Media and digital literacies in Canadian teacher educators’ open educational practices: A post-intentional phenomenology

The Gathering

Vagle (2021) suggested P-IP researchers gather materials rather than data. This distinction is important in order to semantically separate research endeavors from qualitative, positivist perspectives about what researchers collect. Thus, I applied the term data gathering for this PhD research work. Vagle (2018) suggested that phenomena determined “how it is to be studied” (p. 75) and described multiple data gathering moments including observations, writings, interviews, drawings, and music collected over a specified period of time. In fitting with the research topic, this method honours the ethos of openness of OEPr while examining the multiplicity of textual information and an openness in data gathering. Vagle (2018) suggested data moments could include arts-based methods such as drawings, paintings, photos, visuals, films, and performance art. Knowing that these are potential options did not mean that I would use all of these formats in my research.

          I planned the interview protocol (see Appendix D) to be fluid and flexible since unstructured interviews are the most common interview type in phenomenology due to their open dialogic nature (Kennedy, 2016; Vagle, 2018). Data gathering began with web searches of FoE sites for participant related information. The digital information originating from daily digital interactions by participants in online platforms are “extremely insightful to understand what digital actors ‘do’, rather than who they ‘are’” (Caliandro & Gandini, 2017). I searched for open sources of information, such as participants’ social media locations including blog sites, Twitter, Instagram, and course websites wherever these were posted openly on the internet. This information supported the focus and topics that I wove into the interview, often as a conversation prompt or question. While information garnered from multiple web sources may reveal MDL in action, these data gatherings provided insights into the lived experiences, intentionalities, and digital identities of the participants.

          For my own processes in this research, I used a variety of digital technologies to manage and generate data gatherings. First, digital data analysis was done using NVivo on an Apple laptop computer. I used Zoom video conferencing software to capture the interviews. The web-based audio transcription software Otter.ai generated drafts of the interview transcripts in a timely manner, sometimes allowing me to return transcripts within 48 hours to the participants. The web-based word cloud creation software WordArt was used to re-create the transcripts into graphic renderings. This was selected from the abundance of word cloud generators since I already had a free account with this service. This software allowed me to download a PNG image and provide a web-access link to the interactive word cloud image (see below or see Figure 16). I used the web-based open access software Draw.io for concept mapping since it integrated into an existing Google account. I used the graphic visualization software ProCreate on an iPad to generate sketchnotes of concepts and research findings.

          Throughout the process of gathering these data materials, I created observational notes and began to establish preliminary connections to MDL frameworks. Even in the early stages of the research I engaged with data making (Ellingson & Sotirin, 2020) as well as creating dynamic representations for each participant. I created and revisited conceptual maps of connections, locations, and literacies using Draw.io software in order to “animate new ways of thinking and relating by affirming heretofore unimagined configurations” (Ellingson & Sotirin, 2020, p. 11).

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