Instructional Technology Innovation AsTransformational Learning:Female Faculty’s Narratives Of Experience
Bibliographic record
Abstract
Workplaces are potential learning communities that invite critical reflection on practice that can be shared with others. Higher Education (HE) may be described as a workplace in which instructional development activity may be a form of inquiry in which faculty see “the taken-for-granted with new eyes” [33, p.3], prompting them to critically reflect upon their experiences and practice and leading to a foundational reframing of their core beliefs, assumptions, and values and subsequent actions [31]. Instructional innovation in HE can be personally risky, yet this is the level at which transformational thinking and action occurs and is sustained. The incorporation of instructional technology into teaching practice extends an already complex environment, introducing an unfamiliar realm of expertise. This complexity may be increased for female faculty who already experience some degree of marginalization in HE. The study on which this paper is based is a feminist project of narrative inquiry informed by the theoretical constructs of transformative learning, and feminist pedagogy in technology-enhanced environments. In this framework narratives of experience can be understood as “statement(s) of belief, of morality” that are values-based, doing social and political work as they are told [19, p.12]. In this study 47 female faculty from Canadian universities participated in research conversations as both method and site for the construction of personal and sociocultural understanding and change. Comparative analysis of the conversations reveal several interacting themes including psychosocial issues related to female faculty teaching with technology, the role of collaborative design conversations in perspective transformation, and relational practice for action learning.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".