The CoViBE: An Innovating Self-Paced Elearning to Teach Virtually Bench-Top Practice
Bibliographic record
Abstract
COVID-19 pandemic is a disaster and prolonged crisis that has disrupted the education of millions of students with the closure of schools and universities in world-wide. This hard situation rises the necessity to develop a new teaching method to solve the problem of the massive disruption specially to practice work access. The goal of this paper is to set-up an innovative teaching approach for practical work. The comic as a new self-paced e-learning product to teach bench-top practice: the “CoViBE’’ which means Comic Virtual Bench-top Elearning. For using comics to transform practical work sessions by distance you should at first list all the steps that you need to perform your experiment. Then, you choose the actors and material images. For the third step, you have to decide how many frames you need to your comic trip to develop the following instructions: How to do, What to do, What not to do and What to ask. Moreover, you need to provide flashbacks to remind students what kind of knowledges they need to carry on their experiment; the final step is to include humor. Using online survey, positive feedbacks of 179 students on the CoViBE impact about their learning of practical work allowed us to determine around 80% of satisfaction. Finally, during any other situations for the training period, the CoViBE concept could be used in the future to complete practical work session for revisions, for the internationalization of this education system through distance work and for a hybrid education system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".