Posts Tagged 'social media'

MathThink MOOC v4 – Part 3

In Part 3, I describe some aspects and origins of the basic course pedagogy, and how they relate to student expectations.

This post continues the previous two in this series.

Expectations. So far I’ve talked about two expectations many students bring to my MOOC that cause problems:

(1) a perception that learning is a cycle of

instruction –> worked examples –> student exercises

(a process that’s better described as training, not learning), and

(2) a belief that failure is something to be avoided (rather than the essential part of learning that it is).

A third problematic expectation many students bring is based on the assumption that mathematics is a body of knowledge to be absorbed, rather than a way of thinking that has to be learned/acquired/developed. That belief is what can lead to the erroneous, and educationally debilitating, perception of mathematics that what makes it hard to learn is the sheer number of different rules and tricks that have to be learned, as described in the article about Jo Boaler’s work I cited in Part 1 of this series.

The view of mathematics as a large collection of procedures can get you quite a way, which explains the huge success of Khan Academy, which shows you all those rules – thousands of them! But it won’t get you to the stage of thinking like a mathematician. Mastering an array of procedures is fine if you are (1) willing to invest the time to keep learning new tricks and (2) prepared to end up working for someone who can do the latter (i.e., think mathematically). Because, increasingly, in the western world, it is that latter that is the valuable commodity. (I wrote about this back in 2008.) My use of the term “mathematical thinking”, rather than just “mathematics”, to title my course was designed to highlight the distinction, but many students nevertheless come to my MOOC expecting a mathematics course (in the sense they have come to understand that term), and are disappointed to discover that it is nothing of the kind. (Some have even asked why I don’t make it more like Khan Academy, a bizarre request which leaves me wondering why they don’t just enter the KA URL in their browser rather than navigate to my MOOC.)

Based on the kinds of issues I’ve been discussing regarding mathematical thinking, in designing my MOOC (and the classroom course that came earlier), I drew on a number of established pedagogies. Most notably among them is Inquiry-Based Learning. For a general background on this powerful and effective learning method, check out this 21-minute video.

Do please watch this video. The focus of much of the video is producing professional mathematicians, and that reflects a common use of the IBL method in mathematics majors classes. In my course, however, with its focus on general mathematical thinking skills for use in many life situations, I don’t ask the students to act as those in a regular IBL class – that would be impossibly hard to pull off in a MOOC in any case. But I believe the general learning principles apply (perhaps even more so), and some of the comments in the video from people who pursued careers in industry address that aspect.

Another pedagogic strategy I adopt is one that has been used in mathematic education since the time of the ancients, which I usually refer to as the Mr Miyagi Method, after the Japanese martial arts expert in the hit 1984 movie The Karate Kid. Having promised to teach karate to the young American Daniel Larusso, Mr Myagi makes his young student paint a fence, wax a floor, and polish several cars. Only with great reluctance does Daniel acquiesce, but in due course he discovers the value of all that effort, as you see from this brief clip.

As I say, this form of teaching has been used in mathematics for centuries. The reason is that in many cases it is impossible to appreciate how mathematics can be applied in a particular situation until enough of the relevant mathematics has been learned. So you design small, self-contained exercises to develop the individual component abilities. Mathematics textbooks have been doing this since they were written on clay tablets five thousand years ago. It’s what most people experience as “mathematics education.”

An attractive alternative is project-based learning. (Again, please do watch this short video.) Unfortunately, whereas PBL is fine for a regular course, in a MOOC that is designed to be of value both to students working on their own, with few if any additional resources, and to students who just participate in a part of the course, it is not an option. That leaves the Miyagi Method as the only game in town.

Even is a regular classroom, and for sure in a MOOC, I would however strongly recommend not adopting Mr Miyagi’s method of delivery. It would surely have been better (as an educational strategy, though not as a movie scene) if he had first explained to Daniel what those chores had to do with learning karate. If a student has to ask, “Why am I learning this?”, the teaching has failed. Why not tell the student from the start?

