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• 1 2022-06-08T21:23:24+00:00 Research Phases and Timeline 30 outlines the research phases and timeline for this research plain 2023-10-02T17:40:38+00:00 Next the phases and timeline are provided in both text and graphic formats. Although this timeline suggested a linear process, spirals and recursions occurred throughout the research process in order to revisit, review, and reflect on data gatherings and research journal notes. This is symptomatic of P-IP methodology as an iterative and rhizomatic process. This supported the assembling of data engagements (Ellingson & Sotirin, 2020) since data were generated from the lived experiences and intentionality of the participants, as revealed through actions, artifacts, technologies, and discourses within each research phase (see Figure 15).
            Phase One included the preparatory work of seeking research ethics board (REB) approval, preparing the informed consent forms, drafting the interview protocol, developing a draft interview schedule, and searching the internet for potential participants. During this phase I conducted one interview with a teacher educator outside the Canadian teacher education context who was familiar to me. As a novice researcher, this pilot interview allowed me to reflect on the interview process and prompts, and make adjustments to the interview protocol as part of the REB submission. This first phase ended once the REB approval was received (see Appendix A).
  
            Phase Two included a sequence of initial contacts over the space of five months. I aimed to schedule these at least one week apart in order to manage the data gathering and data-engagement process I had planned. Throughout this phase I maintained both an electronic spreadsheet and a research notebook form of tracking to ensure I followed a consistent sequence with each participant. An introductory email was sent to the participant (see Appendix B-1). Once the TEd agreed to participate, I conducted a web search for information that may be relevant for this research e.g. publications, course related information, and social media posts. I recorded this information in a Word doc version of my research journal, along with any notes on insights into MDL connections or thoughts for possible inclusion in the interview.
  
            After the initial agreement to participate, I sent out the informed consent information (see Appendix C) along with a video link as a way of introducing myself to the participant and providing information about the research. The interview was then scheduled for a mutually convenient time and the informed consent was collected. I also sent a copy of the interview protocol (see Appendix D), not with an expectation that participants would prepare prior to meeting, but to provide a guide to our conversation. After the first few interviews were completed, I changed the process slightly to include sending out an electronic calendar invitation which included the Zoom link so participants could see this event on their preferred calendar software.
  
            The interview was then conducted. Immediately prior to meeting the participant, I reviewed my research journal notes to ensure I was fully prepared for the conversation. At the end of the interview participants were asked to prepare a digital artifact using a technology of their choice (text, image, graphic, audio, video) that was reflective of their MDL and OEPr lived experiences. As suggested by Ellingson and Sotirin (2020), this “participatory data engagement requires exceptional openness to change, to uncertainty and ambiguity, and to attending carefully to how different forms of knowledge emerge” (p. 95).
  
            After the interview ended, the recording was saved to my laptop. The audio file provided from the Zoom recording was uploaded to Otter.ai and transcribed, usually within one hour of the upload. After downloading the transcription from Otter.ai, I reviewed the document as I listened and watched the recorded interview. This supported making any necessary edits and observational notes. In this way, I re-encountered the data within an agentic and dynamic state (Ellingson & Sotirin, 2020). Although the recordings or transcripts did not materially change (Ellingson & Sotirin, 2020), my engagement with these data shifted to a different moment in time, thus altering my views in subtle and sometimes dramatic ways. Once the transcript was reviewed, it was saved prior to conducting a process of redacting identifying information such as names or geographic references. This redacted version of the transcript was then inserted into the Word Art software. The rendered word cloud image was then downloaded as a portable graphics network (PNG) file and stored on my computer. I also created a short screen-cast video of some of the interactive word clouds which allowed me to detect words that were not noticed in the first viewing.
  
            In the post-interview email sent to each participant (see Appendix B-2), I included links to the transcript, the audio recording, and the PNG of the word cloud image for review and comments (see this curated collection of word cloud images). In this email I reminded the TEds of the second part of their participation – the creation of a digital artifact representative of their lived experiences with MDL in their OEPr. To provoke their thinking, I provided links to media and digital literacy frameworks that could be referenced for this artifact production. A soft due date was set for two weeks post-interview. I also included a digital e-card to a national bookstore chain as a way to recognize their gift of time with this project.
  
            When I examined the artifacts, I delved more deeply into the TEds lived experiences with MDL within OEPr. This was an opportunity to “focus on analysis and creative representations of participants’ experiences, with consideration of the researcher in a secondary role” (Ellingson, 2009, p. 23). The participants created artifacts in a variety of formats – infographics, a sketch-note, blog post, video recording, interactive story created using Twine, and audio recordings. These digital artifacts revealed a representation of MDL and OEPr in action as a process of becoming. This part of the second phase was a way of “leading to a co-authored understanding of the experience being discussed between the participant and the researcher” (Ranse et al., 2020, p. 6). As mentioned, a spreadsheet and research journal chart were maintained throughout this phase to confirm completion of each task, to track progress, and ensure I reached projected timeline benchmarks.
  
            Phase Three included work done after the interview phase was fully complete. During this phase I blocked one week to review all the interview video recordings while reading the transcripts, modelling the whole-part-whole process in P-IP methodology. This allowed me to make note of connections among and between participants’ stories, as I began to notice trends and commonalities. Immediately following this week-long review, I took time to revisit codes already done in NVivo for each transcript (see Table 2) and then created updated coding charts. I revisited the word art collections from the transcripts and created an overarching word art from all the keywords created by the Otter.ai software. As I did a third review of the transcripts, I further redacted the documents to ensure confidentiality, and added notes and memos as marginalia.
  
