In Mishra, Koehler & Greenhow (2015), they discuss the work that has emerged from designing “big studies with big data.” In my current research lab with Dr. Lisa Linnenbrink-Garcia, we collect longitudinal data on two distinct projects, and one of the ways in which this has been made possible is through Qualtrics software. For my own work, I am interested in using these data sets to ask questions related to motivation for students from underrepresented groups in science. Mishra, Koehler & Greenhow (2015) address an important issue related to my current work which is that our method of data collection may not account for the complexities in student development. They state “designed studies can oversimplify complex human actions and motivations, magnify data errors when multiple datasets are combined, and create divides between those who have access to big data and those who do not” (p. 32).
I have been countering this issue by thinking of a mixed methods study that allows for qualitative data to speak to the complexities of students’ science pathways. As part of the Linnenbrink-Garcia lab this semester, I have been collecting interview data, which we are now in the process of transcribing and coding. Mishra, Koehler & Greenhow (2015) discuss how programs, such as ATLAS, have made it convenient to organize and code qualitative data. Overall, I think our ability to collect, store, organize, and analyize data has become easier within Educational Psychology due to technological advances in the twenty-first century.
Hi Krystal, I liked your reasoning for considering a mixed methods approach. I am similarly thinking about the types of data I want to collect and what will give me the most meaningful insights. What type of qualitative-quantitative combination are you considering? Will you be using the quantitative data to inform your qualitative work, vice versa, or some other combination?
As you noted, technology allows for many creative data collection options, even beyond big data. For example, not only can we trace the analytics of social media use, but we can video capture students’ movements through various platforms too. I’m also curious about walk-through techniques that ask participants to click through various pages and verbalize their thoughts and reflections. Given my interests, this would be an interesting perspective behind students’ choices online.
Hi Krystal, I resonate with your concerns about how technological developments in data collection can serve to both enrich and constrain the way we conduct studies and analyze findings. With the advent of “big data”, it seems to be easier to conduct large-scale quantitative measures, and to condense participants into a data point. It’s tempting to be able to reduce students to a set of data points, but on the flip side, we can use the advances in data-collection to build richer, more complex profiles of students. The integration of design-based research and mixed methods is a major step in this direction.
Personally, I am interested in exploring the use of VR technology to conduct think-aloud-protocols and task analyses that will immerse participants in authentic activities and experiences while capturing audio and movement. Overall, I am quite optimistic about the contributions of technology towards educational research– I think it will enable the collection of varied, richer sources of data– especially in regards to qualitative data and studies conducted in naturalistic settings.
Hi, Krystal. Your research sounds fascinating. Increasing diversity in science fields is an important goal.
You write about new ways that researchers are collecting and analyzing data using digital tools. Since the prompt this week asked about shifts, if you highlight the changes a bit more, by saying a few words about how these methods contrast to more traditional approaches to data collection and analysis.
I agree that using mixed methods can produce studies with powerful insights. However, I believe that analyzing interview data with Atlas doesn’t qualify a study quantitative or mixed. Even though programs like Atlas produce some numbers, I believe that research done with Atlas is still considered qualitative. In fact, Atlas’s promotional tagline is, “The Qualitative Data Analysis and Research Software.” In my understanding, quantitative research is about large sample sizes and statistics.
I look forward to hearing you talk about your research at a future brownbag!
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