Developing, using, and interacting in the flipped learning movement: Gaps among subject areas
Bibliographic record
Abstract
The purpose of this paper is to investigate the current video collection of an open-access video website (TED-Ed). The research questions focus on its content as evidence of development, its viewership as evidence of use, and flipping as evidence of interaction in informal learning. In late September 2013, 686 video lessons were posted on the TED-Ed website that spanned 12 academic subject categories and 60 academic subject subcategories, as labeled and sorted on the TED-Ed website itself. The findings of the analysis of the TED-Ed video collection indicate several gaps in the humanities, social science, and natural science academic areas in terms of the number of video lessons and viewership. Despite the gaps in the numbers of video lessons and the viewership across those three academic areas, the areas have very similar averages of daily flipped lessons. The future research agenda should focus on the motivation of viewers to create flipped lessons as evidence of learning in an open learning environment.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".