E‐learning and research experience exchange in the online setting of student peer mentor network during <scp>COVID</scp>‐19 pandemic and beyond: A laboratory case study
Bibliographic record
Abstract
For close to 2 years, we have witnessed the impacts of the SARS-CoV-2 pandemic on research at several different levels. Among the list, limited access to laboratory-based training for undergraduate students prevented this cohort from gaining exposure to the realities of a research laboratory at a critical time in training when they may have found motivation in this area as a career. COVID exposed a weakness in our training pipeline; an extreme dependency on face-to-face training that threatened to create a void in the research talent needed to replenish the scientific community every year. In the classroom, we witnessed a revolution of e-learning based approaches that could be rapidly implemented based on existing footprints. Out of necessity, our laboratory developed and implemented an e-learning model of an undergraduate peer mentor network that provides a knowledge and experience exchange platform between students with different levels of research experience. Implementation of the platform was to aid students with gaining knowledge in multiple aspects of scientific research and hands-on work in a research laboratory. The collaboration between the students of the network was aimed at not only advancing the theoretical and practical research experience, but also at developing feedback implementation and practicing "soft skills" critical for teamwork and leadership. Herein, we present an overview of the model along with survey responses of the students participating in the peer mentor network. We have found that peer delivery of practical benchwork both via scientific presentations and visualized experiments, reduced the time of training and the amount of staff assistance needed when students returned to the bench. Furthermore, this model accelerated student independence in laboratory work and increased research interest overall. In summary, the model of a peer mentor network has the potential to serve as a training platform and as a customized tool, supplementing research laboratory training at the undergraduate level beyond the pandemic.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".