Symposium 5: A Social Constructionist Approach to Phenomenographic Analysis of Identity Positioning in Networked Learning
Bibliographic record
Abstract
The aims of this research are to explore how doctoral students on networked learning courses experience challenges to their identities, norms, values, and relationships. Within a relational, social constructionist perspective towards identity and positioning amongst individuals, an individual's identity is shaped through a continual interaction of dialogue with others; they shape each other in a mutual and cyclical process. This process is at work equally in Networked Learning as in face-to-face interaction with the difference that the medium through which communication occurs is different but influences the construction of identity. The author briefly describes the Vygotsky Cycle (Harré, 2010), threshold concepts (Meyer & Land, 2005), and with particular relevance to doctoral learners, conceptual threshold crossings (Kiley & Wisker, 2010). These three elements underlie the idea of 'identity positioning thresholds'--that is, the process in which a learner is confronted by conflicting opinions, behaviours, and/or perspectives that, if sufficiently critical, may cause them to examine these conflicting experiences or re-evaluate their own opinions, behaviours, and perspectives within their own social, academic, and/or professional contexts. The main interest of this research is to explore the kinds of critical stories or troublesome experiences that might lead to identity repositioning and the variations in which this can be experienced. To this end, the primary methodology being used is phenomenography. The main method of data collection is the semi-structured interview. One participant was interviewed for a brief pilot study. Then, 18 participants were interviewed for the main phase of data collection. Although the study is currently underway at the time of writing, the author describes the next steps in the study. Supplementary methods will be used to help the researcher develop an in-depth and sensitive understanding of the interview transcripts. These secondary methods include both discourse analysis and two-person interviews. After describing the data collection procedures, the author identifies and discusses a variety of issues both arisen and arising. These issues are related to the abstract nature of the topic itself, the co-constructed nature of phenomenographic interviews, the de-contextualizing and re-contextualizing of transcripts, and issues to be aware of when the times comes for analysis and the development of the outcome space. Finally, the author then briefly discusses some approaches to trustworthiness in the phenomenographic research process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".