Paradigm shifts in education and educational technology: Influencing the students here and now

Back on my last blog post referencing Morgan Ames’ book The Charisma Machine, Alan Kay said in a comment, “What we have here is a whole world view and a whole different world.” I’ve been thinking about that sentence a lot because it captures what I think is going on here. A Kuhnian paradigm shift is happening (and maybe has already happened) in research around education and educational technology from the world of Papert and Bruner to the world of learning sciences. I am going to take a pass at describing the change that I see happening in the field, but I encourage you also to read the International Society of the Learning Science (ISLS) presidential address from Victor Lee here, which describes the field more authority and with more authenticity than me.

I remember asking Janet Kolodner (first editor-in-chief of the Journal of the Learning Sciences), “Why? Why learning sciences? We have educational psychology and cognitive science and so many other education disciplines.” She said that learning scientists were tired of just knowing what should happen. They wanted to get out to influence education practice and understand why learning doesn’t happen. Cognitive scientists mostly (at the time) ignored affect and motivation. Educational psychologists most often worked in controlled laboratories or experimental classrooms. Learning scientists wanted to understand and influence what was really happening in educational contexts, both formal and informal. More, they were devoted to expanding access to high-quality education. Yes, learning scientists explored cutting edge technologies to see what was possible, but even more, we try to figure out contexts that make or inhibit learning for real kids. Look at the titles of the Invited Speakers at ICLS 2020: Lost and found in dialogue: Embracing the promises of interdiscursivity and diminishing its risks, The Ed-Tech Imaginary, and Learning as an Act of Fugitivity. Words like “promises” and “imaginary” and “fugitivity” reflect a desire to change and to respond to what we thought might be, but discovered that reality is different. (Audrey Watters’ keynote is available as an essay here.)

David Feldon told me once that the field is misnamed. It’s much more “Learning humanities” than “Learning sciences.” Once you decide to study what’s going on in actual practice with actual students, you find that you’re mostly in studies with really small n. Contexts, teachers, and students vary wildly. Nobody that I know in learning sciences is trying to invent a general dynamic medium for thought, because it’s so hard to get anything actually adopted and used in an impactful manner. I see Jim Spohrer’s work in Service Science as being part of the same paradigm — how do you actually get services designed and implemented that work in practice?

This shift from the general to the specific, and from what could work to what does work is true in my research too. One of my recent NSF proposals is about working closely with a particular school district to figure out what is going to work there. What we know about Brookline or Brasil is almost irrelevant for this district. Another proposal is about inventing a dynamic medium for thought — but in a particular set of classes, in a task-specific form. I still would love to have a general dynamic medium for thought (as Alan suggests), but I believe we have to figure it out from the ground up. Over time, we will find specific notations that can work for specific tasks, and generalize as possible from there.

The majority of the literature that I draw on these days is about teachers: how they learn, why pre-service education has so little influence on actual teacher practice, and how to influence adoption. Teachers are a gateway for technology in the classroom. There are lots of technologies that could work with kids, but don’t work with teachers. In my work today, I draw on Bruner and Papert for their theoretical framings.. I draw on Bruner’s laboratory-based work (e.g., his definition of scaffolding). I draw on Papert’s descriptions of what the computer offers learners, e.g., its protean nature. But I draw less on their implementation work. Bruner’s MACOS was a brilliant project that had a catastrophic result because they didn’t consider enough what would actually work in US schools. Papert created interesting interventions that didn’t become systemic or sustained. Ames is telling me what’s going wrong in actual implementations of OLPC and may some of why it went wrong. If I want things to be actually adopted, I need to avoid the mistakes that The Charisma Machine is describing.

David’s description of what happened in Brasil in a comment to that earlier blog post is fascinating and super-useful, but doesn’t decrease the value of Ames’ description in Paraguay. I don’t agree with all of her rationalizations of why things turned out as they did (e.g., I don’t find the “technically precocious boys” perspective compelling or having explanatory power), and there are very likely things she missed. But what she describes obviously did happen. Learning from the experiences she describes informs our design processes and iterative feedback loops as a way of improving outcomes.

