Why cant education materials just be open and remain open to everyone, journals and textbooks included? There are enough tax paying citizens all over the world pumping money into our education systems that there is no reason it should not be freely available 24/7 to all.
The fact that unlimited free education still doesnt exist in 2015 makes me sad everytime I think about it.
That said, there are plenty of courses on Coursera where the materials stay up and online for years. But there's nothing wrong with teachers controlling how, when and where their course materials are accessible, and since many of the courses are from private institutions, e.g. notably Ng's Stanford, tax paying and citizenship (two orthogonal concepts orthogonal to accessibility of course materials) are mostly tangential to major MOOC mechanics.
It's absolutely amazing that the resources are so freely available and high quality. I am not sure where one would get standing to make demands upon the private entities providing them.
Now I realize that some aspects of the way some courses are run require simultaneity. Things like human-powered grading, by TAs or peers. But they don't have to be designed that way. I've taken Coursera courses that are largely machine graded. For those, there doesn't seem to be any real reason why the courses are timeboxed. And even for courses where this is difficult to design around, it seems like it should be possible to tighten the iteration cycle.
Just to provide the other side of the story, I've done about a dozen MOOCs (mostly through Coursera) and I'm most of the way through Georgia Tech's online masters program.
Having to make progress every week is contributes a lot to me actually finishing these courses. The Udacity courses I've signed up for never get touched because there's no urgency. Ditto the textbooks on my shelf that I keep meaning to go through, and the Open Yale and MIT OCW courses I've bookmarked... I obviously have the time, but unless there's a threat of "Do it now or it's gone" I'll probably go and watch TV when I get home from work.
I'm the other side of the coin. I may start off on schedule, but once life intervenes and I fall behind, and I've gotten a couple zeros, I'm like why bother catching up? As a result, I've only successfully finished one out of the 6 or 7 Coursera courses I've started.
Anyway, there's pedagogical theory that favors time boxing [and some that doesn't]. Coursera is opinionated in the matter. That's not a controversial opinion in higher education circles upon which Coursera styles itself and from which it draws it's educators.
What MOOCs do very badly is peer interactions whether for discussions or grading. Part of it's scale. Part of it's the vast differences in the levels and expectations of the participants. Of course, as you say, it's precisely for these reasons that we force courses to provide simultaneity.
On the other hand, if you run things on a tight schedule, real life will get in the way for a lot of people in that many of the better courses are fairly significant time commitments.
Sure, but given Coursera's popularity it seems to be doing something right.
>On the other hand, if you run things on a tight schedule, real life will get in the way for a lot of people in that many of the better courses are fairly significant time commitments.
But is it? The time commitments are watching few videos, learning and doing the assignments, all of this in a week. I think it's enough even for people working full time. If you don't have enough time on your hands to do all this, I'd argue you don't have the time required to complete the course.
Furthermore having time constraints is very important for students to keep on track.
That said, people with busy schedules, travel, etc. can easily get behind on a more time-consuming course and, at that point, the natural reaction is just to drop it.
My personal preference is to have structure but to build sufficient wiggle-room in that someone can catch up a week or two.
I understand that open ended courses might not have the volume to create an active forum, but why not create subject forums where people can discuss things and create a community? In my experience, communities are a very important way to give people the motivation to complete some of these courses.
My intuition is that the venture-backed model of the other platforms will make it extremely difficult to maintain a long-term vision of free, ubiquitous education. I did find one edX course which decayed, simply because it required student journaling and deeply personal discussion, which would have been inappropriate to open to the public.
1. Not as exclusively as I thought - I just saw a great course from Coursera and signed up. To date I've only completed edX courses though.
That said, I agree with your main point, which is that if the billions that go into the journal and textbook scams were redirected to instructors (via higher salaries and resources) and students (via lower tuition costs), we would have a much stronger education system.
And its worth remembering that Coursera allows videos and pdfs to be downloaded, which I get into the habit of doing as I progress through a course.
