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Yes, this is a bad time for the (what we think of as "traditional") music business, but just because the tools are "available" doesn't mean that there will be a huge increase in output. e.g. people have had the tools to write for years (pencils, paper, typewriters, word processors) - but professional authors and publishers aren't going away ...

even if you are a hack writer- it still takes "work"to produce a non-trivial product. That "work" part is my argument for why the scenario described won't happen ...

I'll even argue that the biggest change to happen to the music industry was the microphone and/or radio back in the first half of the 20th century not inexpensive computers and the internet. Back then "music companies" made money selling sheet music - which people would purchase to play at home. (I recommend this fantastic Coursera class to anyone interested in the history of modern music business) ...

I agree that the music industry is changing - but "convenience wins every time" is a spurious argument. Most of the "professional" musicians I've heard talk about "how they got into the music business" describe it as something they just had to do. They didn't just wake up one morning and say "I think I'll be a musician" - they followed their passion, put in the work, and eventually made it. That isn't going to change - "passion will beat convenience" no matter what technology comes along ...

We're building a Calculus Two course, and a complex analysis course.

but does it count to credits?

Information about the actual course is located on https://www.coursera.org/course/calc1

Notable information is the class start date, August 23, and the result of taking the class, which is that you get a certificate signed by the instructor. The class is currently in progress (you're too late); the class lecture videos are much of the content are are on various instructor's YouTube channels.

What is checked into Github is the website and backend. There is no license that I can see for any content except (c) 2013, mooculus team, at the bottom of the site's non-doctype'd HTML. Math geeks can't nerd.

If you are an intermediate level programmer as you say then you can easily learn to use a new programming paradigm. There is a coursera course https://www.coursera.org/course/hetero which is ok and should do for your purposes.

I took that course: https://www.coursera.org/course/hetero

I also took a course from Udacity: https://www.udacity.com/course/cs344 but this one I didn't finish, I've done perhaps 30% of it (I already had finished Coursera's). One of these days I'll go there to close matters :-)

The courses in Udacity are "always online", so anyone can register anytime and finish the course with his/hers own pace. Quizzes, exams and grading with certificate included have no fixed limits. On the other hand, the courses from Coursera have deadlines and run more or less in parallel with "snail" university schedules, with start and stop dates, with time limits in quizzes and exams, etc. (You can usually see videos, and do quizzes anytime after they end, but no certificates and grading AFAIK).

Both courses were good -- I recommend both, -- we did homeworks in Amazon's cloud transparently, and certainly both were "sponsored" by Nvidia, coz we learned only CUDA. (Perhaps there was a brief blah blah about competing alternatives.)

But from what I've seen, if someone is afraid from CUDA, then its better to run away very fast from alternatives (OpenCL) :-)

Coursera has some courses on GPU programming, like this one, and what's nice about them pretty slow, and I'm assuming that they explain things well. Other online courses probably offer the same, and I think the video lectures would be helpful in understanding the concepts.

On Coursera you can find the Stanford course "Introduction to Databases" by Jennifer Widom. https://www.coursera.org/course/db . It is free and covers a very broad range of database topics.

This guy built a self-driving car powered by an android phone and a laptop. He did something similar with a raspberry pi in place of the phone. I find this fascinating. In essence, he taught the car to drive by driving it around a black track delineated by white boundaries, with the computer recording a basic video of his driving technique. The neural network was then trained to drive like a human. The neural network ideas were contained in this free Stanford Machine Learning Course by Andrew Ng. It would be unbelievably cool to me if someone could make this technology more accessible to a wider audience.

By comparison, the Coursera course is child's play.

Yes, Udacity is not Coursera. Nonetheless, I think Georgia Tech has a lot of work ahead before their MOOC CS curriculum will be ready for prime time.

If you really want to learn about _working_ AI and not "when I was a boy we did it in the snow, both ways, uphill" then do
https://class.coursera.org/ml/class
Machine Learning by Andrew Ng.
After that you can do
http://work.caltech.edu/telecourse.html
Learning from data by Yaser Abu-Mostafa

Half of Hintons course was about history and what didnt work in AI. Its great to know those things if you have interest in the field, but its not something you should start with (snorefest).

