Assessment of experiential learning in online introductory physics labs during COVID-19
Bibliographic record
Abstract
The sudden COVID lockdown has been a challenge for community college faculty in terms of preparation time, restricted mobility and limited resources.On the one hand, the standard faceto-face lab learning outcomes that are used to assess a student's understanding of the uncertainty, the difference between a "y = ax" versus "y = ax + b" data model, the use of graphical intercept to show systematic error, etc. can be adapted to an online setting when given adequate preparation time.On the other hand, the essence of online lab teaching must include the delivery of some level of experiential activity for which there is convincing evidence in the literature on pedagogy of its worthwhile application.Asynchronous delivery of experiential learning could be used for the synchronous delivery of that learning while the reverse application would require major design changes.Numerous lab videos have been posted on YouTube that were created in the pre-lockdown era and the level of experiential learning in terms of "doing a lab" can be evaluated by replacing the tactile measurement performed in a real lab with an audiovisual measurement shown in a video of a real or simulated instrument in operation.Learning experiences involving simulations would broaden lateral thinking.The "real life doing a lab" transference to "remotely doing a lab" would not be easy to assess during lockdown, when faceto-face practical final exams are impracticable to schedule.Assessment would certainly include grading but grading alone would not provide an adequate holistic assessment.The construction of an assessment rubric for the online experiential learning, based on the McGill University faceto-face experiential learning assessment principle concerning content-process mixture, big picture perspective and reflection is presented here.The advances in artificial intelligence software as it pertains to online experiential learning are discussed.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".