From the Statistics MELO group–now in 3D

This summer the Statistics MELO3D team has zeroed in on a handful of specific goals.  Primary among these goals has been developing, editing, and discussing–let’s face it, we all have different ideas and can learn from one another—content  for our Name That Scenario (NTS) learning object (LO).   We’ve also updated another custom built LO—an R script that allows students to create p-value illustrations—and worked to better integrate it into the course.   Finally, though a number of LOs have previously been integrated into the course we’ve continued to seek out additional LOs to provide students with an additional resource as they synthesize what they learn.

                Indeed, synthesizing knowledge is key objective of NTS.   Stats 250 is an introduction to statistical inference and students learn a number of inference procedures over the course of a semester.  While an effort is made in lecture and lab to draw attention to the distinguishing characteristics of each inference procedure as well as links between them, NTS allows students to put that knowledge into action.   In a nutshell, NTS allows students to read a “scenario”–a contextualized research question and some information regarding study design—and then identify the appropriate inference procedure for the situation.  NTS not only lets users know if they’ve answered correctly, but provides feedback as to why a particular inference procedure is appropriate.  Moreover, students can choose any subset of the 10 inference procedures taught in the course to work with at a given time making NTS a flexible tool for study and review. 

                Creating a learning object from scratch along with content—both scenarios and feedback—for 10 different procedures is not a trivial task.  Luckily the work has been spread out.  Scott Hamm gets credit for creating the flash object including several revisions.   Josh Errickson gets credit for creating the bulk of the feedback and a good deal of content, formatting and editing the content files, and keeping all this content organized.  I’ll take credit as Josh’s sidekick on the content creation and editing fronts (though his work dwarfs mine) and Joel Vaughn deserves credit in this regard as well (though he’s nobody’s sidekick).   Of course, none of this would be possible without the leadership, organization, encouragement, and editing skills of Dr. Brenda “Joyful” Gunderson.   Shout out to Dave Childers as well who I hear thought this whole thing up some years back.  I hope I haven’t left anyone out or understated their contribution!

                We’ve also updated script for visualizing p-values and found several ways to further integrate it into the course.  (Credit goes to Josh for reworking the script and to Tom Brown for the original.)  The script allows students to see the key aspects of p-value visualization and helps to reinforce one of the key ideas on the testing side of inference.    Josh has also created a video wrapper to introduce the LO and we are in the process of developing a pre-lab assignment around it.    It has also been incorporated directly into the lab modules as a way for students to check their work after drafting p-value sketches.

Let’s leave it at that for today. 







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