Like any paradigm shift, it doesn’t mean that all the work that went before is wrong. The questions being asked in each paradigm are different. They start from different world views. Papert and Bruner both offer a vision of what we want, Logo and MACOS. Both ran up against the reality of school in the US, where Thorndike won and Dewey lost. Now, how do we help every student, in real school contexts?

Nathan Holbert and David Weintrop recently told me a great phrase that’s common in the constructionism community (variously attributed to Seymour Papert or Uri Wilensky): “Are you designing for Someday or are you designing for Monday?” Are you designing for a world that might be, or are you designing for things that can go in the classroom soon? Neither are wrong. I don’t think that they even need to be a dichotomy. In my task-specific programming work, I’m making things that can’t go in the classroom Monday, but could go in the classroom next year, which is still a lot closer than Someday. Even to be in the classroom next year, I have to start from where schools are now. There won’t be a Dewey-an revolution in schools over the next year. But maybe Someday there will.

Computational Mapping: An important set of skills in Computational Thinking we can define and test

Last month, I blogged for CACM about a “Twitter convo” (conversation) that I was part of recently, Computational Thinking, education for the poor and rich, and dealing with schools and teachers as they are: A Twitter Convo #doesComputationalThinkingExist.

Part of that conversation was a series of tweets about meanings and alternative terms for computational thinking.

• Lorena Barba tweeted a thread with different definitions of computational thinking, including many she disagreed with and a few that she recommended to us.
• Mitchel Resnick tweeted that they use “computational fluency” to recognize the importance of students developing their voice and identity — “the importance of having the ability to create and express oneself with digital technologies”
• Mark Sherman said that his group at MIT is using the phrase “computational action” to think about how people can use computing to “take action on local, authentic problems, and make a difference.”
• Not in the convo but relevant is Yasmin’s Kafai’s argument to shift computational thinking to computational participation (see CACM piece here) which changes the focus to the social context and the interaction with others around use of computing. (Thanks to Ben Shapiro for pointing out the connection.)

Shuchi Grover emphasized in this convo that she sees CT as the connection between programming and other kids of thinking skills. The skills that she’s promoting and teaching are critical to the use of computing in science, for example (as she talked about here). I think she has her finger on an important set of skills, but I don’t think that they’re “computational thinking” by any of the most popular and often-used definitions for CT. CT has a set of meanings associated with it. W are more likely to hide a good set of concepts behind a vague term than to get the term re-associated with a good, new meaning.

Here’s a proposal for a name for at least part of what Shuchi is promoting and teaching: Computational mapping. Computational mapping is about making an association between computational representations and objects in other domains. Computational mapping skills include using the computational representations to improve understanding of and predictions in the other domains. Computational mapping skills should also include recognizing the limitations of the computational representation, when the mapping is missing critical characteristics of the objects in the domain which limit our understanding and predictive capabilities.

Most computer programs are mapping from the real world (that is continuous and filled with complex and real numbers) to a set of discrete values that can be represented in bits.  There is not a 1:1 relationship between the real world and the computational world. Whenever we create a mapping, we may be capturing exactly the right things (e.g., if you want to simulate projectile motion, position and time is all you really need), but it’s more likely a mismatch, though many times the mismatch is not something we worry about.  Our RGB color scheme can’t capture all colors, or all colors that butterflies can see, but that’s okay — we’re just making colors for humans.

I see computational mapping skills in lots of the things I think about today.

• When Seymour Papert was first promoting the Logo turtle, he talked about it being “body-syntonic.” You could map the turtle to your knowledge and sense about one’s own body. That’s making programming easier through computational mapping skils
• I attended a fascinating Engineering Education Research session where we talked about Julie Gainsburg’s work on engineering judgement. I particularly like one of her paper titles, “Developing skeptical reverence for mathematics.” When is a mathematical or computational model a good enough or precise enough estimation? When you get a result that you didn’t expect, when should you question the implementation of your model, and when should you realize that the results are telling you something new? Engineers and scientists have to do this all the time, and it’s harder when the result is coming out of a computer, looks really slick (high-quality graphs!), and has many decimal places.
• Joy Buolamwini’s terrific TED talk and Algorithmic Justice League are pointing out that our algorithms for facial recognition are missing critical characteristics of faces that we want to be able to recognize.  To even realize that that can happen, that you can come up with computational recognition systems that aren’t recognizing the parts of the world that you need, is part of computational mapping skills.
• The use of types in programming languages are really an exercise in computational mapping. Does this type really capture the characteristics of the objects and data that I need? When are my types mismatched? When are my types insufficient, e.g., are floating point numbers really the same as real numbers?
• I recently told Alan Kay about some work I’m doing in using simulations for teaching social science. He cautioned me that it’s too easy to get the mapping wrong when modeling social science concepts on a computer. Physical situations (like projectile motion) are more exactly mapped and are a better place to learn through using computational modeling. He’s right, of course. But if we modeled physical situations in elementary/middle school, then high school would be a good place to explore the limitations of computational modeling and simulation, and social science is a good place for that.