But remember, times change, and skills and abilities that were valuable in one era sometimes become far less significant, as we are reminded by another Hollywood blockbuster character, Indiana Jones. So you’d better be sure that when you tell a student why a particular topic is important, the reason you give is plausible. (Note: In today’s world, no one balances checkbooks any more – heavens, most people no longer have a checkbook – and no householder uses geometry to figure out how much carpet to order for a room.)

Turning the failing-as-part-of-learning meme on my own journey of learning how to design and give a MOOC, I think that so far I have definitely failed to make sufficiently clear to my MOOC students (1) the basic goals of the course, (2) the approach I am taking to try to achieve those goals, and (3) how those goals lead to adopting the methods I have just outlined above.

To be sure, I laid everything out in detail in the guide-notes I posted on the course website, and in some of my earlier posts to this blog, that I link to from the course site. The problem was, many students never read everything on the site; indeed, some appear not to have read any of the site information.

Now, you might say, they had an obligation to do so. It’s their education, after all, not mine. But MOOCs are about taking learning to a much wider audience than is reached by traditional higher education, and if a MOOC instructor does not manage to connect to that audience, then that is a failure of mission.

As a result, one change I am making with the new version of the course in February is that one of the first things the students will encounter is a video of me explaining the course pedagogy.

[From the very first offering of the course, I posted video discussions between me and my then course TAs, in which we discussed the course design, but those discussions really only made sense after a student had spent some time in the course. So from the second run onwards I cut them into short segments that were released on the site throughout the course. I suspect those discussions were perceived more as "Charlie Rose type" television conversations, rather than providing key information about how to take the course. (In the second of the two discussions, I even asked a professional television and radio host I know to moderate the discussion.) In any case, they did not have the effect I hope will be achieved by a face-to-face explanation by me, as the instructor, of the course goals and structure, given before the course starts. You can view those two earlier videos at: Team Discussion (8mins), What's New in Number Two (10min 45sec).]

Will my new introductory video solve the problem? I don’t know. For sure, MOOC students do watch (almost) all the videos. Indeed, if there is a problem, it is that some seem to perceive the videos as the most important component of the course, a perception the news media seem to share. Why is that a problem? Because video instruction (i.e., direct instruction) in fact-based, science disciplines does not work. Indeed, instructional videos do actual harm by re-enforcing any prior-held false beliefs, as Derek Muller explains in this video. (Yup, putting math and science education out as a MOOC is hard!)

My guess is that my new introductory video will have an effect, but it will be limited, and many will still be left feeling confused as the course moves ahead. Unfortunately, since the only tools we have at our disposal in a MOOC are video, text, and social media, I don’t see what more I can do, so my gut feeling at this stage is that with the new video I will have gone as far as the medium allows. Nothing works for everyone. All we can do is design for a feasible maximum.

I’ll say (yet) more on this theme of recognizing, anticipating, and dealing with student expectations in my next post. Based on giving three successive versions of my MOOC now, I think the student expectations issue is much more significant in a MOOC than in a regular class. The reason is that in a MOOC, because you have no direct contact with the students, you have very limited ability to counter or correct or allow for those expectations. Your only real strategy is to identify them, and pre-emptively try to lessen their impact on the student.

great-expectations-posterTRAILER (LOOKS GOOD)

Coming up for air (and spouting off)

A real-time chronicle of a seasoned professor who has just completed giving his first massively open online course.

Almost a month has passed since I last posted to this blog. Keeping my MOOC running took up so much time that, once it was over, I was faced with a huge backlog of other tasks to complete. Taking a good look at the mass of data from the course is just one of several post-MOOC activities that will have to wait until the New Year. So readers looking for statistics, analyses, and conclusions about my MOOC will, I am afraid, have to wait a little bit longer. Like most others giving these early MOOCs, we are doing so on the top of our existing duties; the time involved has yet to be figured into university workloads.

