# Livebinders: 20-day blogging challenge, day two

Filed Under 20 Day Blogging, Software, statistics

Today I’m on day two of the 20-day blogging challenge, the brain child of Kelly Hines and a great way to find new, interesting bloggers. The second day prompt was to share an organizational tip from your classroom, one thing that works for you.

The latest tool I’ve been using is livebinders . Remember when you were in college having a binder full of notes, handouts from the professor, maybe even copies of tests to study for the final? Well, livebinders appears to be designed more for clipping websites and including media from the web but personally I am using it to create binders for teaching statistics. I’ve just started with one but I’m sure this will eventually split off into several binders.

I’m always writing notes to myself but I have them everywhere – I used Google notebook until they got rid of that, evernote, I’ve got notepads on my laptop, desktop, iPad, phone and even paper notebooks around the place. I even have a PadsX program The Invisible Developer wrote years ago just for me (yes, he loves me).

Still, I’m thinking livebinders is going to be really useful for me to organize all of these notes into one spot.

Why do I want to do that, you might ask?

Well, statistics is a big field, and I have taught a lot of it, from advanced multivariate statistics to psychometrics to biostatistics and a lot of special topics courses. It seems to me that we often assume students have a solid grasp of certain concepts, such as variance or standardization, when I’m sure many of them do not. As I read books and articles, I’m trying to note what these assumptions are. My next step is to have pages in the binders where students can get greater explanation of, say, what does a confidence interval really mean. Right now, I feel that universities are trying to cut costs by combining information into fewer and fewer courses. We say that students learned Analysis of Variance in a course, but did they really? The basic statistics I took in graduate school consisted of a descriptive statistics class (I tested out of that). It ended with a brief introduction to hypothesis testing and a discussion of t-tests, z-scores, t-tests and correlation. The inferential statistics course reviewed hypothesis testing, t-tests and correlation, then focused on regression and ANOVA. The multivariate statistics course covered techniques like cluster analysis, canonical correlation and discriminant function analysis. Psychometric statistics covered factor analysis and various types of reliability and validity. These four courses were the BASICS, what everyone in graduate school took. (People like me who specialized in applied statistics took a bunch more classes on top of that.) Oh, yes, and each class came with a three-hour computer lab AFTER the three-hour lecture,  to teach you enough programming so you could do the analyses yourself. Now, many textbooks try to include all of this in one course, which is just a joke, and ends up with students concluding that they “are just not very good at math”.

I can’t change the curriculum, but what I at least can do is provide some type of resource where every time a student feels he or she needs to back up and understand some concept, there is an explanation of that something.

I plan to have this done by the time I teach Data Mining in August.

Suggestions for what to include are welcome.