He has a course on them at coursera that is pretty good.

https://www.coursera.org/course/neuralnets

Here is their company blog post with a partial list of courses.

http://blog.coursera.org/post/49331574337/coursera-announces-professional-development-courses-to

If capitalism doesn't provide a profit motive to develop alternative forms of energy, government should.

We can create money to fund research into ideas that the free market doesn't immediately reward. The Fed creates money now; but it goes to the banks at 0%, who want to buy T-bills even if they only pay 2% or 3%; but the austerity-pushing Republicans want to limit the sale of T-bills. So the banks sit on the money instead*, and get interest on it if they store it with the Fed.

Instead, give the Fed's created money directly to people, in the form of a basic income. Encourage individuals to innovate on their own or through ad hoc collaborations facilitated to an unprecedented degree by the internet. (Note that the market was too short-sighted to fund the creation of the internet; AT & T felt the internet threatened their business model of telephones, for example.) In this age of MOOCs we can educate ourselves about energy and work on hypotheses that business won't pursue because they are too driven by the requirement that they show a profit on next quarter's shareholder report.

---

* See http://www.federalreserve.gov/releases/h3/current/. Current reserves are at or near all time highs.

For anyone interested in learning more about x86-64, Coursera, in conjunction with UWashington, just started a "Hardware/Software Interface" course that focuses on 64-bit processors.

First of all, the paper is steeped in jargon. Phrases such as (2nd para) "characterized by the formalism of" instead of "described by" obfuscate the meaning and confuse the reader.

Count the number of uses of the verb "to be" (is, are, "to be", were, &c). It's everywhere! Nothing runs or changes, everything "is running" or "is changing". Passive voice removes the actor in a paper describing - largely - actions.

Useless words and phrases litter the landscape, such as "To better understand", "for concreteness", "as a simple means", "to the best of our knowledge". Cushiony adverb goodness pads the document to the required length. "Explicitly propose" (instead of just "propose"), "dynamically revealed information" (as opposed to that other kind of revealed information), "remarkably sophisticated behaviours" (as opposed to the pedestrian kind, I guess).

This paper is all kinds of awesome! It should be the touchstone for Stanford's "Writing in the Sciences" online course.

My first impression (it's really dense!) is that the author conflates maximum entropy with the agent goal. It's not always the case that the goal is the maximally entropic state. This is likely true when actors must cooperate (as shown in the paper), but when actors compete the individual goal may not be maximum entropy.

For example, consider competing for mates. Instead of choosing mates based on competitive merit, should an individual limit their offspring in order to give everyone in society a chance to reproduce? The non-cooperative goal isn't for maximal entropy.

It also appears to describe intelligence as an evolutionary process seeking a function minimum over multiple-parameter phase space. While this might solve physical puzzles such as walking or throwing a ball, I'm not convinced that chess can be solved in this way. The search space is too big for an evolutionary solution.

Still, I may be misapprehending the point of the article (it's really dense!). Read the paper and make your own assessment.

This course on Coursera describes the basics of that technology. It's in its final week but I'm sure it will be reissued later on. They have other courses about computer vision announced for the next months.

The course was pretty interesting but you don't really have to do any programming to get a grade (programming assignments are optional). Lucky for me, because I have a job and no time to spend on lengthy programming assignments, but one can't become an expert of that subject just by listening at the lessons and doing the multiple choice quizzes.

I've been taking online courses for two years(*), and my conclusion is: it's not the subject, it's the presentation.

I've come to the realization that college professors - even highly esteemed professors from highly esteemed universities - don't know much about the actual technique of teaching, nor of presentation.

Every course I've seen so far goes against the grain of how we learn, or has features which repel attention. Droning talk with hypnotic rhythm, no vocal variety, poor spacing and timing, and filled with pauses and disfluencies which put the student to sleep (Daphne Koller, Stanford). Tedious derivations with no initial apparent purpose and no apparent endpoint which go on and on, suddenly ending with simple result (Anant Agarwal, MIT). Pointless exercise and homework with no apparent relevance to the subject (Richard Buckland, UNSW). The list is endless.

