Final Analysis

During my class this semester, I feel like I have gained an understanding of and appreciation for Educational Design Research (EDR). while I still find it similar to action research, it is clearly much more involved and rigorous. As I preview educational programs in my role as site administrator, I will be keeping EDR in mind, and looking for evidence that the program I’m reviewing has gone through an iterative process that used a variety of data to inform the final product.

I’m unlikely to use EDR for my dissertation. As an administrator in a school district, I’m in an awkward power position with practitioners. In addition to the time commitment that I think EDR takes to “do it right”, there is an element of embedded access that I find problematic when looking at my personal goals for a dissertation completion schedule. Conducting multiple iterations during a single school year requires one to be very closely linked to the research situation, and it’s both impractical and unethical to conduct this sort of research at my school site with teachers that I evaluate! While I have an appreciation for EDR/DBR as a research methodology, I don’t see it as being a practical choice for my dissertation.

 I believe that peer review is a very powerful tool. As a recipient of peer feedback, I tend to quickly scan for things I agree with or recognize as easy corrections. I then go back and think through the revisions suggested by reviewers, and either keep them if I think they require more thought, or delete them if I feel like the suggestion is misguided or answered elsewhere. For the most part, I find that the comments are thoughtful and fairly accurate, and I very much appreciate having another set of eyes on my work. When I provide peer feedback, first and foremost I enjoy reading what other students are learning. I am picky about whose work to review, looking for those that match my background or work situation, at least in some way. There have been few peer review activities that haven’t taught me something of value, often outside of the topic of the class! When adding comments, I think carefully about my choice of words, since I know and respect the others in my cohort and don’t want to hurt anyone’s feelings. But I’m honest as well, again because I respect my colleagues and want my feedback to be meaningful. I also tend to double check my technical suggestions - I’m more likely to verify in the APA style manual when correcting someone else than when I’m doing my own writing!

Peer review does require a bit of trust, and a bit of knowing each others' style. In a class that contains a majority of students who have been together for 3 years, mixed with 2 newcomers who do not have the same history and are not at the same point in their educational career, there were some challenges. I suspect all of us tried to be inclusive, but it was more difficult to relate in some ways. It's a lesson I will keep in mind with my own students.

It's Not "Whether" It Works, But "How"

As my understanding of design-based research (DBR) grows, so does my appreciation of how well it fits my philosophy of teaching and learning. The readings in this module helped my understand that "design" in DBR is used in 2 different ways - the researcher creates (designs) an intervention or learning phenomenon, and then collects data that allows them to create a model (design) or guidelines (design principles) that can be used to generalize the intervention into other environments. I think this is how good teaching works, when the teacher has sufficient time and ability to collect the data needed. It s a more rigorous version of piloting an intervention or program, and then making changes to the program based on what actually happens in the classroom. This is how I've designed model lessons in the past, and it's how my district has developed units of study that are disseminated across schools.

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Data collection is the challenge in any type of research. Dr. William Sandoval notes that there is a tendency among researchers, particularly novice ones, to collect everything possible and then try to figure out what is needed later. Since there is as much a need for thick description in DBR as there is in case study research, this can end up being a huge amount of data! Sandoval says that, instead, a researcher should have a clear plan of data collection, and should have a reason for collecting every piece of data that is collected. While that makes perfect sense, I think it probably takes a fair amount of experience to know what data will be relevant and which will be extraneous. My fear, as I'm sure is true for most novice researchers, is that I will begin writing my results and realize there's a gap in my data! I'm not sure how one overcomes the challenge of too much data, though I suspect that working in close communication with experienced researchers who can make recommendations.