Coursera/Stanford online algorithms I course – a retrospective
eli.thegreenplace.net
eli.thegreenplace.net
I think it's an indicator of how deeply broken our education system is that when assessing the value of a class people worry about how smart it made them feel (this is after all what it really means to pass a 'difficult' class), rather than how much they learned and could do with the materials after the class.
Sebastian Thrun even made a comment somewhere that he realized for years he'd be making his standford classes hard, not for the sake of learning but for living up to the reputation of being hard.
A class that feels 'easy' but which has a high amount of 'new stuff learned' should be the pinnacle of what a class can achieve. Difficulty should be a negative property of learning, sometimes necessary, but never desirable in and of itself.
The MIT electronics class often poses questions during the lectures or in the homework that are based on what you've already covered but involve concepts which are still upcoming.
This is difficult, but it's vastly superior as a learning technique as you have to work your way through it for yourself.
The COMP 201 course at Rice University (intro comp sci for non-majors) was the experience that started me on a career path in software/web development. Several of my classmates dropped the course because the instructor used such challenging problems on chapter exams. Even though I hit/exceeded the exam time limit almost every time, I found the exams to be incredibly rewarding. I vividly recall grinding my teeth over an exam prompt asking us to write a set of controls for an RC car that could be driven with a TV remote control and also needed to elegantly remember macros of control sequences... only to show up the next day in the professor's office and be handed a remote control, driving the actual RC car with the solution from my exam. It's hard to beat engagement like that.
However, I still appreciate Coursera for what it is. I'm taking the machine learning course right now, and I feel that I'm gaining a fair amount of knowledge relative to the amount of time I can put in. If I were to dedicate more time to my post-academic education, I'm sure I'd want something that moved at a faster pace or set more difficult challenges before me. It just doesn't seem useful to judge Coursera negatively because it doesn't meet an expectation it wasn't designed to meet.
I once had a professor introduce me to a colleague as "He was the A." He seemed to think the remark praised him as much as me. Only in primary schools do you hear a few voices saying "90% of the class should be performing at A level".
I note that this reviewer didn't get full marks (perfect score) in the class. It's interesting to contrast the review, also on HN, from one who did. http://henrikwarne.com/2012/05/08/coursera-algorithms-course...
I read another review by a current Stanford student who complained that the pre-Coursera Machine Learning class was too easy. I wonder how he did? A close friend of mine took that course and earned a perfect score. He didn't complain it was too easy; he went back to his company and started a group to apply ML to their business. Easy/hard is beside the point. The point is: students learn.
It's not helped by posts like this, the arrogant 'oh it was all too easy' tone is frustrating. Not everything in life is a competition, and as others have said, it ought to be about actually learning something, not proving how wonderfully clever you are.
What's particularly frustrating in all of this is that I found this particular course wonderfully lectured, and the material fascinating. Reading stuff like this takes away that sense of fascination at the material and replaces it with a sense of being a not quite good enough participant in a competition I didn't want to be a part of.
/rant
The fact that the course has managed to reach out to all sorts of people (including someone like you) is something that should be celebrated. That it was designed so that someone who isn't 'good at math' (whatever that means) struggled a bit, but still got through it all is amazing. That's something that really makes a great introductory course.
After all, it's stupid easy to make a course hard, or to assign shit loads of work. The real trick is making it just hard enough.
> I still wish that the course would try to cater for the stronger
> students as well, so that completing it with a high grade would give
> a real sense of accomplishment.
This is contrasted to the brutal method of being forced to learn things within a short period of time, often forgetting what you learned or just picking up banal mechanics. The focus shifts back to thoroughly learning course materials.
Online education is a true equalizer.
NB: Been a fan of online education since OCW started up where the costs of the raw material plummeted to the cost of your internet connection.
Coursera's talked about maybe taking videos down for a while between courses, I hope they don't. I'm going to save them just in case, but it'd be nice to have the inline quizzes.
I probably used every curse I know while working on the 5th PGM assignment, but I am the first to admit that I did learn a lot, and I'm sure they will fix the rough edges for the second run of the class. Moreover, the entrepreneur in me is very inspired to see fresh-from-the-oven code. Sure, I've already seen apps that were lacking some features because they were released fast, but here I have an opportunity to actually see some fresh code (since we are basically asked to "fill in the blanks" in Daphne's implementation). I admire Daphne's entrepreneurial courage to publish something even if is is not 100%.
Also note that the PGM class has many more pointers to recent research in the field [1], which I think the OP would find interesting.
Finally, I think the OP is missing a bit of the bigger picture - comparison of undergraduate studies in Israel and the US. I could probably write a long post about this one day (I've taught science undergrads in both college systems), but at least I should point out a couple of things. In Israel, there is much more focus on the major; about 95% of the classes are in the major field of studies. (One chooses her/his major before applying to college). There are (almost) no GE classes [2]. There is probably no one attending CS classes who does not major in CS (or double major). There are advantages and disadvantages of each system. But as a result, you can put more challenging content into CS classes in Israel.
Therefore, it would probably be more fair to compare a CS class from the Technion to upper-division or first-year grad class. That is, instead of comparing it to Stanford's CS161 (or any other 100-199 classes), it would be more fair for the OP to compare it to the level of CS228/CS229A (or almost any other 200-299 classes).
[1] Not all of these are plugs to Daphne's research- I even remember some pointers to Thruns' papers. (he's sort of a competitor.)
[2] Perhaps this is to allow 3 years college, and save some time we spend serving in the army.
So I know you're not really arguing here but I just wanted to put that out there since you mentioned the forums. Carry on!
The programming exercises were fun, but I don't think they were really the point of the course - this "part 1", at least, was much more about analysis than about design.
As a contrast, I've just started their compilers course: there the theory questions are slightly easier, whereas the programming questions are much more involved - the first one has already taken me several hours and I'm nowhere near finished.
we have a wide array of students to cater to and better setting expectations and providing challenges at the right level to the right students is something that we hope to iterate on and improve over time.
That said, if you are looking for really hard challenges, you should take Daphne Koller's Probabilistic Graphical Models.
Recall the Master Method and its three parameters a,b,d. Which of the following is the best interpretation of b^d, in the context of divide-and-conquer algorithms?