I watch standalone lectures, because there's no commitment to keep going. You trade a wider variety of content with less depth in one area.
In my opinion, the challenges of self-teaching are to be able to study regularly over a longer period of time, and not only read/watch things but do exercises. Those things are enforced at school, but can be difficult to reproduce in a less constrained setting. For that, the MOOC are quite useful. My only grief is that I usually don't like the videos. I'd be happier with a book.
I'm usually listening to things while cleaning, driving, etc, for entertainment rather than maximizing learning. My experience has been that if someone introduces a really novel concept or historical fact in a talk, it will sink in and stick with me for a long time.
I've found a lot of fantastic material in this university's lectures- http://www.gresham.ac.uk/
I'm working on a search engine to curate a wider range of these, although it's still very much a work in progress-
There seems to be a bug with the filter on the left side: after you select one and the page reload, subsequent attempts to filter won't work.
I actually have been working on a similar kind of search engine, albeit being a more "proper" search engine + crawler combination rather than curation likes yours. It's great to see other works like yours for inspiration :-).
Would love the ability to search through these materials that are audio only.
I'm working on adding a feature so that every search result can be subscribed to as a podcast as well, so you can listen to everything on one topic by different people.
I'd love that podcast subscription capability, though listening to it in HTML5 means I can speed up the playback without having to download the audio file first (my podcast app, PocketCast, doesn't let me do this).
A while ago, I was thinking about doing a search engine on top of audio. The startup deepgram seemed to offer interesting tools, but didn't get a chance to play with it: https://www.deepgram.com/
Currently I have it searching transcripts, but I want to get machine transcriptions in as well, because it should work better how often someone says 'um', which I'm going to try as one of the ranking factors.