,

Hilary Dwyer

Hilary A. Dwyer, PhD
Postdoctoral Research Associate
Gevirtz Graduate School of Education
University of California, Santa Barbara

May 4, 2015 at 8:15 am Leave a comment

Sally Fincher on the need for CER: What Are We Doing When We Teach Computing in Schools?

I’ve been looking forward to seeing this article appear in CACM for over a year.  Last January and May, I heard Sally Fincher give two talks about computing education research (CER), where she started by describing (failed) efforts to teach reading over the last hundred years.  She created a compelling analogy.  What educators were doing when they simplified the learning of reading seem analogous to our efforts today to simplify the learning of programming — but those efforts to teach simplified reading led to significant harm to the students.  What harm are we doing to students when we teach programming in these new ways?  She is not calling for an end to these efforts.  Rather, she’s calling for research to figure out what we’re doing and to investigate the effects. She agreed to write up her story for Viewpoints, which is published this month in CACM. Thanks, Sally!

Other approaches believe it is more appropriate to use real syntax, but constrain the environment to a particular (attractive) problem domain so learners become fluent in a constrained space. Event-driven environments (such as Greenfoot) or scaffolded systems (like Processing.js) aim for the learner to develop an accurate mental model of what their code is doing, and ultimately transfer that to other environments. Although whether they actually do so remains unclear: we may be restricting things in the wrong way.

Still others hold that coding—howsoever approached—is insufficient for literacy and advocate a wider approach, taking in “computational thinking,” for instance as embedded in the framework of the “CS Principles”: Enduring Understandings, Learning Objectives, and Essential Knowledge.

What is resolutely held common with traditionally formulated literacy is that these approaches are unleashed on classrooms, often whole school districts, even into the curriculum of entire countries—with scant research or evaluation. And without carrying the teachers. If we are to teach computing in schools we should go properly equipped. Alongside the admirable energy being poured into creating curricular and associated classroom materials, we need an accompanying set of considered and detailed programs of research, to parallel those done for previous literacies.

via What Are We Doing When We Teach Computing in Schools? | May 2015 | Communications of the ACM.

May 1, 2015 at 8:21 am 8 comments


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