CT is such a big and vague term that I’m not sure that it’s useful anymore. We’re better off coming up with new terms (like Mitchel and Mark are doing) for the exact skills we are aiming to teach. Let’s spend our time studying the things that we think are important and that we can actually define. Both the power and limitations of computational modeling feel like something that all citizens of the 21st century should be aware of.

Does Your Teaching Encourage Epistemological Pluralism?

Turkle and Papert’s paper on epistemological pluralisms is one of my favorite by Seymour (which I talked about here).  This is the first paper I’ve read about how to encourage them.

I remember a math teacher I once had. He would ask students to go up to the board and explain how they solved the problem. But he wouldn’t stop there. He would then ask if someone else had a different way of solving the problem and allow the different approaches to be shared with the class. This validated that there were multiple ways a problem can be solved, and that it was not enough to know just one way… It also meant no one remained in doubt about whether their (different) approach was “incorrect” (there was room to clear up misconceptions, for example). It’s not as deep as epistemology, but it’s a start. A start to plurality of the “how”, but we should consider maybe also the plurality of the “what” and “why” (because which questions we choose to pursue for learning and why they matter to us are deep ontological and epistemological questions).

Source: Does Your Teaching Encourage Epistemological Pluralism? – ProfHacker – Blogs – The Chronicle of Higher Education

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).

MOOCs get schoolified: Two reports predict MOOCs will simply be absorbed

Seymour Papert might have predicted this.  It doesn’t matter if they’re great or not.  It is very hard for educational technology to disrupt school.  School fights back, and schoolifies subjects and technologies.  I said before: Education is technology’s Afghanistan.  Lots of technologies have come in and tried to change everything, and the technologies come out limping.

Massive open online courses will not fundamentally reshape higher education, nor will they disappear altogether. Those are the conclusions of separate reports released this week by Teachers College at Columbia University and Bellwether Education Partners, a nonprofit advisory group.

Neither report contains any blockbuster news for those who have followed the decline of the MOOC hype over the last year or so. But they support the theory that the tools and techniques Stanford University professors used in 2011 to enroll 160,000 students in a free, online computer-science course will be subsumed by broader, incremental efforts to improve higher education with technology.

MOOCs are like free gyms, says Mr. Kelly. They might enable some people—mostly people who are already healthy and able to work out without much guidance—to exercise more. But they won’t do much for people who need intensive physical therapy or the care of a doctor.

via Conventional Online Higher Education Will Absorb MOOCs, 2 Reports Say – Wired Campus – Blogs – The Chronicle of Higher Education.

Constructionism for Adults

Constructionism–the N word as opposed to the V word–shares constructivism’s connotation of learning as “building knowledge structures” irrespective of the circumstances of the learning. It then adds the idea that this happens especially felicitously in a context where the learner is consciously engaged in constructing a public entity, whether it’s a sand castle on the beach or a theory of the universe.

• Seymour Papert and Idit Harel “Situating Constructionism”

Most researchers exploring constructionism study children. Mitchel Resnick, Yasmin Kafai, Uri Wilensky, Amy Bruckman, Idit Harel, and other academic offspring of Seymour Papert have studied how children learn through construction in a variety of media, from Scratch to e-textiles. The semi-annual Constructionism and Creativity Conference talks about “students” not “children” on the Constructionism history page, but the proceedings from the 2012 conference show that it’s about children’s learning, both formal and informal.

