Improving the K-12 Online Course Design Review Process: Experts Weigh in on iNACOL National Standards for Quality Online Courses
Bibliographic record
Abstract
Within the K-12 online learning environment there are a variety of standards that designers can utilize when creating online courses. To date, the only research-based standards available are proprietary in manner. As such, many jurisdictions have begun adopting online course design standards from the leading advocacy organization, which that have yet to be validated from a research perspective. This article reports on the second phase of a three-stage study designed to examine the validity and reliability of the iNACOL National Standards for Quality Online Courses. Phase two utilizes two groups of expert reviewers to examine and provide feedback with goal of further refining these standards (after the standards had been scrutinized through the lens of the available K-12 online learning literature).
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.
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 | 0.540 | 0.649 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".