One issue that came up recently was when I put on my “NPR Math Guy” hat and talked with Weekend Edition host Scott Simon about my MOOC experience.

In the interview, I remarked that MOOCs owed more to Facebook than to YouTube. This observation has been questioned by some people, who believe Kahn Academy’s use of YouTube was the major inspiration. In making this comment, they are echoing the statement made by former Stanford Computer Science professor Sebastian Thrun when he announced the formation of Udacity.

In fact, I made my comment to Scott with my own MOOC (and many like it) in mind. Though I have noted in earlier posts to this blog how I studied Sal Khan’s approach in designing my own, having now completed my first MOOC, I am now even more convinced than previously that the eventual (we hope) success of MOOCs will be a consequence of Facebook (or social media in general) rather than of Internet video streaming.

The reason why I felt sure this would be the case is that (in most disciplines) the key to real learning has always been bi-directional human-human interaction (even better in some cases, multi-directional, multi-person interaction), not unidirectional instruction.

What got the entire discussion about MOOCs off in the wrong direction – and with it the public perception of what they are – is the circumstance of their birth, or more accurately, of their hugely accelerated growth when a couple of American Ivy League universities (one of them mine) got in on the act.

But it’s important to note that the first major-league MOOCs all came out of Stanford’s Computer Science Department, as did the two spinoff MOOC platforms, Udacity and Coursera. When MIT teamed up with Harvard to launch their edX platform a few months later, it too came from their Computer Science Department.

And there’s the rub. Computer Science is an atypical case when it comes to online learning. Although many aspects of computer science involve qualitative judgments and conceptual reasoning, the core parts of the subject are highly procedural, and lend themselves to instruction-based learning and to machine evaluation and grading. (“Is that piece of code correct?” Let the computer run it and see if it performs as intended.)

Instructional courses that teach students how to carry out various procedures, which can be assessed to a large degree by automatic grading (often multiple choice questions) are the low hanging fruit for online education. But what about the Humanities, the Arts, and much of Science, where instruction is only a small part of the learning process, and a decidedly unimportant part at that, and where machine assessment of student work is at best a goal in the far distant future, if indeed it is achievable at all?

In the case of my MOOC, “Introduction to Mathematical Thinking,” the focus was the creative/analytic mathematical thinking process and the notion of proof. But you can’t learn how to think a certain way or how prove something by being told or shown how to do it any more than you can learn how to ride a bike by being told or shown. You have to try for yourself, and keep trying, and falling, until it finally clicks. Moreover, apart from some very special, and atypical, simple cases, neither thinking nor proofs can be machine graded. Proofs are more like essays than calculations. Indeed, one of the things I told my students in my MOOC was that a good proof is a story, that explains why something is the case.

For the vast majority of students, discussion with (and getting feedback from) professors, TAs, and other students struggling to acquire problem solving ability and master abstract concepts and proofs, is an essential part of learning. For those purposes, the online version does not find its inspiration in Khan Academy as it did for Thrun, but in Facebook, which showed how social interaction could live on the Internet.

When the online version of Thrun’s Stanford AI class attracted 160,000 students, he did not start a potential revolution in global higher education, but two revolutions, only the first of which he was directly involved in. The first one is relatively easy to recognize and understand, especially for Americans, who for the most part have never experienced anything other than instruction-based education.

For courses where the goal is for the student to achieve mastery of a set of procedures (which is true of many courses in computer science and in mathematics), MOOCs almost certainly will change the face of higher education. Existing institutions that provide little more than basic, how-to instruction have a great deal to fear from MOOCs. They will have to adapt (and there is a clear way to do so) or go out of business.

If I want to learn about AI, I would prefer to do so from an expert such as Sebastian Thrun. (In fact, when I have time, I plan on taking his Udacity course on the subject!) So too will most students. Why pay money to attend a local college and be taught by a (hopefully competent) instructor of less stature when you can learn from Thrun for free?

