Remote learning might be new, but how we can learn best is not
Bibliographic record
Abstract
The challengeIs learning significantly different in a remote environment?As part of our institution's teaching and learning center, our team provides interactive workshops for students on learning skills, metacognition, time management, learning in your second (or third) language, a wealth of different study tactics, including how and when to use them, and more.We believe anyone can learn anything and we strive to help students learn better through our programming and resources, which are grounded in research on learning capacity building and the scholarship of teaching and learning.Before terms like social distancing, remote learning, and Zoom fatigue became part of our common vocabularynamely before March 2020our programming was primarily centered around in-person learning experiences in an active learning classroom and facilitated by trained graduate student assistants.While we also offered a couple webinars through Zoom, they were the exception.Just as for instructors and students, we needed to adapt when in-person support became impossible.Our more specific challenge concerned what exactly we might need to change to fit with the new reality of the remote teaching and learning context.We asked ourselves, how different, really, is remote learning from learning in general?Are we facing a crisis of content?Will the strategies that worked for students before no longer apply?After wrestling with this as Hanson and Liepins Remote learning might be new, but how we can learn best is not
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.009 | 0.037 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.052 | 0.013 |
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".