Enabling in-car location-based experiential learning with Presentria GO
Bibliographic record
Abstract
The COVID-19 pandemic has changed how millions around the globe are educated. The 2nd or 3rd waves of the disease have made learning in classrooms unsafe once again. Many schools are forced to send their students home to take online classes under their government's lock-down protocols. For many young learners, engaging with school is a significant part of their well-being, which has been compromised by the extended period of remote learning and low social interaction levels during the pandemic. New and innovative solutions to address learners' needs have been called during this pandemic. The Presentria GO system is an innovative solution that enables students from K-12 to higher education to learn experientially from their cars during a city excursion. Through a survey with 74 educators and a series of expert interviews and focus group discussions, insights into the feasibility of this active learning mode are explored. This paper proposes the concept of 'in-car location-based experiential learning' as one of the methods to engage students during the pandemic and beyond.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".