True, Computer Science courses are not just about mastery of procedures. There is a lot to be learned from the emphases and nuances provided by a true expert, and that’s why, finances aside, I would choose Thrun’s course. But at the end of the day, it’s the procedural mastery that is the main goal. And that’s why that first collection of Computer Science MOOCs has created the popular public image of the MOOC student as someone watching canned instructional videos (generally of short duration and broken up by quizzes), typing in answers to questions to be evaluated by the system.

But this kind of course occupies the space in the overall educational landscape that McDonalds does in the restaurant business. (As someone who makes regular use of fast food restaurants, this is most emphatically not intended as a denigratory observation. But seeing utility and value in fast food does not mean I confuse a Big Mac with quality nutrition.)

Things are very, very different in the Humanities, Arts, and most of Science (and some parts of Computer Science), including all of mathematics beyond basic skills mastery – something that many people erroneously think is an essential prerequisite for learning how to do math, all evidence from people who really do learn how to do math to the contrary.

[Ask the expert. We don’t master the basic skills; we don’t need them because, early on in our mathematic learning, we acquired one – yes, just one – fundamental ability: mathematical thinking. That’s why the one or two kids in the class who seem to find math easy seem so different. In general, they don’t find math easy, but they are doing something very different from everyone else. Not because they are born with a “math gene”. Rather, instead of wasting their time mastering basic skills, they spent that time learning how to think a certain way. It’s just a matter of how you devote your learning time. It doesn’t help matters that some people managed to become qualified math teachers and professors seemingly without figuring out that far more efficient path, and hence add their own voice to those who keep calling for “more emphasis on basic skills” as being an essential prerequisite to mathematical power.]

But I digress. To get back to my point, while the popular image of a MOOC centers on lecture-videos and multiple-choice quizzes, what Humanities, Arts, and Science MOOCs (including mine) are about is community building and social interaction. For the instructor (and the very word “instructor” is hopelessly off target in this context), the goal in such a course is to create a learning community.  To create an online experience in which thousands of self-motivated individuals from around the world can come together for a predetermined period of intense, human–human interaction, focused on a clearly stated common goal.

We know that this can be done at scale, without the requirement that the participants are physically co-located or even that they know one another. NASA used this approach to put a man on the moon. MMOs (massively multiplayer online games – from which acronym MOOCs got their name) showed that the system works when the shared goal is success in a fantasy game world.

Whether the same approach works for higher education remains an open question. And, for those of us in higher education, what a question! A question that, in my case at least, has proved irresistible.

This, then, is the second MOOC revolution. The social MOOC. It’s outcome is far less evident than the first.

The evidence I have gathered from my first attempt at one of these second kinds of MOOC is encouraging, or at least, I find it so. But there is a long way to go to make my course work in a fashion that even begins to approach what can be achieved in a traditional classroom.

I’ll pursue these thoughts in future posts to this blog — and in future versions of my Mathematical Thinking MOOC, of which I hope to offer two variants in 2013.

Meanwhile, let me direct you to a recent article that speaks to some of the issues I raised above. It is from my legendary colleague in Stanford’s Graduate School of Education, Larry Cuban, where he expresses his skepticism that MOOCs will prove to be an acceptable replacement for much of higher education.

To be continued …

Peer grading: inventing the light bulb

A real-time chronicle of a seasoned professor who has just completed giving his first massively open online course.

With the deadline for submitting the final exam in my MOOC having now passed, the students are engaging in the Peer Evaluation process. I know of just two cases where this has been tried in a genuine MOOC (where the M means what it says), one in Computer Science, the other in Humanities, and both encountered enormous difficulties, and as a result a lot of student frustration. My case was no different.

Anticipating problems, I had given the class a much simplified version of the process – with no grade points at stake – at the end of Week 4, so they could familiarize themselves with the process and the platform mechanics before they had to do it for real. That might have helped, but the real difficulties only emerged when 1,520 exam scripts started to make their way through the system.

