Posts tagged ‘MOOCs’
ICER 2015 (see website here) is August 9-13 in Omaha, Nebraska. The event starts for me and Barbara Ericson, Miranda Parker, and Briana Morrison on Saturday August 8. They’re all in the Doctoral Consortium, and I’m one of the co-chairs this year. (No, I’m not a discussant for any of my students.) The DC kickoff dinner is on Saturday, and the DC is on Sunday. My thanks to my co-chair Anthony Robins and to our discussants Tiffany Barnes, Steve Cooper, Beth Simon, Ben Shapiro, and Aman Yadav. A huge thanks to the SIGCSE Board who fund the DC each year.
We’ve got two papers in ICER this year, and I’ll preview each of them in separate blog posts. The papers are already available in the ACM digital library (see listing here), and I’ll put them on my Guzdial Papers page as soon as the Authorizer updates with them.
I’m very excited that the first CSLearning4U project paper is being presented by Barbara on Tuesday. (See our website here, the initial blog post when I announced the project here, and the announcement that the ebook is now available). Her paper, “Analysis of Interactive Features Designed to Enhance Learning in an Ebook,” presents the educational psychology principles on memory and learning that we’re building on, describes features of the ebooks that we’re building, and presents the first empirical description of how the Runestone ebooks that we’re studying (some that we built, some that others have built) are being used.
My favorite figure in the paper is this one:
This lists all the interactive practice elements of one chapter of a Runestone ebook along the horizontal axis (in the order in which they appear in the book left-to-right), and the number of users who used that element vertically. The drop-off from left-to-right is the classic non-completion rate that we see in MOOCs and other online education. Notice the light blue bars labelled “AC-E”? That’s editing code (in executable Active Code elements). Notice all the taller bars around those light blue bars? That’s everything else. What we see here is that fewer and fewer learners edit code, while we still see learners doing other kinds of learning practice, like Parsons Problems and multiple choice problems. Variety works to keep more users engaged for longer.
A big chunk of the paper is a detailed analysis of learners using Parsons Problems. Barbara did observational studies and log file analyses to gauge how difficult the Parsons problems were. The teachers solved them in one or two tries, but they had more programming experience. The undergraduate and high schools students had more difficulty — some took over 100 tries to solve a problem. Her analysis supports her argument that we need adaptive Parsons Problems, which is a challenge that she’s planning on tackling next.
We’re years into the MOOC phenomenon, and I’d hoped that we’d get past MOOC hype. But we’re not. The article below shows the same misunderstandings of learning and teaching that we heard at the start — misunderstandings that even MOOC supporters (like here and here) have stopped espousing.
The value of being in the front row of a class is that you talk with the teacher. Getting physically closer to the lecturer doesn’t improve learning. Engagement improves learning. A MOOC puts everyone at the back of the class, listening only and doing the homework.
In many ways, we have a romanticized view of college. Popular portrayals of a typical classroom show a handful of engaged students sitting attentively around a small seminar table while their Harrison Ford-like professor shares their wisdom about the world. We all know the real classroom is very different. Especially in big introductory classes — American history, U.S. government, human psychology, etc. — hundreds of disinterested, and often distracted, students cram into large impersonal lecture halls, passively taking notes, occasionally glancing up at the clock waiting for the class to end. And it’s no more engaging for the professor. Usually we can’t tell whether students are taking notes or updating their Facebook page. For me, everything past the ninth row was distance learning. A good online platform puts every student in the front row.
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.
These do sound like the kinds of things that learning scientists were saying at the start of the MOOC hype (like this post), but I’m glad that he now realizes that MOOCs have limited use and that students vary widely.
And as for MOOCs, which many still predict will displace traditional teaching, he said that they “were the answer when we weren’t sure what the question was.”
He said that their massive nature, which attracted so much attention, was ultimately a problem. “When I think about MOOCs, the advantage — the ability to prepare a course and offer it without personal interaction — is what makes them inexpensive and makes them very limited.”
Students “vary widely in terms of their skills and capability,” he said, such that massiveness is simply not an educational advantage. “For some it’s too deep and for some it is too shallow.”
Really great news: The Google CS4HS program is again open to face-to-face professional development! Last year, they only offered MOOC-based PD (see blog post here). The new call is backed up with research, so that the CS4HS programs are designed to be more effective. (I suspect that Chris Stephenson’s move to Google had something to do with this…)
Google believes the new Advanced Placement Computer Science Principles (CSP) course being developed by the National Science Foundation and the College Board is key to engaging a more diverse audience of students in computer science. Adoption and exemplary teaching of this course requires a community-wide effort to prepare teachers. To that end, in 2015 the CS4HS program will be providing grants to universities and educational non-profits interested in helping their local teacher community prepare to teach CSP.
Research (Joyce & Showers, 2002; Wiske, Stone, & Levinson, 1993) shows that peer-to-peer professional development and on-going support improve teachers’ abilities to adopt and implement new content and skills. Based on this research Google’s intention in 2015 is to provide funding support for:
- professional development workshops (face to face and online) focused on CSP
- establishment of or work with existing communities of practice (COP) that will support ongoing professional development and advocacy for CSP on an ongoing basis.
Applicants must satisfy the following criteria in order to be eligible:
You must be affiliated with a college, university, technical college, community college, or an official non-profit organization focused on education.
Your workshop must have a clear focus on the College Board’s new AP Computer Science Principles curriculum.
Your workshop must be followed up with a plan for year-round communities of practice work that supports ongoing PD and advocacy for the Computer Science Principles curriculum.
Online courses must use Google products for content delivery.
Online courses must be massive, open, and online; therefore enrollment cannot be capped.
A nice piece updating what we know about MOOCs, who’s taking them, and what they’re good for. I have decided to offer my first MOOC, as part of an HCI specialization with Coursera. (See the announcement here.) This fits in exactly with what I think a MOOC is good for — it’s professional development for people with background in the field. If students going to learn about HCI, I’d also like them to learn about making technologies for learning and about how people learn. I agreed to do a short four week MOOC on designing learning technologies, development to occur this summer. This isn’t about my research exactly (though, because it’s me, a lot of the examples will probably come from computing education). It’s not about reaching an under-served population, or teaching CS-novices or teachers. Different purpose, different objectives — and objectives for this course and for the GT HCI specialization match for what a MOOC is good for.
The companies that rode to fame on the MOOC wave had visions and still do of offering unfettered elite education to the masses and driving down college tuition. But the sweet spot for MOOCs is far less inspirational and compelling. The courses have become an important supplement to classroom learning and a tool for professional development.
Annie Murphy Paul has a nice article about autodidacts — yes, there are some, but most of us aren’t. MOOCs are mostly for autodidacts. The paper from Educational Psychologist is excellent, and I reading the original as well as Paul’s review.
In a paper published in Educational Psychologist last year, Jeroen J.G. van Merriënboer of Maastricht University and Paul A. Kirschner of the Open University of the Netherlands challenge the popular assumption “that it is the learner who knows best and that she or he should be the controlling force in her or his learning.”
There are three problems with this premise, van Merriënboer and Kirschner write. The first is that novices, by definition, don’t yet know much about the subject they’re learning, and so are ill equipped to make effective choices about what and how to learn next. The second problem is that learners “often choose what they prefer, but what they prefer is not always what is best for them;” that is, they practice tasks that they enjoy or are already proficient at, instead of tackling the more difficult tasks that would actually enhance their expertise. And third, although learners like having some options, unlimited choices quickly become frustrating—as well as mentally taxing, constraining the very learning such freedom was supposed to liberate.