 My Courses -> Archived -> Course Page -> Select session that I did -> Go To Course.
As other commenbters have said, course material can often be found on the net if you look in the right places.
For example, Hinton's class on neural networks from 2012 seems to be open again. https://www.coursera.org/course/neuralnets
This also means that Coursera courses have naturally the highest "total enrollment" numbers, even though probably only a few percent of the students are actually taking the class.
Also if the course you want to enroll is not scheduled for near future, you can go to the course page and view the older version of the course.
I usually can find the course material online in a tar or zip file which has the home work and answers. Youtube usually has the videos. Still prefer to just open up the Coursera classes.
Write the code here to do X :
I write a messy 4 liner after lots of thinking.
Professor Norvig comes along and does it in a simple functional one liner.
He teaches good functional style program design one epiphany at a time.
>> Professor Norvig comes along and does it in a simple functional one liner.
>> Mind blown.
I would also recommend the Cryptography 1 course by Dan Boneh on Coursera . Excellent if you are at all interested in the subject.
I always download the lecture videos, slides, quizzes, labs and exams because, as mentioned, many of the courses don't allow access once the class is completed.
You definitely have to have plenty of self discipline to complete MOOCs. And I don't have any delusions about a Coursera certificate being useful in landing a job; that's not what I'm after. I'm building the skills I want to apply to my own projects.
YouTube has a few really amazing courses available online.
For example, UCB has a channel with up-to-date content that otherwise not available on MOOC platform.
* https://www.youtube.com/watch?v=QMV45tHCYNI is a very good class on data structure
* https://www.youtube.com/watch?v=HyUK5RAJg1c and the rest has very good lectures on theory of computation
* https://www.youtube.com/watch?v=_G6_-ljgmXE also very good for algorithms. I find MIT's version to be too theory based for practitioners. Anyway, I still watch MIT's just to complement anything missing (no two speakers can teach the same topic equally)
I wish people would stop looking so much at completion scores and not judge the concept as a failure just because they are so low.
But in MOOC land I get nothing. I did the complete automata class mostly to see if there's much new since I did a brick and mortar version some decades ago, and the "reward" was not exactly fulfilling. I'm not expecting Ullman to fly out and shake my hand, or my phone to ring off the hook from recruiters seeing I jumped thru some hoops, but something more would have been nice? Actually, anything would have been nice?
Since the grind factor was aggressively burned out of me, I haven't completed a single course. I did watch the videos and did the assignments for computational neuroscience and one of the multi-class algo series and the functional programming and the famous AI class.
There's a lot of talk about how grinders don't grind therefore grinders suck or grinding sucks. However what really sucks is the playground presented to the grinders. Its not their fault they aren't being given something worth grinding.
By analogy most MMORPGs that tanked and died, tanked and died because the grind experience sucked. Its not the fault of the grinders that the businessmen couldn't or wouldn't present something as enjoyably grindable as WoW or Eve.
To some extent the whole market segment of MOOCs smells like a freshmen psych class lab experiment, "what happens to grind performance when you leave all the annoyance factors of grinds in place but remove all the rewards, with the hypothesis that almost every rat in the box stops grinding"
And, I expect, that if you start from the assumptions that 1.) MOOC certificates aren't worth anything as a real-world "vocational meal ticket" beyond the value of the underlying education and 2.) Companies offering MOOCs are not going to become unicorns and probably won't even return investors money-- you probably end up with something that isn't designed to mirror a university course.
I'm proposing that as a bottom up narrative, they continue to include extensive grind barriers such as peculiar scheduling and pacing, someone else designing arbitrary barriers in terms of assignments with firm due dates, etc. Yet the reward has been cut out as per reasons in the top down narrative, etc. So from the bottom up perspective you're left with a stereotypical grind game where the reward has been removed, and from that theoretical model, the dropout rate makes sense. Then when people sign up and get something out of partial participation, it makes no sense to use a successful grind game metric measurement when its a failed grind game therefore the players aren't cooperating because they feel no need to do so. Because "MOOC as a grind game" has failed, a MOOC is a place to learn, not a place to jump thru hoops or not get a reward other than making someone else's completion metric result look good.
