Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
COVID-19 and its ensuing pandemic ignited an atomic bomb on educational systems across the world invoking an emergent and abrupt transition to remote learning. The aftershocks were unpredictable but left a crippled educational system where students were forced into their bedrooms, sometimes deported to their homelands in different time-zones and isolated from their friends and peers. Learning quickly transitioned from social face-to-face interactions to an estranged and detached face-to-computer dependence. Although some introverted students welcomed this transition, many were dissatisfied, and their performance reflected this sentiment. In this study, we compare students’ performance in an undergraduate mathematics class in a large research-intensive university in the Western United States of America over a 2-year time period from 2019 to 2020. This started as a traditional lecture-style course for 3 quarters, transitioned to a hybrid lecture style with integrated adaptive team-based quizzes for 2 quarters, and abruptly changed with the COVID-19 pandemic to online lectures with team-based quizzes for 1 quarter. We demonstrate in our retrospective data analysis that the performance gains from the traditional lecture-style transition to active learning were subsequently lost in the movement to remote learning. We discuss the many obstacles that may have accounted for this loss of performance and suggest future directions for improving remote active learning methodologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it