This page is referenced by:

• 1 2022-06-04T15:43:12+00:00 Glossary 17 alphabetic listing of glossary items with links to notes that describe each item plain 2023-06-27T12:14:19+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
  • Data Analysis - deductive
  • 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)
  • Open Education
  • Organization for Economic Co-operation and Development (OECD)
  • Open Educational Practices (OEPr)
  • Paywall
  • PDF
  • Platforms
  • 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 15 relating to data and how it is managed plain 2023-06-23T14:55:46+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 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 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. 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 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 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.
  
            For the duration of the research I kept both digital and paper versions of a research journal for notes and observations since both were fluid territories for me. 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 textualizations 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).