People who give lectures for a living - public speakers, professional salesmen, life coaches, and so on - have this figured out. They *have* to, because their livelihood depends on it. Their presentation has to capture interest, have relevance, have value to the listener, and be easily understood.

College professors sing to a captive audience with no feedback. If students don't do well, it's because of the course content; or it's because the students are not "Stanford level" or whatever. Stanford is considered tough, but no one ever wonders whether it's because the quality of teaching is low. Colleges aren't rated highly when they can teach anyone, they are rated highly when they can only teach the top students.

The typical online course just videotapes a lecture and throws it up on the net with some homework and grading software. There is no rehearsal, no redoing of bloopers or flubs, nothing one would get in a professionally-made video. The homework is generally "one question per concept" and is often "get it right the first time". No room for experimentation, multiple practice, or exploration. No feedback or watching the professor run through an example.

They wonder why the attrition rate is so low, it's obvious.

It's because their methods are just bloody awful.

(Note: I've scored high 90's in each course so far. The material isn't that tough, if you've ever had a good professor you know how understanding is easy when well presented. Blaming the content or the student is a dodge - very little is difficult to understand if it is taught well.)

I've been taking some online classes through http://coursera.org/, when I log in from work where I'm only allowed to use IE on Win7 I get a big banner telling me to upgrade to a modern browser.
I can stil view the site, access the video lectures, take multiple choice quizzes, and most everything else but for some reason it won't allow me to submit essay type quizzes.

Coursera, too ( at coursera.org ). I know they have several programming classes in rotation. Not sure which ones will be available during the summer window, but it would be pretty easy to find out or keep an eye on as they open up. I dabbled with a class that involved Python programming to create computer games, and it was both well presented and slightly more fun than the average non-games-programming class. (Proper link: https://www.coursera.org/course/interactivepython - currently TBA.)

Depending on timing, there may also be related topics (databases, math, logic, mobile devices, etc.) if he wants to take a couple of classes at once. I'm currently taking a databases class from Stanford (previously also released once via Coursera) that's proving educational and quite challenging.

Purists might contend that lisp, ocaml or haskell are the only ways to grok functional programming. Nevertheless, a functional/OO hybrid that runs on the JVM might be a nice complement to the ubiquitous Java courses this kid may encounter. (Do they still use Java as a teaching language?!)

https://www.coursera.org/course/algo About the Course In this course you will learn several fundamental principles of algorithm design. You'll learn the divide-and-conquer design paradigm, with applications to fast sorting, searching, and multiplication. You'll learn several blazingly fast primitives for computing on graphs, such as how to compute connectivity information and shortest paths. Finally, we'll study how allowing the computer to "flip coins" can lead to elegant and practical algorithms and data structures. Learn the answers to questions such as: How do data structures like heaps, hash tables, bloom filters, and balanced search trees actually work, anyway? How come QuickSort runs so fast? What can graph algorithms tell us about the structure of the Web and social networks? Did my 3rd-grade teacher explain only a suboptimal algorithm for multiplying two numbers?

Maybe you'll enjoy this:
https://www.coursera.org/course/gametheory

It has barely started and it's supposed to be introductory.

You are comparing a single action to a series of actions, and then claiming the latter one is superior because repeating it often enough results in more data!

Any machine that only ever performs a single action is not Turing complete. Performing multiple actions sequentially is exactly how computers operate, and is what makes them useful computational devices. If you've been arguing from this perspective, your point still does not hold. Since your computer can only perform one operation, it can only do one of three things only only once: move its read position, change its state, or write a single bit. Thus, the only precision you can achieve is flipping one bit, so the keyboard is *still* the most accurate input device you can achieve.

Now, if you were to compare apples to apples you could use either input device to get the exact same result if you just repeated the action often enough. You could enter 1.000 units with a mouse just as well as you could enter it with a keyboard.

