Speeding lectures to make time for retrieval practice: Can we improve the efficiency of interpolated testing?
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
Testing is increasingly recognized as an important tool in learning. One form of testing often used in lectures, particularly recorded lectures, is interpolated testing wherein tests are interspersed throughout the lecture. Like testing in general, interpolated testing appears to benefit performance on content tests among other outcome variables (e.g., mind wandering). While beneficial, adding testing also increases instructional time. In the present investigation, we examine one strategy to mitigate the costs of this increase in instructional time in the context of recorded lectures. Specifically, we examine the interaction between increasing the playback speed of a recorded lecture and adding interpolated tests. Results demonstrate that the conjoint effects of these two interventions are largely additive. That is, the benefit of testing was as robust in a normal speed lecture and a lecture that was sped up 1.5×. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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 | 0.001 | 0.001 |
| 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 it