This was the first semester where ALL of our Applied Methods sections used R instead of proprietary software, so we took this opportunity to make some small, but substantial changes to our teaching methods.
For the first time:
- We introduced R on the first day of the semester EVEN though we don't get into data analysis until much further along. Why did we do this? We thought that introducing students to R early would build confidence as we taught skills incrementally.
- We gave brief and simple homework assignments after most classes. This is something we had been hesitant to do in the past, as not all students had access to statistical software off-campus (i.e., we never required students to purchase expensive propriety software previously). Since R and RStudio are free, safe downloads, everyone could access it from their home computers. Some authors have suggested that requiring students to do independent research work outside of the classroom is related to more positive attitudes towards research and increased abilities (see Elliott, Choi, & Friedline, 2013).
- We had an open lab at least once a week where students could join us and ask questions about R or their homework assignments. This gave students the opportunity to get a little one-on-one attention in areas where they thought they might need some individual help.
- The students completed a semester-long research class evaluating factors related to satisfaction with social work education. They got involved in all aspects of the project from identifying research questions, creating hypotheses, constructing a survey, collecting data, to analyzing data and reporting findings.
By the way, if you are reading this blog and you were one of our students this summer, we welcome comments and constructive criticism, particularly on how R worked for you!
If you find this interesting, we invite you to join us at The Council on Social Work Education Annual Program Meeting in Tampa. We are presenting a skills workshop on Saturday, October 25th at 11 am. The title of the workshop is Using R to Build Research Skills and Research Capacity in Master's-Level Students. As we get closer to APM, we will share with you a bit of what we will be presenting, but, as usual, we will be able to provide you with much more content and many more resources than makes sense in a blog.
Feel free to check out our website at www.ssdanalysis.com and, as always, we welcome your comments!
The hands-on research component was definitely a highlight of the class!
ReplyDeleteI was one of the students in this class and found the way that R was used seemed most sensible. As this was a class where data sets were to be expressed as statistics that grew in complexity week to week, learning how to use R to interpret the data concurrently while learning how to use manual methods to interpret the data solidified the learning process for both. Often times, technology is introduced late in the game after expertise has already been achieved in manual methods. While some students and professors may prefer that manual methods are learned beforehand, I'm of the camp that subscribes to concurrent learning. For me personally, learning R after learning manual methods would have made me question whether I was doing things right because of the time between the two. I could see myself LOSING confidence rather than gaining confidence in my abilities this way. However, learning R concurrently helped me grasp mathematical ideas more efficiently, as I had no time to wonder if I was doing things right, rather having R be a check for my manual methods and my manual methods be a check for R.
ReplyDelete- Nafees