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.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.124 | 0.045 |
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".