Posts tagged ‘learning science’
There is a sense of vindication that the predictions that many of us made about MOOCs have been proven right, e.g., see this blog post where I explicitly argue (as the article below states) that MOOCs misunderstand the importance of active learning. It’s disappointing that so much effort went wasted. MOOCs do have value, but it’s much more modest than the sales pitch.
What accounts for MOOCs’ modest performance? While the technological solution they devised was novel, most MOOC innovators were unfamiliar with key trends in education. That is, they knew a lot about computers and networks, but they hadn’t really thought through how people learn.
It’s unsurprising then that the first MOOCs merely replicated the standard lecture, an uninspiring teaching style but one with which the computer scientists were most familiar. As the education technology consultant Phil Hill recently observed in the Chronicle of Higher Education, “The big MOOCs mostly employed smooth-functioning but basic video recording of lectures, multiple-choice quizzes, and unruly discussion forums. They were big, but they did not break new ground in pedagogy.”
Indeed, most MOOC founders were unaware that a pedagogical revolution was already under way at the nation’s universities: The traditional lecture was being rejected by many scholars, practitioners, and, most tellingly, tech-savvy students. MOOC advocates also failed to appreciate the existing body of knowledge about learning online, built over the last couple of decades by adventurous faculty who were attracted to online teaching for its innovative potential, such as peer-to-peer learning, virtual teamwork, and interactive exercises. These modes of instruction, known collectively as “active” learning, encourage student engagement, in stark contrast to passive listening in lectures. Indeed, even as the first MOOCs were being unveiled, traditional lectures were on their way out.
Research+Practice Partnerships and Finding the Sweet Spots: Notes from the ECEP and White House Summit
I wrote back in October about the summit on state implementation of the CS for All initiative which we at Expanding Computing Education Pathways (ECEP) alliance organized with the White House Office of Science and Technology Policy (OSTP). You can see the agenda here and a press release on the two days of meetings here.
I have been meaning to write about some of the lessons I learned in those two days, but have been simply slammed this month. I did finally write about some of the incremental steps that states are taking towards CS for All in my Blog@CACM post for November. That post is about the models of teacher certification that are developing, the CSNYC school-based mandate, and New Hampshire’s micro-certifications.
In this post, I want to tell you about a couple of the RPC ideas that I found most compelling. The first part of the day at the Eisenhower Executive Office Building (EEOB) on the White House grounds was organized by the Research+Practice Collaboratory (RPC). I was the moderator for the first panel of the day, where Phil Bell, Nichole Pinkard, and Dan Gallagher talked about the benefits of combining research plus practice.
I was excited to hear about the amazing work that Nichole Pinkard (pictured above) is doing in Chicago, working with Brenda Wilkerson in Chicago Public Schools. Nichole is a learning scientist who has been developing innovative approaches to engaging urban youth (see her Digital Youth Network website). She has all these cool things she’s doing to make the CS for All efforts in Chicago work. She’s partnering with Chicago parks and libraries — other than schools, they’re the ones who cover the city and connect with all kids. She’s partnering with Comcast to create vans that can go to parks to create hotspots for connectivity. Because she’s a researcher working directly with schools, they can do things that researchers alone would find hard to do — like when a student shows up to a CS activity, she can email the student’s parents to tell them the next steps to make sure that they continue the activity at home.
There was a second panel on “Finding the Sweet Spot: What Problems of Practice are Ripe for Knowledge Generation?” I didn’t know Shelley Pasnik from the Center for Children and Technology, and she had an idea I really liked that connected to one of Nichole’s points. Shelley emphasizes “2Gen learning,” having students bring with them parents or even grandparents so that there are two generations of learners involved. The older generation can learn alongside the student, and keep the student focused on the activity.
I know that the RPC folks are producing a report on their activity at the summit, so I’m sure we’ll be hearing more about their work.
Making learning effective, efficient, and engaging: An Interview With an Educational Realist and Grumpy Old Man, Paul Kirschner
I am a fan of Paul Kirschner‘s work. This interview is great with useful insights about education — deep and pragmatic thinking.
I want to fundamentally understand how people can learn in effective, efficient, and enjoyable ways, and how you can teach and design learning materials to achieve this objective. If a learner doesn’t enjoy the learning experience, even if it’s effective and/or efficient, they won’t do it. The same is true for teaching: that is it must also be effective, efficient, and enjoyable for the teacher because if a teacher doesn’t enjoy the teaching process, even if it’s effective and/or efficient, they won’t do it.
The below-linked article does a good job of considering the argument about whether everyone can learn to program, and comes to the same conclusion that I do — a CS teacher must always believe that everyone can learn to program.
