A Taxonomy of Asynchronous Instructional Video Styles
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
<p class="FirstParagraph">Many educational organizations are employing instructional videos in their pedagogy, but there is a limited understanding of the possible video formats. In practice, the presentation format of instructional videos ranges from direct recording of classroom teaching with a stationary camera, or screencasts with voice-over, to highly elaborate video post-production. Previous work evaluated the effectiveness of several production styles, but there has not been any consistent taxonomy, which would have made comparisons and meta-analyses possible. Therefore, we need a taxonomy of instructional video formats that facilitates the understanding of the landscape of available instructional video production styles. For this purpose, we surveyed the research literature and examined contemporary video-based courses, which have been produced by diverse educational organizations and teachers across several academic disciplines. We organized instructional video styles in two dimensions according to the level of human presence and to the type of instructional media. In addition to organizing existing instructional videos in a comprehensive way, the proposed taxonomy offers a design space, which should facilitate choice, as well as the preparation of novel video formats.</p>
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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