Comparing CANeLearn Design Principles for K-12 Online Learning with Researched Models and Standards
Bibliographic record
Abstract
In 2023, researchers explored the relationship between Design Principles for K-12 Online Learning (Crichton & Childs, 2022) and quality standards for K-12 online learning, publishing a report on the intersection between design principles and standards (LaBonte et al., 2023). Design principles refer to the fundamental concepts and guidelines that inform the creation and implementation of educational programs, materials, and systems (Kukulska-Hulme & Traxler, 2013), while standards tend to be more discrete, flexible, and responsive to local conditions (Bell, 2003). Unlike standards, the design principles focus on institutional support of technology, infrastructure, students, and faculty, as well as program effectiveness and assessment which are not described in most standards. The Community of Inquiry (COI), a research-based model describing the three interdependent elements of social, cognitive, and teaching presence (Garrison et al., 2000), was used to explore the intersection points between the COI, design principles, and published standards for K-12 online learning. The analysis was used to revise the design principles further and support the ongoing development of quality standards. It is hoped that basing design principles and standards in the context of a research-based model will further develop an understanding of quality in K-12 online learning and inform practice.
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How this classification was reachedexpand
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 | 0.006 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".