Remote Teaching of a Graduate-Level Instrument Repair and Maintenance Course Using Take-Home Kits and Laboratory Demonstrations
Bibliographic record
Abstract
Because of the COVID-19 pandemic, most courses at universities in many countries transitioned to a remote format for the 2020–21 academic year. This presented additional challenges for courses taught with a hands-on component. Here we describe the implementation of a take-home activity kit and laboratory demonstrations to facilitate hands-on learning for a graduate-level instrument repair and maintenance course. Each student was provided with a take-home kit to enable hands-on activities at home, demonstrated by the course instructors during the synchronous lectures. Laboratory demonstrations were presented using short videos, photos, and instrument manufacturer instruction manuals. Student success was evaluated by means of a hands-on practical exam using the take-home kits and a student experience survey. All of the students who completed the survey indicated that they used the kit and felt that it improved their understanding of topics discussed in the synchronous lectures. The take-home kits and laboratory demonstrations enabled active remote learning that not only fulfilled the course learning objectives but also enhanced student experience and practical skills.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".