If you read my original comment I said the keyboard is the most precise device you could create. Any device which reduces to the functionality of the binary keyboard, like clicking a mouse button, is therefore obviously just as precise. As long as you can manipulate the data stream on a per bit basis, you've achieved maximum precision. Something like a mouse optical sensor or inertial unit does not work this way (although I could think of a couple ways to rig them to do so). What still is not true is your initial assertion that you could create a device *more* precise.

If you are still confused by any of this, may I suggest a course on Automata Theory? I haven't taken this particular one, but looking at the syllabus, you should have all the information by Week 6 to arrive at the same conclusion I am presenting here.

Here's a program that actually looks appealing, and I'd consider paying for it (I think about $2k for the year-long program):
http://www.pce.uw.edu/certificates/data-science.html

It's a graduate certificate in data science. It's my understanding the guy behind it (J. Nathan Kutz) is the same one who teaches a couple of the data science courses on Coursera for free. So for some money and a bit more rigor in your work and assignments, you get an actual certificate you can put on your resume.

Here are the Coursera courses:
https://www.coursera.org/course/compmethods
https://www.coursera.org/course/scientificcomp

If Andrew Ng and Stanford offered some kind of real credit and certificates, I'd be all over that.

Here's a program that actually looks appealing, and I'd consider paying for it (I think about $2k for the year-long program):
http://www.pce.uw.edu/certificates/data-science.html

It's a graduate certificate in data science. It's my understanding the guy behind it (J. Nathan Kutz) is the same one who teaches a couple of the data science courses on Coursera for free. So for some money and a bit more rigor in your work and assignments, you get an actual certificate you can put on your resume.

Here are the Coursera courses:
https://www.coursera.org/course/compmethods
https://www.coursera.org/course/scientificcomp

If Andrew Ng and Stanford offered some kind of real credit and certificates, I'd be all over that.

Whoever did the reasearch for this needs to take this course:
https://www.coursera.org/course/foe - "Fundamentals of Online Education: Planning and Application"

Ivy league is now all free, in your own time and 100% online now: https://www.coursera.org/ To be honest whether or not you have a degree makes no difference by itself. What you know will shine through in any real technical interview. I've interviewed CS PhDs that couldn't code their way out of a paperbag - and on the other hand many of best programmers I know are no-degree "ferrels". Anders Hejlsberg to name one. You should concern yourself with learning. Read CS textbooks and do the exercises (knuth, ullman, cslr). Compete in programming competitions. Set yourself some ambitious project work like a compiler or an OS kernel. Write And Read lots of code written by lots of different people that does lots of different things. Programming is a craft

(Check out the online videos for Probabilistic Graphical Models by Dr. Daphne Koller at Stanford. Alternately, check out her book on the subject. The book is largely unreadable, and the videos are dreadfully obtuse. Her class at Stanford is well known as a weeder.)

I'll have you know that I at least, as a Ph.D. in a related field, have no problem what-so-ever with any of those lectures. They are terse and succinct, describing only the necessary details of probabilistic graphical models to an audience already familiar with both probabilistic models and graph theory.

Probabilistic Graphical Models by Dr. Daphne Koller at Stanford

The course slides use Comic Sans as a font? Most horrible idea ever. Other than that, the course materials look quite ok, but as usual with mathematical material, reading and listening to it does not help much anyway - you need to work with the stuff yourself. A good presentation only helps marginally.

Compare a course where you would retain 30% of the content with a course where you would retain 70% of the same content: which would you choose?

Everyone whining about "pandering to the unmotivated" is missing the point: the current class/lecture model started over a thousand years ago and is not optimized for learning. In this century we now know much more about the neurological underpinnings of how people learn, so it makes sense that we should try to optimize the process.

College (or an online course, or work-related training) should be as effective as possible. Some lecturers have this figured out, but most don't.

Stanford is considered a hard school not because the material is difficult, but because it's presented in a way that's hard to learn. Only the brightest and most motivated students can thrive in that situation, which helps to build the "best and brightest" reputation. The reputation comes not from quality of education, but difficulty of education.

(Check out the online videos for Probabilistic Graphical Models by Dr. Daphne Koller at Stanford. Alternately, check out her book on the subject. The book is largely unreadable, and the videos are dreadfully obtuse. Her class at Stanford is well known as a weeder.)

