Digital Literacy and Resilience: How Can Professional Development Prepare Instructors to Succeed in Changing Times?
Bibliographic record
Abstract
Digital literacy is essential for societal participation, making it a crucial aspect of an educator’s role. The importance of digital literacy corresponds with the rising demand for postsecondary digital education in Canada, alongside numerous changes from COVID-19, demographic shifts, and technological advancements, requiring educators to be resilient. However, there is a lack of data to inform decisions about instructor professional development. In this qualitative study, I investigated how digital literacy professional development can enhance the digital resilience of technical and vocational education and training (TVET) instructors in British Columbia. The technology acceptance model provides a context for understanding instructors’ motivation to enhance digital pedagogy. I collected data through ten semi-structured interviews with faculty developers and instructors. Thematic analysis resulted in four key themes: the breadth of instructors’ needs and competencies, TVET-specific professional development, critical digital literacy, and meaningful connections for resilience. The findings include recommendations and strategies for instructors, institutions, and provincial accreditation bodies to consider for future TVET instructor professional development initiatives.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".