This past Wednesday I taught my second class in statistics. The focus was to explain types of data and examine visually appropriate ways to represent data. This took the form of making an appropriate APA table as well as constructing several graphs. I think that most of the students recognized the actual attributes of the representations, and some walked away with an understanding of the differences that they might not have known before.

Although I was not surprised, what was most striking to me was how the differences in abilities, mainly related to computer expertise (not content understanding/knowledge). The students are very open, unassuming, and metacognitive when it comes to their level of experience using technology. I value that, because I don’t have to guess what’s happening. They just let me know. Therefore, I can adjust to the needs of the students. Also wonderful, is watching those with more experience and expertise lean over and provide the support and help that some need themselves.

This has all of the feels of differentiation. However, the interesting part is that all students have to end in the same place, in the same 15 weeks of the instructional experience. I guess what makes me fret most is that we meet for a very limited time: two hours per week, only once a week. That’s not a great deal of contact time to meet the individual needs of each student, especially in a fairly large class.

I consider the initial topics of the first two weeks fairly easy.  When the content becomes more demanding, I think my challenges will increase.  It is important for me to continue with my inquiry-oriented constructivist approach (I really hope I’m doing this . . . ) to prepare these students for authentic quantitative studies.

I taught my first graduate class in statistics today. As I said to my students, I think I was far more nervous than they were. I found that I was sweating – probably a combination of nerves and movement while teaching the class. But, nonetheless, I wanted to share my personal philosophy of the discipline. As I have stated before, I view statistics as a tool to help us understand the things that MATTER TO US. Certainly there are those that study statistics for its pure value, however I don’t see it that way. If statistics are to be practical, then there must be value for the user.

I’ve encouraged my students to collect data that is meaningful to them. If they can learn something about their teaching and, in the process, learn something about a sophisticated method to evaluate its effectives, I think we bring such greater meaning. This doesn’t stray far from my dissertation where I purport that learning takes place in a situated environment, where constituents become members of the community of practice (see Brill). Work needs to be authentic, not sort-of-maybe-on-Tuesday-authentic. I cringe every time I hear the saying, “like real life,” in some instructional setting. Why can’t it just be real life?

I think this is an incredibly hard concept for educators to grasp. I gave an assignment for the teachers to collect some data that had meaning to them for further evaluation in our class. I was not completely convinced that everyone bought into the value of this. Some students certainly won’t have access to good or usable information for the assignment, but I think some that might will complete the assignment with arbitrary or fictitious data. This is totally fine and acceptable within the context of the learning. However, I guess my own personal biases in education really want me to have students completing real work. Ultimately what’s most important is that students learn well. That can happen with or without authenticity. As the saying goes, I shouldn’t impose my values on others.

Now for a bad transition. The purpose of the class was to talk about p. p being probability. I wanted students to have a very firm grasp of what p was, since it is the real foundation to statistics. When they see the p, or the Sig. notations, I want them to quickly think about how sensitive that value is compared to other decisions we make that have far higher risk (i.e., a higher p value). We played Black Jack to illustrate this. Students were interested in taking a card with a p value of ~.50 – the 50/50 odds. Yet in education, we wouldn’t dare say anything was statistically significant (make a choice to take a card) unless we were at least 95% sure that it was different (p<.05).

I hope I effectively communicated my big idea of p. So much can make sense with just a simple, yet clear understanding of p values.

Tomorrow, I will begin to teach a doctoral course in statistics. I am excited about the prospect of working with highly motivated, engaged teachers. What concerns me most is the “fright factor” that some have. To me, statistics has always been a tool to help explain whether or not a phenomenon, a teaching strategy, or perhaps student learning changes due to influencing factors.

That said, I think of statistics as an applied tool. Statistics don’t exist in isolation. They exist IN CONTEXT. The challenge for me will be to translate some technical mumbo-jumbo into meaningful concepts that these teachers can realistically apply to their research or even to their understandings of their own students.

When I learned statistics, I was working in a bacterial genetics laboratory examining DNA restriction fragments of human chromosome 6, locus HLA-C (major histocompatibility complex of the immune system). I needed to determine the significance of different fragment lengths to see if our human genome project mapping strategy was working. Now, realistically, I know that most won’t even understand the nature of this genetics project, and that doesn’t matter. The fact was, that I needed to use statistics to explain what I had done experimentally. My data was IN CONTEXT for me. That being said, statistics for educators need to be in context for them. It’s not just good enough to examine fictitious educational data, we also need to consider meaningful data to the student-constituents.

My colleague and good friend, Krista Ritchie sent me some interesting literature recently and one
paper caught my eye:

Singer, J. D., & Willett, J. B. (1990). Improving the teaching of applied statistics: Putting the data back into data analysis. The American Statistician, 44(3), 223-230. (Argues against artificial data sets for learning statistics – that false data makes statistics seem dry and dull. Argues for learning statistics through authentic research).

I had the opportunity to present to this group of students earlier this summer, talking about 21st-century skills and their application in the classroom. I guess now it’s my turn to put up and show that authentic teaching and learning can be a reality. I will attempt to use this blog as a point of reflexivity for both me and my students, I’ve established a collaborative wiki for us to share ideas, and I will attempt to get an online survey working as well. I thought this would be done before writing, but alas, I am having technical difficulties with the limesurvey freeware. If I can’t get it to work by tomorrow’s class, I will resort to paper (maybe bubblesheets from Apperson?). I think the online survey will be valuable to my students, not only as an expository activity, but as a potential tool for their dissertations.

Challenges await for both the students and me. As Marcy says, “I take no prisoners.”