An Administrative and Faculty Autoethnographic Analysis of Shifting Modalities of Pre-service Technology Education Programming during the Onset of COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic has disrupted our collective normal patterns of behavior in almost all aspects of our personal and professional lives. While many K-12 and post-secondary subject area curricula lend themselves more easily to a migration to online and remote learning, technology education faces unique challenges. This research paper sought to understand the challenges, benefits, and lessons learned through an analysis of the process of re-organizing a pre-service technology education diploma for remote, blended, and face-to-face learning during the early stages of the COVID-19 pandemic. The investigation followed a collaborative autoethnographic methodology as the authors constructed two narratives based on their roles of administering and instructing in a pre-service technology education diploma program. An interpretive descriptive analysis suggests a number of challenges associated with the organizational changes, but also a number of positive outcomes related to the instructional shifts. Challenges included maintaining equitable access to physical materials and technologies for all students, scheduling issues related to changing pandemic rules and regulations, and a loss of social presence with students. Benefits included more student autonomy, less dependence on group work for technical skill development, and the development of alternative delivery models for pre-service technology education that could be used to expand program offerings to non-traditional students.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".