One great aspect of the ongoing MOOC revolution is that everyone is competing on an open field. Instructors using more effective techniques will be perceived as better teachers while the "old-school, cannot change, it's always worked for me" crowd will be left in the dust.

Gamification is a technique for more effective teaching.

Good. I hope everyone benefits from this feature. MOOC has been a boon for me, and I suspect, for others as well.

One remarkable thing that recently came out of Coursera is Rice University's CodeSkulptor . With CodeSkulptor, I can write interactive games in Python (with additional help from CodeSkulptor's library functions).

You can do all that if you take the course "An Introduction to Interaction Programming in Python" . It's a lot of fun.

Not just that.

Suppose you're 68, retired and bored. You live 300 km from the nearest university. And you don't have the qualifications to get in to university either.

If you are willing to put in the time and work required to learn about astronomy, including reading up on the maths and physics involved as it pops up, why shouldn't you be able to follow a course on the subject?

You may find out later, that this isn't something you have the necessary skills for yet, or that it's a lot more boring than you thought, but if you're curious about it, but you've never had the chance to learn about it outside of TV, why not?

Humans are rarely too old to learn new things.

I don't know, Stanford Online has taught Cryptography and Networking- two upper level undergraduate CS courses at my university. And Coursera has a Databases course too. Sure, these courses might be vastly outnumbered by the number of "Python Introduction Tutorials", but that doesn't mean they don't exist.

Okay, I'm not Anonymous, and I haven't taken any Richard Buckland courses.

I have been involved with the MOOC movement since last year (Dr. Thrun's AI class), taken several online courses, and study human learning for my day job. I've evaluated and compared the teaching styles of MOOCs for my own purposes.

From what I've seen of his work online (YouTube videos), Richard Buckland is the best.

In my opinion his style of presentation maximizes the student interest. Regardless of the content, Richard Buckland will make learning enjoyable; he will cultivate the student's interest and perceived value.

Coursera and edX believe in the "learning is hard" model - they present artificial barriers and difficulties so that only the most intelligent and dedicated student will complete the course. For an example, watch the first lecture or two of Daphne Koller's "Probabalistic Graphical Models" online course.

Richard Buckland takes the view of "learning is fun", and does everything he can to motivate the students. He's been trying out different techniques over the years, keeping what works and dropping what doesn't. At this point in his career, he's got a pretty good handle on what encourages students to learn.

I predict that "The Art of Programming" will have the highest completion rate of all the online courses.

Of the course offerings and business models I've seen, this is likely to be the best one to date.

Okay, I'm not Anonymous, and I haven't taken any Richard Buckland courses.

I have been involved with the MOOC movement since last year (Dr. Thrun's AI class), taken several online courses, and study human learning for my day job. I've evaluated and compared the teaching styles of MOOCs for my own purposes.

From what I've seen of his work online (YouTube videos), Richard Buckland is the best.

In my opinion his style of presentation maximizes the student interest. Regardless of the content, Richard Buckland will make learning enjoyable; he will cultivate the student's interest and perceived value.

Coursera and edX believe in the "learning is hard" model - they present artificial barriers and difficulties so that only the most intelligent and dedicated student will complete the course. For an example, watch the first lecture or two of Daphne Koller's "Probabalistic Graphical Models" online course.

Richard Buckland takes the view of "learning is fun", and does everything he can to motivate the students. He's been trying out different techniques over the years, keeping what works and dropping what doesn't. At this point in his career, he's got a pretty good handle on what encourages students to learn.

I predict that "The Art of Programming" will have the highest completion rate of all the online courses.

Of the course offerings and business models I've seen, this is likely to be the best one to date.

I've been "tasting" the various online courses for the last 15 months or so: started with Dr. Thrun's online AI course, have contacts with people at edX, have taken or viewed courses from a half-dozen entities.

One salient aspect of all of the MOOCs is their overall poor quality.

The teachers are, as a general rule: smart, familiar with the subject, nice people, and well meaning.

