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Sunday, September 23, 2012

Coursera Weekly Reflection 23.09.2012

Coursera I am posting weekly reflections here on my current Coursera MOOCs as they progress.

It's been an interesting week on the Coursera Statistics One MOOC - sequential multiple regression and matrix algebra. Statistics *One* - seriously? Confusion is arising in lack of linkage between video lectures and assessments - both are good on their own but the signposting linking them is missing. The organization on Coursera Stats One is laughable and I no longer care about the assessments, but I'm continuing to participate as I'm meeting the learning outcomes I set for myself for this MOOC. Self-motivated learning is clearly important in this MOOC game. Am I still learning? Yes, not so much from the lectures now but by noodling around with R and picking things up (e.g. the par() function to layout multiple graphs, quartz() to open multiple graphics windows, etc), and I've been deeper into multiple regression than I've ever been before. I also had a light bulb moment about how I'd like to teach level 2 statistics - via annotated R scripts where the annotations allow me inside student's heads to view their thought processes. That's not going to happen with class sizes over 100 through.


Coursera Social Network Analysis and Writing for the Sciences both start next week, and term starts the week after. I hope they're better organised than Stats One, but at least I'll get a broader view of Coursera. I need to set my personal learning objectives for those courses. I'm away three days this week. Clearly, I'm not going to be able to participate in all of these courses, but once I've had a peek inside, I'll decide how to proceed.





1 comment:

  1. You're certainly right that the organisation of this course is not slick and I'd agree that Statistics 'One' is a complete misnomer (judging by the course forums, many would agree with that).

    But overall I think the course is excellent, and exceptionally good for something that is free. I've looked for good statistics resources and videos on the web and haven't found anything that could hold a candle to this. I think the lectures are well-explained and, apart from a few bugs, the assignements fit the lectures well. It seems like we are on different courses!

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