Conversion of a Senior Instrumental Analysis Laboratory Course to Online Delivery and Remote Learning
Bibliographic record
Abstract
The global Coronavirus disease 2019 (COVID-19) pandemic and associated restrictions on indoor gatherings posed considerable challenges to the delivery of courses that traditionally have relied on hands-on learning in their laboratory components. Here, our experiences converting a senior instrumental analysis course to remote online learning in the Fall 2020 semester are described. The main objective was the production of laboratory videos in lieu of in-person experiments that maintained a high level of authenticity. Videos of six instrumental analysis experiments were created. Experiments were performed and recorded using a headband or tripod mounted “GoPro Hero8” camera by a teaching assistant, providing students with a first-person perspective. The videos were edited in “Camtasia” software and posted on the course’s “Desire2Learn” (D2L) Web site for students to view on-demand. This allowed lectures to be coordinated with the experiment schedule, which permitted the discussion of theory (in the lecture) and experiments (in the online laboratory) to be tightly integrated. Student performance (assessed through summative oral exams) and student feedback from anonymous electronic surveys were generally positive, though course completion rates dropped. The laboratory videos were utilized when the laboratory returned to in-person delivery in the Fall 2021 and 2022 semesters. The availability of first-person videos was found to enrich the students’ experience, improve preparation, reduce anxiety, and foster a more inclusive environment by allowing for the scheduling of online makeup laboratories (in case of missed lab experiments), hence representing a valuable tool for student success.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.056 | 0.021 |
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".