The online courses are, as a general rule: boring, poorly presented, supported by poor online tools, and counter-instructive.

Everyone realizes that education is changing, and that in ten years or so there will only be a few players left. Everyone wants desperately to be one of those players, so you have everyone frantically recording lectures and putting them online in a desperate attempt to remain relevant.

Sebastian. Thrun's AI course never bothered to check or correct errors in content, resulting in massive frustration from the students. Anant Agarwal's electronics course had students drowning in directionless theory that suddenly uncovered a useful equation. Daphne Koller's presentation style makes the simplest concepts appear dense.

To give an representative example, Kristin Sainani over at Coursera is running a course on scientific writing (writing for purposes of a published paper, or review of said paper &c). The course content is very good, but the students edit and grade each others' homework.

Perhaps 80% of the students speak almost no English. The end result: 80% of the editing work is tediously instructing other students not on course content, but on basic English (when to use articles, which prepositions to use when, &c), while 80% of the corrections you receive for your work are utterly useless. The overall experience is "massive waste of time for a course of heavily diluted value".

There are occasional standout exceptions, but the overall quality is very low. No one has quite realized that you can't just videotape a lecture and put it up on the web - you have to plan things out ahead of time, add good production value, and have good support. It's not easy, and no one group so far is doing it particularly well.

Online learning is still in beta. Perhaps in a couple of years the technology will mature.

I've been "tasting" the various online courses for the last 15 months or so: started with Dr. Thrun's online AI course, have contacts with people at edX, have taken or viewed courses from a half-dozen entities.

One salient aspect of all of the MOOCs is their overall poor quality.

The teachers are, as a general rule: smart, familiar with the subject, nice people, and well meaning.

The online courses are, as a general rule: boring, poorly presented, supported by poor online tools, and counter-instructive.

Everyone realizes that education is changing, and that in ten years or so there will only be a few players left. Everyone wants desperately to be one of those players, so you have everyone frantically recording lectures and putting them online in a desperate attempt to remain relevant.

Sebastian. Thrun's AI course never bothered to check or correct errors in content, resulting in massive frustration from the students. Anant Agarwal's electronics course had students drowning in directionless theory that suddenly uncovered a useful equation. Daphne Koller's presentation style makes the simplest concepts appear dense.

To give an representative example, Kristin Sainani over at Coursera is running a course on scientific writing (writing for purposes of a published paper, or review of said paper &c). The course content is very good, but the students edit and grade each others' homework.

Perhaps 80% of the students speak almost no English. The end result: 80% of the editing work is tediously instructing other students not on course content, but on basic English (when to use articles, which prepositions to use when, &c), while 80% of the corrections you receive for your work are utterly useless. The overall experience is "massive waste of time for a course of heavily diluted value".

There are occasional standout exceptions, but the overall quality is very low. No one has quite realized that you can't just videotape a lecture and put it up on the web - you have to plan things out ahead of time, add good production value, and have good support. It's not easy, and no one group so far is doing it particularly well.

Online learning is still in beta. Perhaps in a couple of years the technology will mature.

I've been "tasting" the various online courses for the last 15 months or so: started with Dr. Thrun's online AI course, have contacts with people at edX, have taken or viewed courses from a half-dozen entities.

One salient aspect of all of the MOOCs is their overall poor quality.

The teachers are, as a general rule: smart, familiar with the subject, nice people, and well meaning.

The online courses are, as a general rule: boring, poorly presented, supported by poor online tools, and counter-instructive.

Everyone realizes that education is changing, and that in ten years or so there will only be a few players left. Everyone wants desperately to be one of those players, so you have everyone frantically recording lectures and putting them online in a desperate attempt to remain relevant.

Sebastian. Thrun's AI course never bothered to check or correct errors in content, resulting in massive frustration from the students. Anant Agarwal's electronics course had students drowning in directionless theory that suddenly uncovered a useful equation. Daphne Koller's presentation style makes the simplest concepts appear dense.

To give an representative example, Kristin Sainani over at Coursera is running a course on scientific writing (writing for purposes of a published paper, or review of said paper &c). The course content is very good, but the students edit and grade each others' homework.

