Yes, this is a demanding course for most people. My strong sense is that it, like other statistics courses, should only be taught in the 15-week format. I say that knowing the strong preference among adult students for 8-week or shorter courses they can more quickly check as “Completed” on their degree To Do list.
We need to remember that regionally-accredited degree programs require courses to satisfy the Carnegie credit system in which a credit-hour represents the equivalent of 3 student work hours per week for 15 weeks. (Silve & White, 2015) Thus, this 3-credit-hour course must require 9 student work hours per week in the 15-week format, which equates to about 17 hours per week in the 8-week format.
Again, my strong sense is that most adult students rationalize “they” can get the work done in less time either consciously or subconsciously. And that can lead to stress when the inevitable work/life issues occur which disrupt our plans. I believe that this type of added stress does not help people learn.
A second reason I believe this quant course should be taught in only 15-week terms is that stats is a subject in which time is needed to process and to really learn the concepts. There are two aspects of this:
First, most of us need time to reflect on what we have read and perhaps go back and re-read the material or read supporting material [you can also apply this concept to material you have watched.] I’m guessing that all of us have had instances of where we leave a discussion/argument with less than satisfactory results only to have the “perfect” response pop into our minds later after we mull over the discussion. Similarly, I have no doubt that we all have had the experience of coming up with a solution to a problem after we “sleep” on it. That same thing happens to me a lot when I ponder how to solve a complex stats problem.
There is an analogy in sports/exercise. Recall the “burn” in muscles we all experience when we begin to learn a new sport/exercise which uses muscles differently then we are used to using them. We are told to space our exercise to allow our muscles time to recover. (Bishop & Woods, 2008) We are well advised to space our exercise at least 48 hours including a good night’s sleep if at all possible. Same for studying stats, in my opinion. (Kapur, 2014)
Second, there is good research that shows better results in math-like courses occur when students use spaced-repetition, which is nothing more than having time between their work sessions on topics. (Cepeda, Pashler, Vul, Wixted, & Rohrer, 2006) That is one reason I always recommend my students space their work over the course of the week, beginning early in the week, and not delay “everything” for a crashed pace on the weekend.
Finally, there is the fact that this is an online course which limits the student-student and student-instructor interactions which I believe are important in most difficult topics. I took a few online courses during my doctorate, but they were not really parallel to this course because I could still see and talk to my classmates and instructor at school the following days to rehash what went on in the online course. I have tried holding Google Hangouts in my online courses but find that only a small portion of the class can participate each time I try to hold them. And some of my students complained that the Hangouts were unfair to them because their work/life did not allow them to attend regardless of when I scheduled the Hangouts. Viewing the video of the hangout did not satisfy their need for interaction the way actual attendance would. But my sense is that if all my courses were 15-week terms, it is more likely that every student would be able to attend some of the weekly/twice-weekly hangouts. And that would be materially beneficial.
My opinion based on my observations (admittedly anecdotal evidence) in teaching stats for seven years is that adult students with all their family and job responsibilities do better (learn more with less stress) in 15-week terms. Period.
Bishop, P., & Woods, A. (2008). Recovery from training: a brief review. Journal of Strength and Conditioning Research, 22(3):1015-1024.
Cepeda, N., Pashler, H., Vul, E., Wixted, J., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. . Psychological Bulletin, 132:354-380. Retrieved from UCLA College of Life Sciences.
Kapur, M. (2014). Productive failure in learning math. Cognitive Science, 38(5): 1008-1022.
Silve, E., & White, T. :. (2015). The Carnegie Unit: A Century-Old Standard in a Changing Education Landscape. Standford: Carnegie Foundation for the Advancement of Teaching.