Redesigning Design: Field Testing a Revised Design Rubric Based of iNACOL Quality Course Standards
Bibliographic record
Abstract
Designers have a limited selection of K-12 online course creation standards to choose from that are not blocked behind proprietary or pay walls. For numerous institutions and states, the use of the iNACOL National Standards for Quality Online Courses is becoming a widely used resource. This article presents the final phase in a three-part study to test the validity and reliability of the iNACOL standards specifically to online course design. Phase three was a field test of the revised rubric based on the iNACOL standards against current K-12 online courses. While the results show a strong exact match percentage, there is more work to be done with the revised rubric. Résumé Les concepteurs ont une sélection limitée des normes K-12 de création de cours en ligne à choisir qui ne sont pas bloqués derrière des propriétés exclusives ou des péages informatiques. Pour de nombreuses institutions et états, l'utilisation des Normes nationales pour les cours en ligne de qualité iNACOL devient une ressource largement utilisée. Cet article présente la phase finale d’une étude en trois parties pour tester la validité et la fiabilité des normes iNACOL spécifiquement liées à la conception de cours en ligne. La phase trois était une mise à l’essai sur le terrain de la rubrique révisée établie en fonction des normes iNACOL par rapport aux cours en ligne K-12 actuels. Bien que les résultats montrent un fort pourcentage de correspondance exacte, il y a plus de travail à faire avec la rubrique révisée.
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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 | 0.062 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".