By then the instructional part of the course was over. The class had seen and worked through all the material in the curriculum, and had completed five machine-graded problem sets. Consequently, there were enough data in the system to award certificates fairly if we had to abandon the peer evaluation process as a grading device, as happened for that humanities MOOC I mentioned, where the professor decided on the fly to make that part of the exam optional. So I was able to sleep at night. But only just.

With over 1,000 of the students now engaged in the peer review process, and three days left to the deadline for completing grading, I am inclined to see the whole thing through to the (bitter) end. We need the data that this first trial will produce so we can figure out how to make it work better next time.

Long before the course launched, I felt sure that there were two things we would need to accomplish, and accomplish well, in order to make a (conceptual, proof-oriented) advanced math MOOC work: the establishment (and data gathering from) small study groups in which students could help one another, and the provision of a crowd-sourced evaluation and grading system.

When I put my course together, the Coursera platform supported neither. They were working on a calibrated peer review module, but implementing the group interaction side was still in the future. (The user-base growth of Coursera has been so phenomenal, it’s a wonder they can keep the system running at all!)

Thus, when my course launched, there was no grouping system, nor indeed any social media functionality other than the common discussion forums. So the students had to form their own groups using whatever media they could: Facebook, Skype, Google Groups, Google Docs, or even the local pub, bar, or coffee shop for co-located groups. Those probably worked out fine, but since they were outside our platform, we had no way to monitor the activity – an essential functionality if we are to turn this initial, experimental phase of MOOCs  into something robust and useful in the long term.

Coursera had built a beta-release, peer evaluation system for a course on Human Computer Interaction, given by a Stanford colleague of mine. But his needs were different from mine, so the platform module needed more work – more work than there was really time for! In my last post, I described some of the things I had to cope with to get my exam up and running. (To be honest, I like the atmosphere of working in startup mode, but even in Silicon Valley there are still only 24 hours in a day.)

It’s important to remember that the first wave of MOOCs in the current, explosive, growth period all came out of computer science departments, first at Stanford, then at MIT. But CS is an atypical case when it comes to online learning. Although many aspects of computer science involve qualitative judgments and conceptual reasoning, the core parts of the subject are highly procedural, and lend themselves to instruction-based learning and to machine evaluation and grading. (“Is that piece of code correct?” Just see if it runs as intended.)

The core notion in university level mathematics, however, is the proof. But you can’t learn how to prove something by being told or shown how to do it any more than you can learn how to ride a bike by being told or shown. You have to try for yourself, and keep trying, and falling, until it finally clicks. Moreover, apart from some very special, and atypical, simple cases, proofs cannot be machine graded. In that regard, they are more like essays than calculations. Indeed, one of the things I told my students was that a good proof is a story, that explains why something is the case.

Feedback from others struggling to master abstract concepts and proofs can help enormously. Study groups can provide that, along with the psychological stimulus of knowing that others are having just as much difficulty as you are. Since companies like Facebook have shown us how to build platforms that support the creation of groups, that part can be provided online. And when Coursera is able to devote resources to doing it, I know it will work just fine. (If they want to, they can simply hire some engineers from Facebook, which is little more than a mile away. I gather that, like Google before it, the fun period there has long since passed and fully vested employees are looking to move.)

The other issue, that of evaluation and grading, is more tricky. The traditional solution is for the professor to evaluate and grade the class, perhaps assisted by one or more TAs (Teaching Assistants). But for classes that number in the tens of thousands, that is clearly out of the question. Though it’s tempting to dream about building a Wikipedia-like community of dedicated, math-PhD-bearing volunteers, who will participate in a mathematical MOOC whenever it is offered – indeed I do dream about it – it would take time to build up such a community, and what’s more, it’s hard to see there being enough qualified volunteers to handle the many different math MOOCs that will soon be offered by different instructors. (In contrast, there is just one Wikipedia, of course.)