Indeed, one can find a good number of opinionators weighing in on the subject. In “Separating Programming Sheep from Non-Programming Goats,” Stack Exchange co-founder Jeff Atwood cites Bornat’s initial paper and concludes, “the act of programming seems literally unteachable to a sizable subset of incoming computer science students.” Linux creator Linus Torvalds has been quoted as saying, “I actually don’t believe that everybody should necessarily try to learn to code” — although, he does propose that people be exposed to it to see if they have “the aptitude.” Clayton Lewis of the University of Colorado at Boulder conducted a survey in which 77% of responding computer science faculty strongly disagreed with the statement “Nearly everyone is capable of succeeding in the computer science curriculum if they work at it.” As a “bright-eyed beginner” (with a scant 15 years of introductory programming teaching under my belt), it’s hard for me to accept the assertion that there are “some who can’t.” Such reasoning smacks of elitism and prejudice, even if such attitudes aren’t expressed consciously.
Of course, I’ll be the first to admit that my own opinion rests heavily on my own preconceptions: I’ve always had that “Montessori feeling” — every interested student should be given a chance to try, and sometimes fail, in a supportive environment.So, rather than give up on some, shouldn’t educators themselves keep trying? The inverse to the question “are there some students who can’t learn?” is this question, “are there some students whom our (current) teaching methods can’t reach?” The first question by itself implies a “yes,” and thus closes a door on some students. The second question opens up a world of inquiry: if basic coding concepts are truly so simple (as they truly are once the abstraction is understood), what do we need to do to bring the hard cases home?
My PhD advisor, Elliot Soloway, considers a new report on the value of computers in education, and gets to the bottomline. To swipe a line from Bill Clinton, “It’s the pedagogy, stupid!” Of course, I agree with Elliot, and it’s why Lecia Barker’s findings are so disturbing. We have to be willing to change pedagogy to improve learning.
The findings are the findings, but what is really interesting is a statement that Andreas Schleicher, the director of OECD, made as to why the impact of technology is negative. In the foreword to the OECD report, he writes, “…adding 21st century technologies to 20th century teaching practices will just dilute the effectiveness of teaching.”WOW! In this one sentence, Schleicher names clearly what he sees as the root cause of the lack of technology’s impact on student achievement. While the NYT’s articles danced around the issues, Schleicher doesn’t pull any punches: The reason computers are not having a positive impact lies in the use of outmoded teaching practices that do not truly exploit the opportunities that a 1-to-1 classroom affords.
Little Evidence That Executive Function Interventions Boost Student Achievement: So why should computing?
Here’s how I interpret the results described below. Yes, having higher executive function (e.g., being able to postpone the gratification of eating a marshmallow) is correlated with greater achievement. Yes, we have had some success teaching some of these executive functions. But teaching these executive functions has not had any causal impact on achievement. The original correlations between executive function and achievement might have been because of other factors, like the kids who had higher executive function also had higher IQ or came from richer families.
This is relevant for us because the myth that “Computer science teaches you how to think” or “Computer science teaches problem-solving skills” is pervasive in our community. (See a screenshot of my Google search below, and consider this blog post of a few weeks ago.) But there is no support for that belief. If this study finds no evidence that explicitly teaching thinking skills leads to improved transferable achievement, then why should teaching computer science indirectly lead to improved thinking skills and transferable achievement to other fields?
Why do CS teachers insist that we teach for a given outcome (“thinking skills” or “problem-solving skills”) when we have no evidence that we’re achieving that outcome?
The meta-analysis, by researchers Robin Jacob of the University of Michigan and Julia Parkinson of the American Institutes for Research, analyzed 67 studies published over the past 25 years on the link between executive function and achievement. The authors critically assessed whether improvements in executive function skills—the skills related to thoughtful planning, use of memory and attention, and ability to control impulses and resist distractions—lead to increases in reading and math achievement , as measured by standardized test scores, among school-age children from preschool through high school. More than half of the studies identified by the authors were published after 2010, reflecting the rapid increase in interest in the topic in recent years.
While the authors found that previous research indicated a strong correlation between executive function and achievement, they found “surprisingly little evidence” that the two are causally related.
“There’s a lot of evidence that executive function and achievement are highly correlated with one another, but there is not yet a resounding body of evidence that indicates that if you changed executive functioning skills by intervening in schools, that it would then lead to an improvement in achievement in children,” said Jacob. “Although investing in executive function interventions has strong intuitive appeal, we should be wary of investing in these often expensive programs before we have a strong research base behind them.”
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.