Ten Key Factors for Making Educational and Instructional Videos
Bibliographic record
Abstract
Drawing on experiences in creating instructional videos for multiple projects, this reflective article discusses a ten-factor framework for the practical benefit of educators wishing to develop educational videos for audiences both within and outside of academic contexts. Informed by literature on best practices in video design from both cognitive scientists and other instructional video creators, the article emphasizes that there is no universal approach to making design decisions. The article explores key questions and tensions in the development process through a consideration of the elements of audience, purpose, resources, scripting, visuals, accessibility, interactivity, distribution, sustainability, and execution.Résumé Cet article de réflexion s’appuie sur des expériences acquises lors de la création de vidéos pédagogiques pour de multiples projets. L’objectif de l’article est de présenter un guide en dix points en vue d’aider les éducateurs désirant créer des vidéos pédagogiques destinées à des publics tant académiques que non-académiques. Pour atteindre son but, l’article se rapporte à la littérature sur les meilleures pratiques en matière de conception vidéo provenant à la fois de spécialistes des sciences cognitives et d’autres créateurs de vidéos éducatives. En même temps, il souligne qu’il n’y a pas une seule approche universelle pour prendre des décisions sur la réalisation de vidéos pédagogiques. L’article explore les questions et les tensions clés de cette réalisation en examinant les éléments suivants : public, objectif, ressources, scénario, éléments visuels, accessibilité, interactivité, distribution, durabilité et exécution.
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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 | 0.022 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".