That leaves just one solution: peer grading, where all the students in the class, or at least a significant portion thereof, are given the task of grading the work of their peers. In other words, we have to make this work. And to do that, we have to take the first step. I just did.

Knowing just how many unknowns we were dealing with, my expectations were not high, and I tried to prepare the students for what could well turn out to be chaos. (It did.) The website description of the exam grading system was littered with my cautions and references to being “live beta”. On October 15, when the test run without the grading part was about to launch, I posted yet one more cautionary note on the main course announcements page:

… using the Calibrated Peer Review System for a course like this is, I believe, new. (It’s certainly new to me and my assistants!) So this is all very much experimental. Please approach it in that spirit!

Even so, many of the students were taken aback by just how clunky and buggy the thing was, and the forums sprung alive with exasperated flames. I took solace in the recent release of Apple Maps on the iPhone, which showed that even with the resources and expert personnel available to one of the world’s wealthiest companies, product launches can go badly wrong – and we were just one guy and two part-time, volunteer student assistants, working on a platform being built under us by a small startup company sustained on free Coke and stock options. (I’m guessing the part about the Coke and the options, but that is the prevalent Silicon Valley model.)

At which point, one of those oh-so-timely events occurred that are often described as “Acts of God.” Just when I worried that I was about to witness, and be responsible for starting, the first global, massive open online riot (MOOR) in a math class, Hurricane Sandy struck the Eastern Seaboard, reminding everyone that a clunky system for grading math exams is not the worst thing in the world. Calm, reasoned, steadying, constructive posts started to appear on the forum.  I was getting my feedback after all. The world was a good place once again.

Failure (meaning things don’t go smoothly, or maybe don’t work at all) doesn’t bother me. If it did, I’d never have become a mathematician, a profession in which the failure rate in first attempts to solve a problem is somewhere north of 95%. The important thing is to get enough data to increase the chances of getting it right – or far more likely, just getting it better – the second time round. Give me enough feedback, and I count that “failure” as a success.

As Edison is said to have replied to a young reporter about his many failed attempts to construct a light bulb, “Why would I ever give up? I now know definitively over 9,000 ways that an electric light bulb will not work. Success is almost in my grasp.” (Edison supposedly failed a further 1,000 times before he got it right. Please don’t tell my students that. We are just at failure 1.)

If there were one piece of advice I’d give to anyone about to give their first MOOC, it’s this: remember Edison.

To be continued …

How Facebook Made MOOCs Viable: MOOC planning – Part 2

A real-time chronicle of a seasoned professor embarking on his first massively open online course.

One obvious, but huge distinction between planning a physical course and planning a MOOC is that for the former, it is generally fairly easy to make changes — even major ones — once the course is underway. But MOOCs are different. It requires an enormous amount of time to put a MOOC together (video recording/editing and implementing all the online course materials are just two elements not present in a physical course, or if they are, those materials can usually simply be omitted if a mid-course adjustment is required). As a result, once the course launches, you are pretty well committed to running it through largely as planned.

If I were putting together a MOOC for which Stanford would charge (and offer credit), by now I would be getting decidedly nervous. But that is not how things stand at present. Everyone sees this sudden MOOC explosion purely as an experiment to see what the medium can offer. The courses are free, and since there is no credential at stake, there is no worry about unmotivated students or of cheating. An unmotivated student is not going to continue with the course beyond the first week or so, and the only person who loses by student cheating is the student. Presumably both will change if this experimental phase is a success, and MOOCs take their place alongside other forms of higher education, where there are payments and credentials.

My own view, as I’ve noted elsewhere, is that MOOCs are not a replacement of the traditional bricks-and-mortar university, rather they are the twenty-first century version of the textbook.