I’ve grown up constructionist-by-association, rather than by training. I got to work with Seymour and with Mitchel for a short time on the design for LCSI Microworlds. Yasmin is one of my oldest friends, from even before she went to work with Idit and Seymour. I worked from a constructionist perspective here at Georgia Tech with Amy Bruckman and Janet Kolodner.

Nowadays, I work mostly with adult learners — undergraduates, end-user programmers, and high school teachers. There’s nothing in Seymour’s definition that prohibits applying constructionism to adults. Their learning should be “especially felicitous” when they are “constructing a public entity.” But I don’t think that constructionism for adults is the same as constructionism for children.

I can identify examples (as an existence proof) that constructionism can work for adults as well as children.

• Teachers know that if you want to learn a new subject, sign up to teach the new subject. Constructing the course and teaching it to others is a great way of developing that knowledge.
• Programmers take on new projects to learn a new method, language, context, or community. My former PhD student, Mike Hewner, wanted to know what professional game development was like. Because he’s an exceptional software engineer, he was able to land himself an internship with a game company one summer (with no prior game experience), explicitly to learn game development.

I see three big differences in adult constructionism from child constructionism, and they’re related.

(1) Saving Face I’m learning to play the ukulele. I bought it about a few months ago, and am playing it daily. I’m learning a huge amount, both in terms of the skill and concepts needed to play, but also at a meta level about music. The ukulele makes me think about timing, strumming, and chord patterns in a different way, and now I listen to all kinds of stringed instruments in a different way. It’s helping me to sing better, since I can more easily hear when I’m at the wrong pitch and I hear rhythm differently when I’m strumming.

But I am not learning to play ukulele as a public artifact. I’m frightened by the thought of playing in public. Only my family has ever heard me play.

Adele Goldberg worked on one of the iterations of the UK Open University’s introductory computing course, and she told me that distance learning opportunities were most important for adults. She pointed out that adults work for decades to develop their careers and their prestige. It’s really hard for them to then put their hands up in a physical classroom to ask a question and risk being found out as not knowing.  There’s a recent Freakonomics podcast that claims that the three hardest words to say in the English language are: “I don’t know.”

Constructionism for kids is all about the public aspect. The Scratch website plays a role in students sharing their work, downloading others’ projects, remixing and sharing back what they found. Collaboration and public sharing has always played a big role in stories of constructionist learning.  Maybe this is why work in Constructionism tends to focus at the youngest children, because the social standing and peer pressure issues increase as the kids get older.

Adults have face in a different way than children. We can still learn from construction, but we might not want it to be as public in the same way as children. We might not want to even publicly remix, or others might learn what we’re doing.

(2) Presumption of Expertise I’ve mentioned before in this blog that I’ve been singing in my church choir. I often feel ignorant — and embarrassed at my ignorance. There is so much about singing in a choir that is assumed when you are an adult, from how to sing into a microphone to how to harmonize by hearing the melody. We teach these things to children, because we know that they don’t have the basics.  We expect them to be novices at most things.

As an adult engaging in an activity, we are presumed not to be novices. If you sing in a choir, the assumption is that you must have sung in choirs before –“You all know the basics.” But if you’re starting out in a new domain, you may not. Even when I admit my ignorance (hard to do because of the issue of face) and ask questions, the director quickly forgets my lack of background — a couple things get explained, and then the presumption of expertise comes back. I look like all the other adults there. It’s not like a classroom of similarly-aged students where the teacher can assume a similar background. Adults have radically different backgrounds. I recently served on the advisory board for a science and engineering learning project that used Lego robotics context.  The most common teacher professional development question was about the Lego.  These teachers had not played with Lego as children, were uncomfortable with it, and had to spend extra (unexpected from the researchers’ perspective) time to learn to use Lego.

Constructionism depends on learning in the context of construction.  The goal of the learning isn’t the construction itself.  It’s construction as something to think with.  As Seymour put it, you can’t think about thinking without thinking about something.  But if you don’t know how to construct, then most of the activities of construction don’t fall into the background, and then it’s hard to think about the artifact being constructed and to learn from that process.  Children learn through Lego and Scratch after they get the basics of how to put blocks together (in both physical and virtual forms).  Adult teachers who learn from constructing lectures and adult programmers who learn from constructing software only learn after they’re comfortable with course design and programming.  When you first design a course, you’re learning about course design, and less about the content.  Few people will learn to program by joining an open source development effort.

