A Case Study Exploring Quality Standards for Quality E-Learning
Bibliographic record
Abstract
In order to satisfy the needs of growing numbers of adult learners, the availability of well-designed, effectively implemented, and efficiently delivered online courses is essential (MacDonald, Stodel & Casimiro, 2006; Palloff & Pratt, 2001). Despite the demand and prevalence of e-learning, there are still concerns regarding the quality and effectiveness of education offered online (Carstens & Worsfold, 2000; Noble, 2002). Too often, in an “effort to simply get something up and running” (Dick, 1996, p. 59), educators have been forced to compromise quality and design. Intensive competition among educational institutions has resulted in quality assurance becoming a critical issue for promoting learning and learning programs. Within this economically motivated environment, online learning has not escaped the scrutiny of quality standards. Quality in online programs is generally defined in terms of the design of the learning experience, the contextualized experience of learners, and evidence of learning outcomes (Jung, 2000; Salmon, 2000). However, the plethora of online learning courses and programs with few standards to ensure the quality of content, delivery, and/or service creates a challenge. The resulting variance in quality makes it difficult for an organization or learner to choose a program that meets their needs and is also of high quality.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".