THREE INSTITUTIONS, THREE APPROACHES, ONE GOAL: ADDRESSING QUALITY ASSURANCE IN ONLINE LEARNING
Bibliographic record
Abstract
The rapid growth of online academic programs in higher education has prompted institutions to develop processes and implement strategies to ensure the quality of their online offerings. Although there is no “one-size-fits-all” approach, there are “quality” standards which institutions can effectively implement regardless of context. This paper examines approaches from three different types of institutions in addressing quality assurance in online education on their respective campuses. Specifically, this paper presents three case studies and describes each institution’s 1) background and overview, 2) quality definition, 3) approach to quality assurance, 4) models and approaches, 5) goals, 6) successes, 7) challenges, and 8) lessons learned. A comparison reveals that despite differences in scope, size, location, mission and extent of online development, there is consistency in the institutions’ strategies to addressing quality assurance in online learning.
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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 | 0.040 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".