Assessment of experiential learning in online introductory physics labs during COVID-19
Bibliographic record
Abstract
Abstract The online delivery of experiential learning in physics lab classes in a New York City community college during COVID-19 has been a challenge in terms of preparation time, restricted mobility, and limited resources. On one hand, the standard in-person lab learning outcomes in the understanding of uncertainty resulted from massless pulley or spring assumption, floor tilting, model difference y = ax versus y = x + b, graphical intercept method to show systematic error , weighting error null offset, etc. can be adapted to an online setting when given preparation time. On the other hand, the essence of online lab must include the delivery of some level of experiential experience with justification from the literature on pedagogy. Simon Fraser University posts a definition of experiential learning, which states "The strategic, active engagement of students in opportunities to learn through doing, and reflection on those activities, which empowers them to apply their theoretical knowledge to practical endeavours in a multitude of settings inside and outside of the classroom". There have been numerous lab videos on Youtube created in the pre-lockdown era and the level of experiential learning in terms of "doing a lab" can be delivered by limiting a tactile measurement to a visual-audio only measurement on an real or simulated image of an instrument reading. The experiential learning of simulation experience would broaden lateral thinking but the real life transference to "doing a lab" would not be easy to assess, during lockdown condition in New York City when in-person final practical exams are excluded, without using a metacognition approach. The construction of an assessment rubric for online experiential learning, based on the McGill University in-person experiential learning assessment principle in content-process mixture, big picture perspective, and reflection, is presented. The advances in artificial intelligence software in the extension of online experiential learning are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".