Hi Isaac,

Is your course actually "quantitative methods" or STATS? I ask because I teach a data analysis course using TBL that I had to work hard to make sure was actually an ANALYSIS class and not just STATS. In my mind, the difference is that in a stats class, students may have to use data but the problems they work on are more about doing calculations while in an analysis class, the focus is more on the decisions that analysts must make before they actually get to the statistics (such as WHICH statistical tool to use, which variables to include in the model, etc.). I think the latter lends itself wonderfully to TBL because in a lot of situations, there are multiple valid choices that the analyst might make, though different choices may lead to different outcomes, and the team discussions really highlight those issues. But I have found that statistical calculations don't make very good team applications because, as you note, there may be some students who are better at crunching the numbers and everyone else just relies on them. What I often do is have students do the number crunching on their own, before class (using a quiz in Blackboard to ensure they do it), and then the team applications are built around interpretation and analysis. For example, I send them to a website to download a dataset and ask them to calculate the correlation between two variables BEFORE class. Then the team discusses what that correlation coefficient actually MEANS. At an even simpler level, they might calculate the mean and median of a variable before class and then the team has to decide which one is the more appropriate for a particular application.

My course is for undergrad econ majors but I'd be happy to send you some examples of my applications if it would help.

Jennifer


On Thu, Jan 16, 2014 at 5:36 AM, Van Patten, Isaac T <[log in to unmask]> wrote:

I am teaching a course in quantitative methods to first year graduate students and introduced team-based learning to this course for the first time last spring.  For the most part it went well with one exception, Application Exercises.

 

Here is my dilemma.  The subject matter certainly provides ample material for application exercises.  Working with real-world data, students conduct real-world analyses.  The problem has to do with the team-based aspect of the activity.  The applications readily lend themselves to individual effort.  However, figuring out how to best meet the single answer, simultaneous reporting criteria is difficult.

 

What I observed was usually the one student on the team who had best statistical knowledge would sit at their workstation and do all the work.  The rest of the team watched over their shoulder.  Obviously this much social loafing is counter-productive.  As a team, they were more focused on "getting the right answer" than on learning the statistical concepts behind the analyses.  I encouraged everyone to work the problem and to help one another out.  Still, the students would quickly gravitate to the one workstation where the student already "got it."

 

My question is how to use team-based applications in this situation to greater effect.  I am striving for that mystical "cognitive

 

__________________________________________

Isaac T. Van Patten, Ph.D.

Professor of Criminal Justice

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It doesn't matter how beautiful your theory is;

It doesn't matter how smart you are;

If it doesn't agree with the [data], its wrong.

-Richard Feynman