You can run a grind game without a vocational meal ticket reward model. WoW and Eve do just fine.
My suggestion would be to use advanced technology to abandon the grind game aspects, assuming higher completion rates are inherently valuable. Even if completion rates are not valuable, removal of pointless grind will result in better operating conditions for the students. Why must class schedules correspond vaguely to the northern hemisphere agricultural growing season? If you're not teaching project management, why are due dates so important? Why is there (typically) only one very fixed learning track if more exist or there is a "chose two from column B and three from column C" aspect inherent in the field itself? All of that would be very difficult to implement at Harvard in 1640, but due to technological advances would not be very challenging to implement today.
Of course, as you suggest, there are potentially ways to gamify and provide incentives for learning that don't as closely mimic traditional course structures.
All that matters is whether the user found some useful information in it, and just fully watching one video lecture should be enough for that metric.
So you either saw different approaches to the same problem (e.g. Data Analysis) or everyone discussing a different problem (An Introduction to Operations Management). I am sure that I got a lot (in terms of learning) from that part of the course.
If everyone just picks and chooses some papers/videos and drops out before the end you don't get anyone to grade your work and you have no other work to grade (and possibly learn from).
Found a couple of articles:
Unlike Andrew Ng's Coursera Machine Learning class, it is a real, unadulterated Caltech class, and exactly as challenging as that implies. It delves much more deeply into the mathematics behind ML, and the homework assignments are quite time consuming.
I took it as part of an interactive session through EdX, and the professor himself was extremely active on the forums: responding to student questions, clarifying lecture points, and giving homework suggestions--seemingly at all hours of the day and night.
I'm now doing Coursera's Interaction Design specialisation , which is proving to be very informative and a lot of fun.
If you're considering doing a MOOC then I'd definitely recommend it. Choose a free, short-ish course to start with, make the commitment, and dive in.
Although personally I like better the philosophy introduction offered by MIT:
I am also looking forward to this one
> An introduction to philosophy of mind, exploring consciousness, reality, AI, and more. The most in-depth philosophy course available online.
> What you will learn
The basics of argumentation
Some central arguments for and against the view that a
sufficiently powerful computer can think (AI)
The main theories of mental states and their relations to physical states
Some central arguments for and against the view that the world is not as we perceive it to be
What the "hard problem of consciousness" is
For a novice, their 'Intro to Computer Science' course is fantastic, as is the follow-on 'Web Development' course, led by Steve Huffman.
surprised by the courses that are more popular in this list.
I think the pattern is that the foundational or introductory courses are popular as they have a
larger audience. But it doesn't comment on the quality of the course. An interesting data point
is the social media "Share" widget that appears on the right column .
Enrolment would be an empty number to me, completion would be the mark of quality.
The thing about MOOC's is that scale is staggering.
Rating by the total number of students that completed a course would be even more revealing. Add a student retention rate, too. Etc etc.
Some considerations: all courses have lots of people that enroll to subsequently discover the course is hard, boring or otherwise not what they expected. I expect this number varies strongly per course. On the other hand, not attempting/passing exercises doesn't mean someone hasn't invested time in watching all lectures.
The courses I've found so far haven't been all that helpful, even Khan academy is confusing me somewhat. Does this course explain things better?
2^x = (e^(ln 2))^x = e ^ (x * ln 2)
So MOOC strategy works for some areas that don't have sticking points like that, but not so well where five minutes with a Socratic strategy tutor could save a lot of video re-watching time. Arguably, better video would point out likely sticking points. However, advanced math instructors got their position not by getting education degrees which theoretically vocationally train people to present like that, but got their position by being very successful when getting a PHD in an obscure and advanced part of the field. So if you're lucky, your higher math instructor might be a talented educator in addition to being a talented mathematician... but probably not, unfortunately.
