I often see people talking about how great they personally found a MOOC course, but that self-assessment is largely useless. To put it bluntly: you don't know what aspects of a topic you don't understand, were not told about, or whether the information you received was at all accurate, because you lack the expertise to determine that.
For example, people thought they had learned a lot from the Udacity Statistics 101 course and it received glowing reviews from participants. But unfortunately they did not learn about some of the most important basic ideas in statistics, and some of the things they did learn were just wrong:
http://www.angrymath.com/2012/09/udacity-statistics-101.html
the course is amazingly, shockingly awful. It is poorly structured; it evidences an almost complete lack of planning for the lectures; it routinely fails to properly define or use standard terms or notation; it necessitates occasional massive gaps where “magic” happens; and it results in nonstandard computations that would not be accepted in normal statistical work.
Astoundingly, the Udacity Introduction to Statistics course manages to go almost its entire length without ever mentioning or making any distinction between the population and sample in a study. I say I'm “astounded” because in my classes (and any one I've surveyed or looked at), this is the key idea in introductory inferential statistics. It's the very first thing that is mentioned in my class (or the book), and it's the very last thing on the last day, too. It's the entire reason why inferential statistics is necessary in the first place. In fact, the very word “statistics” means measures for one (sample) and not the other (population) – but you'll never learn that from this class.