The problem of expecting expertise shows up often in undergraduate education. In undergraduate computer science courses, we expect students to know about mathematic concepts from algebra, trigonometry, geometry, and even calculus. If students don’t know those concepts, we expect them to “pick them up” on their own, and their grades suffer. When they fail, we complain that “these students don’t have the right background.” If they don’t have the basic background, it’s hard to move forward. Think about it from a developmental perspective, instead of our more common judgmental “hold the standard” perspective. Where does the student get the knowledge that we expect but they “missed”? If an adult misses the basics, is that it? They’ve simply missed out for this lifetime? How does an adult fit in learning Algebra 1 (for example) if he missed out earlier?

Because of the presumption of expertise, we adult learners tend to gravitate to constructionist learning opportunities where we do know the basics. Teachers have taught before, so they can learn by teaching something new. Mike Hewner is an excellent software engineer, so simply shifting to a new domain was an enjoyable challenge.

Or, we tackle project where adults with no expertise are expected, like learning a foreign language or introductory web design. But if I as an adult decided to learn how to build a bookcase from lumber, it’s not clear where I’d go to get the basic knowledge of carpentry that I lack. Go to the local DIY store and there’s an assumption that you did shop as a kid and that you know how to hammer and saw efficiently.

Maybe this is why it’s so hard for adults to jump into a new career, to start over, to construct new prestige. We lose face because we give up our former prestige. But as we live longer, there is time enough to develop new prestige, a new face.

(3) Time and Responsibility. I saved the most obvious difference for last. In our modern society, we do the majority of formal education before our citizens develop responsibilities around home, family, and career, when they can devote time to learning. Adults are swamped with responsibilities and do not have much time to devote to learning.

Constructionism is not an efficient form of learning. Learning can happen “irrespective of the circumstances of the learning” (as Seymour says). One can learn from reading a book or attending a lecture. Building through construction can be a motivating context for learning, and it can lead to deep learning. But there are more efficient forms of learning, like individual tutoring and guided instruction. We can get better learning from mastery learning.

Adults need efficient learning. Efficient learning fits better into the time available. Learning occurs more efficiently with a teacher or mentor, who can design learning, guide learning, provide useful feedback, and cut-off dead-ends and wasted time. But the first two differences make it more difficult for adults to get the guidance that a good teacher can provide. Adult learners are less likely to seek out a teacher and ask their questions. It’s hard for teachers to recognize adult learner’s needs, because they presume expertise.

Sure, some adults will spend lots of time in “inefficient” constructionist learning activities, like model railroads, recreational mathematics, and the Society for Creative Anachronism.  What are the conditions under which that happens?  Obviously, leisure time is necessary — time that the adult feels can be spared from other responsibilities.  What if the adult wants to learn something “real” (e.g., something that aids in meeting responsibilities, like perhaps skills towards a new job or promotion), then they are unlikely to choose a constructionist route.  They might choose a MOOC, or some vocational form of learning that is more authentic.

Conclusion: I do believe that constructionism is an “especially felicitous” way to learn.  It’s fun to learn through constructionism.  Constructionist learning tends to be deep learning.  We do want adults to be able to use constructionist learning.

Constructionism can work for adults, but it’s more challenging. There are different issues than with children. Adults have less time to spend on learning and more responsibilities. They may not have the basic construction skills and knowledge in the medium of choice for constructionist learning, which is necessary to learn through construction.  They are less likely to ask for and receive the help that makes learning for effective and efficient. They are less likely to share, if that sharing might expose their lack of understanding. Constructionism is a particularly fun way to learn, but the costs of constructionism may be greater for adults than the utility provided.

As we live longer, the challenges of learning as adults becomes more of a problem.  If people are going to live to 80 or 90, it’s less believable that you will learn all the basics you will ever need for whatever career(s) you might be interested in by the time you are 21.  There’s time enough for a second career.  We need to make opportunities sufficient to learn for that career, too.

