Keeping students connected and engaged in a wet-lab research experience during a time of social distancing via mobile devices and video conferencing software
Bibliographic record
Abstract
Abstract Two major COVID-19 pandemic challenges presented for in-person instruction included adhering to social distancing guidelines and accommodating remote learners who were temporarily isolated or permanently participating from afar. At Binghamton University, our First-year Research Immersion (FRI) program was challenged with providing students with a wet lab course-based undergraduate research experience (CURE), an intense hands-on experience that emphasized student teamwork, lab protocol development, iteration, troubleshooting and other elements of the process of science that could not be replicated in a fully remote environment. We developed an innovative technology approach to maximize all students’ connection to the lab research experience utilizing dedicated mobile devices (iPod Touch) and video conferencing software (Zoom) to synchronously connect remote learners to in-person learners, peer mentors and instructors in our FRI research labs. In this way, despite limited lab capacities and fluctuating remote learning populations, we were able to connect remote learners to their peers and mentors in real-time and give them responsibilities that allowed them to be engaged and feel like meaningful participants in the research process. Although our students reported a preference for in-person labs, they noted this hybrid model was better than other traditionally employed remote-learning lab options. We believe lessons learned here can be applied to improve access to research in all situations and allow us to be prepared for other catastrophic disruptions to the educational system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".