E-Based Practicum: A COVID-19 Model Worthy of Retention for Student-Teachers in Guyana
Bibliographic record
Abstract
Practicum is critical to the teacher education programme at the University of Guyana. Practicum offers opportunities and experiences for skills development and simulations to enable student-teachers to acquire and demonstrate effective pedagogical practices and innovations. In 2020, the COVID-19 pandemic reconfigured practicum to e-based modes, capitalising on mock teaching and video-stimulated reviews. A descriptive survey allowed 241 student-teachers to visualise and categorise their experiences, challenges and potential opportunities from this new learning mode. The elements of e-based practicum that make it worthy of retention include its capacity for autonomous off-campus learning and experimentation, partnership and equitable relations, performance pacing and gauging, archiving of pedagogical growth, technology literacy skills development, and reflective and self-correcting practice. The experiences of these student-teachers could help (re)shape the practicum delivery for future cohorts and be informative for reviewing and upgrading practicum courses that rely solely on physical classroom interactions, observations, and supervision.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".