Learning is about the failure and struggle, not the success

“It is not the result of scientific research that ennobles humans and enriches their lives, but the struggle to understand while performing creative and open-minded intellectual work.” Einstein

Several kind readers sent me links to this piece in The Chronicle, which argues that students in fields like humanities need to learn to be like computer science students, in that students learn to deal with failure.  That we learn through failure isn’t a new idea.  Roger Schank has talked a lot about learning through failure, and Seymour Papert connected the notion of “hard fun” (developed in his work on constructionist programming environments) to learning to write, much as The Chronicle author has.  Not much gets learned from success, especially on the first draft.

Computer science is unusual among academic disciplines in that you can’t succeed without getting past an unrelenting critic with exacting standards–the computer. A program compiles, or it doesn’t.  The program does exactly what you specified, not what you wanted or meant.  Natural language is about engendering a meaning in the reader’s head.  Roy Pea noted a long time ago (1986) that the major, language-independent “superbug” that students face in programming is that they think that there’s a mind in the computer — that they see the role of programming languages as being like natural language, that you want to give the computer the “idea” of what you want.  But the computer has no mind to engender ideas in, and that’s confounding for students.

The Chronicle piece is also about the dangerous goal of avoiding failure in learning.  Maybe that’s a side effect of the high-cost of education, and the desire to make each moment as productive and effective as possible.  As the Einstein quote at the top points out, it’s not the success that ennobles us, but the struggle — and there is no struggle where there is no failure.  The author paints an overly-optimistic picture of computer science and how they take failure “in stride.”  While failure, and learning from failure, is a critical piece of computing education, it’s also the part that dissuades students from succeeding in computer science.  The literature is rich with stories of students deciding that they “just can’t do computers” (low self-efficacy), and how that leads to students giving up on computer science.  Successful computer science students respond to failure, and don’t find it “degrading.”  But isn’t that true for students in any field?

One of the stories that we hear from teachers about Media Computation classes is that “failures” are interesting.  You try to implement a particular image or audio effect, and you get something you didn’t expect.  Why?  It’s not a “failure” — you still got a picture or sound.  But it’s not the one that you aimed for.  Now, there’s a concrete artifact in front of you, with the program that generated it.  Figure it out.

The real challenge, as Seymour Papert suggested back in 1981 in Mindstorms, is to change school culture.  Learning should be seen as a process of “debugging,” which is just computer science speak for, “fail, then figure out why.”  Computer science doesn’t have a monopoly on failure-based learning.  You just can’t make progress in computer science unless you learn that attitude — and those that don’t pick up that attitude, don’t make progress.

I decided that as I sat in on a colleague’s computer-science course during the beginning of this, my last, semester in the classroom. I am moving into administration full time, and I figured that this was my last chance to learn some of the cool new digital-humanities stuff I’ve been reading about. What eventually drove me out of the class (which I was enjoying tremendously) was the time commitment: The work of coding, I discovered, was an endless round of failure, failure, failure before eventual success. Computer-science students are used to failing. They do it all the time. It’s built into the process, and they take it in stride.

via Next Time, Fail Better – Commentary – The Chronicle of Higher Education.

Learning about Learning (even CS), from Singing in the Choir

Earlier this year, I talked about Seymour Papert’s encouragement to challenge yourself as a learner, in order to gain insight into learning and teaching.  I used my first-time experiences working on a play as an example.

I was in my first choir for a only year when our first child was born.  I was 28 when I first started trying to figure out if I was a bass or tenor (and even learn what those terms meant).  Three children and 20 years later, our children can get themselves to and from church on their own. In September, I again joined our church choir.  I am pretty close to a complete novice–I have hardly even had to read a bass clef in the last two decades.

