Redesigning Human Physiology Labs as Integrated Instructional Units with Comparative Parallel Mechanistic Analyses for Post‐COVID Online College Teaching
Bibliographic record
Abstract
One of the biggest challenges in remote online college teaching of human physiology is the lab component. To overcome this challenge, we redesigned our pre‐COVID face‐to‐face physiology labs with the objective of preserving most of their learning outcomes. Towards this end, we applied the concept of treating labs as integrated instructional units (America's Lab Report, https://www.nap.edu/read/11311/chapter/5 ). Our approach was to replace student hands‐on lab protocol focus with an integrative lab approach that allows the students to not only record data from real‐time, video‐recorded experiments, but also to map them out on the corresponding anatomical architecture and mechanisms under investigation. The video recordings were performed by the instructor synchronously during the class session then posted for the students online as part of an expanded lab package that includes instrumentation and protocol details, experimental parameters and variables, relevant anatomical and physiological mechanistic footnotes, as well as expected lab report format and rubric. Using this approach, physiology labs originally designed separately to investigate principles and mechanisms of diffusion, neuronal reflexes, EMG, ECG, and hemodynamics were replaced with integrated labs designed to allow side‐by‐side comparisons and provide stimulating visual demonstrations of empirical work and relevant mechanistic and anatomical architecture. Two such lab examples are: (1) comparing speedsof: (a) food dye diffusion in solution based on real‐time measurements in a glass plate aligned with measuring scales, (b) action potential conduction based on mapped‐out neuronal pathway lengths and online measurement of relevant reaction time ( https://faculty.washington.edu/chudler/java/redgreen.html ), and (c) blood flow between the cardiac left ventricle and the site of pulse measurement in the finger based on vasculature anatomy and the time delay between the ECG's QRS electrical wave peak and onset of finger mechanical pulse wave, and (2) comparing time, amplitude, distribution, and discreteness of grip strength‐forearm EMG recording with that of the QRS and P‐wave of the ECG with emphasis on skeletal versus cardiac muscle electrophysiological mechanisms of action potential initiation and propagation as well as corresponding muscle mass as well as physiological mechanisms. Having successfully applied this new lab design in Fall 2020, more data is expected in Spring 2021 to be presented at the APS annual meeting in late April 2021.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".