            The time came to generate unifying codes to discern the overarching research story. I reviewed the codebook within NVivo to combine to reduce the listing and provided detailed descriptions (see Table 3 in Appendix H). Once this was completed, I created a graphic rendering of early and emergent ideas (see Figure 17) and a preliminary concept map (see Figure 18) as I attempted to bring ideas and conceptions together. I shared these digital artifacts with critical friends in my PLN. After receiving feedback, I took a pause from my immersion into the data. During the next period of time I immersed myself in reading and rereading literature, while also attending and viewing webinars relating to coding and generating themes. Phase three ended with a renewed plan for revising themes and organizing quotes for the writing of the findings section of the dissertation.
   
• 1 2022-06-04T15:43:12+00:00 Glossary 19 alphabetic listing of glossary items with links to notes that describe each item plain 2023-06-28T15:29:30+00:00 Here is an alphabetic listing of the glossary items included in this dissertation document. Each item is linked to a note where the item is defined, described, and/or examples provided. These glossary items are also embedded throughout the document as notes within pages, where they provide 'just in time' clarification for you, the reader.
  • Actor Network Theory
  • Affinity Spaces
  • Alternative Dissertation
  • Artificial Intelligence (AI)
  • Black Box technology
  • Block Chain
  • ChatGPT
  • Computer Assisted Qualitative Data Analysis Software (CAQDAS)
  • Creative Commons
  • Cynefin framework
  • Data Gathering
  • Digital Rights Management (DRM)
  • Educommunication
  • Emirec
  • Episteme / Phronesis
  • Faculty of Education (FoE)
  • #FemEdTech
  • Free and Open Software (FOSS)
  • Homo Faber
  • Hupomnemata
  • Interpretive Phenomenological Analysis (IPA)
  • Learning Management Systems (LMS)
  • Makerspace
  • Massive Open Online Course (MOOC)
  • Media and Information Literacy (MIL)
  • Organization for Economic Co-operation and Development (OECD)
  • Open Educational Practices (OEPr)
  • Paywall
  • Platforms
  • PDF
  • Portable Graphics Network (PNG)
  • Post-Intentional Phenomenology (P-IP)
  • Practice - both noun and verb
  • Research Ethics Board (REB)
  • Safety, Security, Privacy, Permission (SSPP)
  • Social Sciences and Humanities Research Council, Canada (SSHRC)
  • Teacher Candidates (TCs)
  • Teacher Educators (TEds)
  • Teacher Educator Technology Competencies (TETCs)
  • TPACK
  • Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans
  • UNESCO
  • Uniform Resource Locator (URL)
  • Universal Serial Bus (USB)
  • Visitors / Residents
  
   
• 1 2022-06-08T21:22:24+00:00 The Gathering 19 relating to data and how it is managed plain 2023-10-02T13:42:01+00:00 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 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 honoured the ethos of openness of OEPr, examinined the multiplicity of textual information, and revealed 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 were 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 were 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. Digital information originating from daily digital interactions by participants in online platforms were “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. Although 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 Macbook Pro 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 forty-eight 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 portable network graphic (PNG) image and provide a web-access link to the interactive word cloud image. 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.
  
            Since both digital and paper forms of research journals were fluid territories for me, I kept both versions of research journals to capture notes of my thoughts and observations. This included annotations, video recordings, and textual artifacts. I curated, stored, and organized notes within participant folders on my computer hard drive, on private blog posts, and posted thoughts on open blog posts when anonymity was maintained. 
  
            Notes and annotations for this research included jottings, descriptive observations of social media sites such as tweets and blog posts, and linkages recorded as marginalia on transcripts. Since jottings and cognitive connections occurred at any time, these were recorded at the time, place, and available media, indicative of the fluid nature of the research process. Saldaña (2016) suggested these private, personal, written and recorded musings become “question-raising, puzzle-piecing, connection-making, strategy-building, problem-solving, answer-generating, rising-above-the-data” heuristics (p. 44). I heeded Saldaña’s (2016) caution to not rely on “mental notes to self” (p. 45) as a method, and took advantage of the technologies at hand to capture my wonderings and wanderings along research paths. 
  
            Vagle (2018) suggested taking walks to provide time and space for phenomenological musings to occur. With the COVID-19 pandemic in full swing as I conducted the research, outdoor walks and bike rides not only provided time to think, or to NOT think, but also became an avenue for mental health and well-being during this research phase. In true P-IP fashion, the technology made me as researcher while I made notes about research data gatherings (see Figure 14). As the liveliness of my notes and musings also became data gatherings, these notes revealed the “affective or entangled engagements with materializations or textualualizations whether as a glow or a strange idea or an imaginative glimpse into a new becoming” (Ellingson & Sotirin, 2020, p. 22).
  
            Throughout the process of gathering these data materials, I created observational notes and began to establish preliminary connections to MDL frameworks. In the early stages of the research, I engaged with data making (Ellingson & Sotirin, 2020) in order to create dynamic representations for each participant. I created and revisited conceptual maps of connections, locations, and literacies using a variety of software in order to “animate new ways of thinking and relating by affirming heretofore unimagined configurations” (Ellingson & Sotirin, 2020, p. 11).