Singing in the choir has the most unwritten, folklore knowledge of any activity I’ve ever been involved with. We will be singing something, and I can tell that what we sang was not what was in the music.  “Oh, yeah. We do it differently,” someone will explain. Everyone just remembers so many pieces and how this choir sings them.  Sometimes we are given pieces like the one pictured above.  It’s just words with chords and some hand-written notes on the photocopy.  We sing in harmony for this (I sing bass).  As the choir director says when he hands out pieces like this, “You all know this one.”  And on average, he’s right.  My wife has been singing in the choir for 13 years now, and that’s about average.  People measure their time in this choir in decades.  The harmony for songs like this were worked out years and years ago, and just about everyone does know it.  There are few new people each year — “new” includes even those 3 years in. (Puts the “long” four years of undergraduate in new perspective for me.) The choir does help the newcomers. One of the most senior bass singers gives me hand gestures to help me figure out when next phrase is going up or down in pitch. But the gap between “novice+help” and “average” is still enormous.

Lave and Wenger in their book “Situated Learning” talk about learning situations like these.  The choir is a community of practice.  There are people who are central to the practice, and there are novices like me.  There is a learning path that leads novices into the center.

The choir is an unusual community of practice in that physical positioning in the choir is the opposite of position with respect to the community.  The newbies (like me) are put in the center of our section.  That helps us to hear where we need to be when singing.  The more experienced people are on the outside.  The most experienced person in the choir, who may also be the eldest, tends to sit on the sidelines, rather than stand with the rest of the choir.  He nails every note, with perfect pitch and timing.

Being a novice in the choir is enormous cognitive overload.  As we sing each piece, I am reading the music (which I’m not too good at) to figure out what I’m singing and where we’re going. I am watching the conductor to make sure that my timing is right and matches everyone else. I am listening intently to the others in my section to check my pitch (especially important for when there is no music!).  Most choir members have sung these pieces for ages and have memorized their phrasing, so they really just watch the director to get synchronized.

When the director introduces a new piece of music with, “Now this one has some tricky parts,” I groan to myself.  It’s “tricky” for the average choir members — those who read the music and who have lots of experience.  It’s “tricky” for those with literacy and fluency.  For me, still struggling with the notation, it takes me awhile to get each piece, to understand how our harmony will blend with the other parts.

I think often about my students learning Java while I am in choir.  In my class, I introduce “tricky” ideas like walking a tree or network, both iteratively and recursively, and they are still struggling with type declarations and public static void main.  I noticed last year that many of my students’ questions were answered by me just helping them use the right language to ask their question correctly. How hard it must be for them to listen to me in lecture, read the programs we’re studying, and still try to get the “tricky” big picture of operations over dynamic data structures–when they still struggle with what the words mean in the programs.

Unlike working on the play, singing in the choir doesn’t take an enormous time investment — we rehearse for two hours one night, and an hour before mass.  I’m having a lot of fun, and hope to stick with it long enough to move out of the newbie class.  What’s motivating me to stick with it is enjoyment of the music and of becoming part of the community.  There’s another good lesson for computer science classes looking to improve retention.  Retention is about enjoying the content and enjoying the community you’re joining.

 

New book on integrating technology: The Learning Edge: What Technology Can Do to Educate All Children

I liked this review of the new book by Bain & Weston, in that it’s referencing Seymour Papert’s explanation for what happened to Logo. Rather than becoming something to think with, Logo became something to be taught. That shift of focus from tool to goal led to Logo’s downfall, because that raises the question, “Well, if we add this learning goal, what has to go? The curriculum is already packed!” That’s a zero-sum game. But if instead, the question is, “What can we teach better or differently with the tool?” then we’re about increasing and improving learning, not pushing something out.

Technology in education has increased exponentially over the past years, but has it really impacted student achievement? If so, why standardized tests such as PISA or TIMMS don’t reflect this? Technology adoption by itself is not enough to ensure improvement on students, this seems to be very clear nowadays. So what makes the difference? The approach proposed by Dr. Bain and Dr. Weston changes the paradigm. It’s not about acess, it’s about use of technology. It’s not about substituting books for digital content, but empowering teachers on what they do before, in and after the classroom. How effective feedback and practices can be scaled through technology. Technology becomes the tool, not the end. The book is very approachable and easy to read, with great examples that really help illustrate the theory and implementation. This is a must read, I strongly recommend it to anyone interested in educational reform and how to implement such reform.

via Amazon.com: The Learning Edge: What Technology Can Do to Educate All Children (Technology Education Connections) (9780807752715): Alan Bain, Mark E. Weston: Books.