It would be interesting as a thought experiment, or maybe as a startup, to see a good (emphasis on good) K-12 math instructor with a strong background in educational teaching skills try to teach diffeqs or higher math in general. I suspect they'd be extremely good at it, although probably very slow.
I'd actually tried to understand it earlier, but I got stuck at the point it says:
We now use these equations to rewrite f (g(x + h)). In
particular, use the first equation to obtain
f(g(x + h)) = f(g(x) + [g′(x) + v]h),
and use the second equation applied to the right-hand-side
with k = [g′(x) + v]h and y = g(x).
f(g(x + h)) = f(g(x) + [g′(x) + v]h)
f(y + k) = f(y + [g'(x) + v]h)
1. Programming Mobile Applications for Android Handheld Systems – Part 1 / University of Maryland
2. Introduction to Philosophy / University of Edinburgh
3. Inspiring Leadership through Emotional Intelligence / Case Western Reserve
4. Introduction to Computer Science / Harvard University
5. Data Analysis and Statistical Inference / Duke University
6. Gamification / University of Pennsylvania / Wharton
7. Social Psychology / Wesleyan University
8. Circuits and Electronics / MIT
9. Think Again: How to Reason and Argue / Duke University
10. Creativity, Innovation and Change / Penn State
11. A Beginner’s Guide to Irrational Behavior / Duke University
12. Learn to Program: The Fundamentals / University of Toronto
13. Game Theory / Stanford University, University of British Columbia
14. Greek and Roman Mythology / University of Pennsylvania
15. Startup Engineering / Stanford University
16. Computational Investing, Part I / Georgia Institute of Technology
17. Financial Markets / Yale University
18. Introduction to Artificial Intelligence / Stanford University
19. Introduction to Computer Science and Programming / MIT
20. Introduction to Financial Accounting / University of Pennsylvania / Wharton
21. Modern & Contemporary American Poetry / University of Pennsylvania
22. Machine Learning / Stanford University
23. Data Analysis / Johns Hopkins Bloomberg School
24. Introduction to Computer Science and Programming Using Python / MIT
25. Science and Cooking: From Haute Cuisine to Soft Matter Science / Harvard University
26. Introduction to Philosophy: God, Knowledge, and Consciousness / MIT
27. Introduction to Operations Management / University of Pennsylvania / Wharton
28. Introduction to Mathematical Thinking / Stanford University
29. Justice / Harvard University
30. A History of the World Since 1300 / Princeton University
31. Creative Programming for Digital Media & Mobile Apps / University of London/ Goldsmiths
32. Neural Networks for Machine Learning / University of Toronto
33. Learn to Program – Crafting Quality Code / University of Toronto
34. Critical Thinking in Global Challenges / The University of Edinburgh
35. Statistics – Making Sense of Data / University of Toronto
36. Introduction to Biology – The Secret of Life / MIT
37. Drugs and the Brain / Caltech
38. Introduction to Databases / Stanford University
39. The Ancient Greek Hero / Harvard University
40. Social Network Analysis / University of Michigan
41. Health in Numbers: Quantitative Methods in Clinical & Public Health Research / Harvard University
42. Introduction to Astronomy / Duke University
43. Human Health and Global Environmental Change / Harvard University
44. Software Defined Networking / Princeton University
45. Introduction to Statistics: Descriptive Statistics / UC Berkeley
46. Computing for Data Analysis / Johns Hopkins Bloomberg School of Public Health
47. Functional Programming Principles in Scala / Ecole Polytechnique Federale de Lausanne
48. The Camera Never Lies / University of London/ Royal Holloway
49. Calculus One / Ohio State University
50. Maps and the Geospatial Revolution / Penn State