Shifting online: 12 tips for online teaching derived from contemporary educational psychology research
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
Abstract Background As a result of the COVID‐19 pandemic, many teachers found themselves making a rapid and often challenging shift from in‐person classroom teaching to teaching in an online environment. As teachers continue to learn about working in this new environment, research in cognitive and learning sciences, specifically findings from cognitive load theory and related areas, can provide meaningful strategies for teaching in this ‘new normal’. Objectives This paper describes 12 tips derived from contemporary research in educational psychology, focusing particularly on empirically supported strategies that teachers may apply in their online classroom to ensure that learning is optimized. Implications for Practice These strategies are generalizable across age groups and learning areas, and are categorized into one of two themes: approaches to optimize the design of online learning materials, and instructional strategies to support student learning. A discussion follows, outlining how teachers may apply these strategies in different contexts, with a brief overview of emerging efforts that aim to bridge cognitive load theory and self‐regulated learning research.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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