TAKING EXPERIENTIAL LEARNING ONLINE: STUDENT PERCEPTIONS
Bibliographic record
Abstract
This article examines the impact of an online field experience course designed for Bachelor of Education students during the COVID-19 crisis. When Alberta schools closed two days before preservice teachers' practicum was to begin, all 435 in-school placements had to be canceled. To ensure students were able to progress in their program without disruption, the authors designed a unique online course to replace the traditional in-school practicum. This mixed-methods research study explores the key findings of an online survey of preservice teachers who made the shift to an online environment. The data included examination of course documents and discussions with instructors during weekly community of practice meetings. Through the innovation of the newly created online practicum course, preservice teachers developed an enhanced appreciation for online learning. However, in the absence of kindergarten to grade 12 students, the online practicum was unable to provide some of the more practical aspects of an in-school practicum. The authors have begun to explore a gap in preservice teacher education, which they have coined digital instructional literacy.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".