Widespread availability of textbooks did not replace universities. Indeed, they did not change university education very much at all. In theory, once every student could purchase a textbook, there should have been little need for professors to give mainstream content lectures — particularly if the professor had written the course textbook — but the basic content lecture continued to remain the dominant model.  Early in my professorial career, I tried to adopt a flipped classroom approach, based on giving students reading assignments from a book I had written, and using the class time to discuss the material. It proved to be a disaster; hardly any of the student read the assigned reading, and of those that had, few really knew how to read a mathematics text and learn by so doing. I soon ended up having to give classical lectures on the material that was expressed far better in my textbook — far better because I had spent time putting my thoughts onto the page and the resulting manuscript had been professionally edited.

I am not sure that, on their own, video-recorded instructional material will lead to much of a change in university education either. Video-lectures are not really very different from textbooks. At least, for most university-level material that is the case. For learning how to carry out maintenance around the house, to change a bicycle tire, to assemble a piece of furniture, etc., video is far better than text. But those are all simple procedural learning — the goal is to learn how to do something, and for that purpose, showing is more efficient than describing in words. In contrast, the main focus of much university education is understanding; the student is supposed to learn how to think differently. That is very hard to do at arm’s length, regardless of whether the arm involves a textbook or a video. It is by direct interaction with an instructor and with other learners that we can gain understanding and learn how to think a certain way. That is why I don’t see MOOCs as a threat to the existence of universities.

MOOCs may, however, do what textbooks and instructional-videos failed to do. They may finally give rise to flipped classrooms — a mere six centuries after the invention of the printing press give rise to textbooks. The reason is, MOOCs are far more than video-recorded instruction. In fact, video lectures are one of the least significant elements of a MOOC. The key to the educational potential of MOOCs are human-computer and human-human interaction —  the latter especially so for most subjects. In particular, social media are what make MOOCs possible, and it is the widespread familiarity with, and acceptance of, human-human interaction over an ethernet cable that led to the sudden explosion of interest in MOOCs. In short, MOOCs are a direct consequence of the growth of Facebook, which made interaction-by-social-media global.

[I should add that I don't see the degree of human-human interaction offered by social media in a MOOC being as educationally powerful as direct fact-to-face interaction. The unavoidable limitation in a MOOC is not the medium per se, rather is the scalability factor. In a physical class, the students get to interact with the professor -- the expert, the domain professional. In a MOOC, that crucial part is missing. I think good course design can get a lot out of social media, but that one factor means that we'll always need physical universities.]

The challenge facing a professor setting out to design and offer a MOOC, then, is to figure out how to take advantage of the (human-computer and) human-human interaction made possible on a global scale by social media, in order to provide students with a valuable learning experience.

In this regard, the experiment really begins with (many of) the 117 MOOCs currently offered by the MOOC platform Coursera. Coursera is a spin-off from a Stanford project in Computer Science to develop a platform to support flipped classrooms at the university. The first wave of Stanford MOOCs were basic level computer science courses, where there is a heavy focus on procedural learning and less dependency on reflection and peer interaction. (Those features come later in CS, and when they do, not a few Stanford CS students drop out and start their own companies, occasionally becoming millionaires within a few years!) But many of the second wave of courses now underway are in humanities and other areas, where the primary focus is on thinking and understanding, not doing.

To take just one instance of course design, in a basic-level computer science MOOC, it is possible to give machine-graded assignments. It would be possible to offer a math MOOC a similar way, provided the focus was on mastering basic computational procedures.  But in my case, where my goal is to develop mathematical thinking, I realized from the start that the key to making it work would be the social media factor. Just as it is for humanities courses.

That impacted how I would design, structure, and present the core material, as I’ll describe in my next post.

To be continued …


I'm Dr. Keith Devlin, a mathematician at Stanford University. In fall 2012, I gave my first free, open, online math course. I repeated it in spring 2013, then in fall 2013, and in February I am giving it a fourth time, each time with changes. This blog chronicles my experiences as they happen.

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