Perhaps 80% of the students speak almost no English. The end result: 80% of the editing work is tediously instructing other students not on course content, but on basic English (when to use articles, which prepositions to use when, &c), while 80% of the corrections you receive for your work are utterly useless. The overall experience is "massive waste of time for a course of heavily diluted value".

There are occasional standout exceptions, but the overall quality is very low. No one has quite realized that you can't just videotape a lecture and put it up on the web - you have to plan things out ahead of time, add good production value, and have good support. It's not easy, and no one group so far is doing it particularly well.

Online learning is still in beta. Perhaps in a couple of years the technology will mature.

The "Writing In the Sciences" online course over at Coursera says to distinguish between "compare to" and "compare with".

"Compare to" is used to find similarities, as in "shall I compare thee to a summer's day?". "Compare with" is used to find differences, as in "His time was 2:11:10 compared with 2:14 for his closest competitor." (Many sources on the net.)

So I have to ask, was he being compared to Jerry Sandusky, or compared with Jerry Sandusky?

Inquiring [Scientific writing] minds want to know :-)

The "Writing In the Sciences" online course over at Coursera says to distinguish between "compare to" and "compare with".

"Compare to" is used to find similarities, as in "shall I compare thee to a summer's day?". "Compare with" is used to find differences, as in "His time was 2:11:10 compared with 2:14 for his closest competitor." (Many sources on the net.)

So I have to ask, was he being compared to Jerry Sandusky, or compared with Jerry Sandusky?

Inquiring [Scientific writing] minds want to know :-)

Out of curiosity I went to https://www.coursera.org/. What do I at once on the first page?

Introduction to Genetics and Evolution

Okay Mods, here I go, this is coming from concerned frustration and is not intended as flamebait! I'm using a couple of rhetorical flourishes, so let's hope I don't misfire them.

I believe the professor's comments are tragically flawed, starting with one reason. They might have worked for *any other subject*, combined with a more constructive goal of "how do we refine next year's class for the best experience" etc. But what is the subject here? Wait for it ... Statistics. The art of studying a Sample from a Population, right?

So in this evaluation, the Sample Size is One Class. Sorry Prof, you mentioned *three* other sources of online classes, namely OpenCourseWare, Coursera, and edX (I'm leaving off Khan Academy, it's structured differently). A glance at Wikipedia lists even more. So my first concern is why that sample class is being equated to free statistics courses in general and even worse, online learning as a whole. Some examples:
https://www.coursera.org/course/stats1 - Coursera's version of Stats 1.

Then for the criticisms:
1. Lack of Planning
2. Sloppy Writing
3. Quiz Regime
4. Population and Sample
5. Normal Curve Calculations
7. Bipolar Difficulty
8. Final Exam Certification
9. Hucksterism
10. Lack of Updates?

Let's separate those out into Badly Written Course complaints, that can apply for *any* course, including traditional ones. Those are:
1. Lack of Planning
2. Sloppy Writing
4. Population and Sample
5. Normal Curve Calculations
6. CLT Not Explained

So for my fellow Slashdot Readers, the ones for us to thrash around are the ones dealing with the Online Concept.
3. Quiz Regime
7. Bipolar Difficulty
8. Final Exam Certification
9. Hucksterism
10. Lack of Updates

This word "worth" means different things to different people. In fact it has different value at different times in the life of each person.

So in the end, for me a PHD in the middle of my life where I have other distractions is more than too hard, but I do enjoy learning, and in fact I am partaking in Algorithms. Then again I am not you. I expect most of the constructive replies to simply try and pin down the value of "worth"

I don't know... I don't see any drinking courses... https://www.coursera.org/illinois

Through Coursera, Rice University is offering a free online course in a few weeks on introductory Python programming with a specific focus on games:

https://www.coursera.org/course/interactivepython

Why not try it? If you don't like it or it otherwise doesn't work for you then you've lost nothing.

I don't know if anyone has been keeping track, but there's this thing called the internet where you can get a really good education for free. Tablets will give children access to this internet.

