By Kimberly Hirsh

In the last blog post, I described our technical workflow for collaborative coding in the EITM Lab. In this blog post, I will explain our analytical process. It works as follows:

1. For the first three transcripts, we undertook line-by-line coding, assigning a label to each line of code.
2. At the end of line-by-line coding, we had hundreds of individual codes, but many overlapped in meaning as each lab member created her own verbiage.
3. Laura and Kimberly then coded the codes, creating possible categories to use in the coding of future transcripts.
4. Dr. Melo reviewed the categories Laura and Kimberly had generated and built a codebook based on these categories and her own coding experience, providing definitions and examples for each code.
5. All three lab members then applied the categories Dr. Melo included in the codebook in later rounds of coding.
6. As we engaged in later rounds of coding, we continued to identify new codes and categories as we noticed them recurring in the data.

Do you have any questions about our data analysis process? Feel free to get in touch.

By Dr. Marijel (Maggie) Melo

June 2020-January 2021

The EiTM Lab is gearing up for a new semester, so I (Dr. Melo) wanted to take a moment to share a project update on Phase 1 of our research program. We’ve completed our study design, interviews, and coding – and began the early stages of analysis (although I will say data collection and analysis are co-acted throughout the entire process). We’ve gathered rich, interesting, and surprising data that inform how we will respond to our research question, “What are the defining features of a makerspace?” We designed this study with the intent to accomplish two major tasks. First, we will identify 3D models, assets, and avatars for the VR environment (Phase 2). Second, we will develop a theory of makerspaces. We’re confident in the data we’ve collected and are fascinated by some of the early findings we’ve discovered.

Early Findings

We asked participants to imagine walking into a room they soon realize is a makerspace. How do they know they’re in a makerspace? What do they see, smell, and/or hear?

Here are a few early responses:

• Sight: 3D printers and tools, people working/making, blurring of staff/user, and projects
• Smell: Burning wood, “waffles,” burning plastic, and disinfectant
  • Honorable mentions: “generic library smell,” Bojangles, and “project du jour”
• Sound: Conversation, tools/machines, music

While the identification of makerspace features was fairly consistent, we were surprised to encounter some resistance to define makerspaces more holistically. We identified a pattern in the way that several participants responded: many began their descriptive musings with affective (not physical) descriptions of the space. Descriptors such as “good vibrations,” “bright,” and “inviting” showed up frequently in responses. We also felt a palpable sense of tension emerging during the interviews and subsequently in the coding process. Asking participants to define a makerspace seemed to be difficult, revealing, and anxiety-provoking. I have a lot more to say regarding this tension, but will leave this brief update with an invitation to view my recent presentation at the Coalition for Networked Information. I outline and contextualize our early findings in this video.

I’m eager to see where the data takes us this semester as we begin transitioning to Phase 2 of EiTM’s research program. Our sincerest thanks to the information professionals we’ve interviewed for Phase 1.