New NSF Initiative: Graduating 10,000 New Engineers and Computer Scientists

Interesting new initiative between the White House and NSF to increase the number of graduates in computing and engineering by focusing on retention. (I strongly agree, because retention is where we’ve been focusing our attention.)

This letter announces a cooperative activity between NSF and members of the Jobs Councils High Tech Education working group, led by Intel and GE, to stimulate comprehensive action at universities and colleges to help increase the annual number of new B.S. graduates in engineering and computer science by 10,000. Proposals for support of projects would be submitted under a special funding focus Graduate 10K+ within the NSF Science, Technology, Engineering, and Mathematics Talent Expansion Program STEP, see http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=5488.

Studies have shown that retention during the critical first two years in a students major, or along the path towards declaration of a major, is an excellent predictor of eventual graduation with a STEM degree. Recognizing that the correlation between retention and graduation is particularly strong for students in engineering and computer science, we invite proposals from institutions that can demonstrate their commitment to:(i) significant improvement in first and second year retention rates in these particular majors, beyond current levels; and (ii) sustained, institutionally-embraced practices e.g. http://www.asee.org/retention-project that lead, ultimately, to increased graduation. Jobs Council members anticipate providing support for this special funding focus, with the number of awards to be made contingent on the availability of funds.

via US NSF – Dear Colleague Letter – Graduating 10,000 New Engineers and Computer Scientists – A Partnership between the Presidents Jobs Council and NSF Graduate 10K+ NSF12108.

Outrage over Udacity Statistics 101: But is it really worse than others?

AngryMath’s blog post on Udacity Statistics 101 (linked below) is detailed, compelling, and damning.  It’s certainly not the best statistics course anywhere.  But I have to wonder: Is it worse than average?  It’s hard to teach statistics well (I really did try this last summer).  It’s hard to teach anything well, and there’s evidence that we need to improve our teaching in computer science.  This doesn’t feel like an indictment of MOOC courses overall.

In brief, here is my overall assessment: 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. In surveying the course, some nights I personally got seriously depressed at the notion that this might be standard fare for the college lectures encountered by most students during their academic careers.

via AngryMath: Udacity Statistics 101.