We currently have four major players in this arena:

• Khan academy, for high-school up through 1st year college
• Coursera, college level
• MITx/edx, college level
• Udacity, college level

This is in addition to all the universities which are putting lecture videos online, along with course materials and (in a few cases) the textbook content. Oh, and youtube videos of lectures, and the zillion-and-one websites explaining whichever subject you're interested in. Google "relativity" or "tensors" sometime - see if you can find an explanation that works for you.

An experiment in India has shown that when you give uneducated, poor children access to an internet-connected computer they figure things out on their own. Complex, interesting, and difficult things that you might not expect an ignorant user to manage. (Such as typing a thank-you note without access to a keyboard.)

This is all you need, kids will figure things out for themselves. Having a teacher to nudge them in the right direction, or help them over a difficult part is just gravy.

Kids are voracious learners, and have always been. Abe Lincoln used to sit at home practicing his "ciphering" (arithmetic) by drawing numbers on a shovel with charcoal. Over and over, until he got comfortable with the math. All kids do this - it's in the nature of growing up.

Just giving kids access to material will be a huge leap over the current situation. Schools and teachers are extra.

Khan Academy has a good series of ten-minute instructional videos about the theory of chemistry and organic chemistry, starting from first principles, this would be a good place to start.

http://www.khanacademy.org/#chemistry
http://www.khanacademy.org/#organic-chemistry

There are many online university lectures, which might be a little too advanced for a 10 year old, but they available anyway:
http://www.academicearth.org/subjects/chemistry
http://www.youtube.com/ (Search for long >20 minute videos)
https://www.coursera.org/ (Doesn't offer chemistry yet, but may do in the future)

I myself missed out a large part of my formal education and as a result became mostly self-taught. Being homeschooled means you don't have any deadlines or exams to worry about. The core thing to maintain is curiosity (the willingness to ask questions) and the confidence ans skills to go about answering them for yourself. Google is your biggest friend!

The approach is very different from structured learning. Pick a question, a project or a task. Jump in at the deep end, google the question directly, even its is rather advanced. The explanation will probably be full of alien words and concepts that you don't fully understand and simply raise up an even bigger pile of questions. So pick the first of these new questions, and keep drilling down until you have a good enough understanding of each word or concept that you can start to make sense of the original answer to the original question. Rather than trying to cover a pre-defined syllabus in sequential order and to a given timetable, you are aiming to drill down to whatever level of detail is needed in order to have the clarity required to answer the question you are interested in. It seems slow at first, but by the time you have fully answered your first proper question, you will have already covered half the syllabus. Age then becomes irrelevant and as long as you keep asking questions, you never stop learning.

Sugata Mitra has an interesting take on the power of simply giving children the tools to teach themselves:
http://www.ted.com/talks/sugata_mitra_the_child_driven_education.html
http://www.ted.com/talks/sugata_mitra_shows_how_kids_teach_themselves.html

As the parents and the grandparents are not confident in teaching the subject, maybe you should turn the tables, set the 10 year kid the challenge of teaching the grandparents how do "French Cooking" (as it was once known).

I've been using Octave (an open source version of Matlab) in Stanford's online PGM course. My first reaction was "great matrix manipulation library, extremely bad language". It's like time travelling to the 70's and discarding every progress CS made in the last forty years. Actually Matlab has object oriented classes now but somebody commented in the PGM forums that it's not so good. (Octave uses an older Matlab OO syntax I'll be merciful not to comment about.) I don't have any direct experience with R but on the PGM forum I read that its status is not so different.

My suggestion to the scientific community is to work on replacing those old languages with something modern, even Python which I cordially hate because of that white space thing. Obviously you need a fast (written in C) scientific library and an interactive prompt is extremely handy. Python and Ruby are sensible choices IMHO. Matlab and R won't disappear, Cobol didn't go away, but there is no reason why a 20 years old student shouldn't start coding with a modern language, if it's on par with the old ones (a big if, I know).

What about http://www.coursera.org/?

And there seem to be some introductory courses from both Stanford's Coursera and Udacity (Sebatian Thrun's new start up).

They're programming oriented, if I'm not mistaken, though.