I woke up excited about coding. What does this say about me? I’m a happy doc student nerd? So be it. Now that I’ve read the Saldana coding chapter excerpt, I feel much more confident about coding my own data. I’ve recorded 4 post conferences thus far, and will transcribe those in order to examine them in detail, using the information I’ve learned in this reading as well as the Dana and Yendol-Hoppey book, The Reflective Educator’s Guide to Classroom Research (2014). Three of the conferences were audio recordings, and one a video. They were between 20 and 34 minutes each, so I will have a lot of data to review in these instances. I will also be adding my post conference notes, FEAPS observation tools, student feedback emails, comments after each conference, and a formal online feedback survey.
The coding article provided a wealth of information regarding different ways to code, what to prioritize, how one can analyze data, and how to go about organizing everything coherently. Coding for patterns, coding filters, categorizing, recoding, codes and themes, what gets coded, the mechanics of coding, coding collaboratively or solo, coding methods, and necessary personal attributes for coding are all explained. I found the section on coding and re-coding interesting, pondering how deep one could go into the process, breaking down the information, categorizing, recoding, finding themes, and coding again. I could see how an inquirer could swim in their work, living, eating, breathing and dreaming it, waking up excited about coding! I’m curious to see where my research examination takes me, and what surprises are in store. It’s an academic adventure. The most helpful portions were the examples provided after each explanation. This gave me a concrete sample to set as a framework for my own work. I found them very useful. In considering how I would like to begin coding my work, I was drawn to the idea of six pedagogical skills as outlined by Burns & Bendiali (2013, 2014), which I blogged about last week. These “pedagogical skills that contribute to a clinical pedagogy of supervision” are: noticing (which includes marking), ignoring, intervening, pointing, unpacking, and processing-for-action. Having read this article recently, I was in this mindset, and as I pondered how to best categorize and code my work, these sprang to mind as the perfect way to do so. I anticipate that my work will fall naturally into these categories, and this will help me organize and understand the research, finding themes, commonalities, and even differences. I’d also like to code in such a way as to understand who did the noticing, supervisor or intern, as well as who does the pointing, unpacking, and processing for action. I may organize this as simultaneous coding, but will need to ponder it further to decide it this is the correct approach. Frequency coding will play a role in this portion as well. One of the things mentioned here that I wish I had done was to code some as I create and record the data. This would have made it easier I think, but it’s a lesson I will carry into my next inquiry. Studying the Dana and Yendol-Hoppey text was insightful. I had read this book as an instructor teaching Level I interns last semester. It definitely did not hold the same depth of meaning as it does for me now re-reading it as a student doing my own inquiry. The text is easy to read and practical, taking into account the audience that it’s written for – busy teachers. I found the outline of how to proceed through each phase very helpful, as well as the chapter exercises with focused questions. Not only are these questions I can ask myself, but questions that I can use with my interns who are working hard on their own inquiries. The sample graphs in chapter 6 were also helpful, and something I will incorporate into my presentation at the inquiry conference. I’ve certainly used graphs before when teaching science and math, as well as in examining my own performance as an evaluator in relation to other peers. But this was a great reminder that this type of comparative graph can be used to visually represent data for the purposes of my inquiry. This assuages my need to include “scientific before and after” data when presenting my inquiry. This leads me to chapter 8 in the Yendol-Hoppey book, the topic of inquiry versus action research or teacher research. I was reassured. This was a misconception I held as well, that inquiry needs to be very data heavy, with “extensive number crunching and statistical analyses, white lab coats, experimental designs with a control and treatment group, and long hours in the library” (pg. 215) The experts say this is not true! I agree (although the final claim is questionable). Inquiry is about self-examination and how ultimately this affects students. My inquiry is based only on me, although the information may be transferable to others, if my work is of good quality. This is a part of my growth process, focusing and fostering it. Inquiry is a tool I can continue to use, and share with others. References: Burns, R. & Badailai, (under review). Unearthing the complexities of clinical pedagogy in supervision. Action in Teacher Education. Dana, N. F. & Yendol-Hoppey, D. (2014). The reflective educator's guide to classroom research: Learning to teach and teaching to learn through practitioner inquiry (3rd ed.). Thousand Oaks, Calif.: Corwin Press. Saldana, J. (2012). The Coding Manual for Qualitative Researchers (2nd ed.). SAGE Publications Ltd.
1 Comment
3/25/2015 11:29:31 pm
There's nothing wrong with being an academic nerd - welcome to the club!!!! :) It sounds like you have lots of data to use - that's good. If you can, start following Dana & Yendol-Hoppey's (2014) advice - start analyzing now throughout the process so it doesn't feel so overwhelming!
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