The Incorporation of Quality Attributes into Online Course Design in Higher Education.
Bibliographic record
Abstract
A survey was designed incorporating questions on 28 attributes shown in the literature to be quality features in online academic courses in higher education. This study sought to investigate the ongoing practice of instructional designers and instructors in the United States with respect to their incorporation of these quality best practices into their design process and course content. Although most respondents indicated they included the majority of the quality attributes in courses they designed or taught, a significant number rarely or never included some of the generally accepted features shown to result in positive learner outcomes. Resume Nous avons mene une enquete dont les questions portaient sur les 28 criteres de qualite (identifies dans le cadre d’une revue de litterature) des cours en ligne dans l’enseignement superieur. Cette etude cherchait a faire ressortir dans quelle mesure les bonnes pratiques definies concernant la qualite sont mises en œuvre par les concepteurs pedagogiques et les enseignants, aux Etats-Unis, dans leur processus de conception des cours et le contenu de ces derniers. Bien que les repondants indiquent, pour la plupart d’entre eux, qu’ils integrent la majorite des criteres de qualite dans les cours qu’ils concoivent ou enseignent, un nombre significatif d’entre eux n’integre que rarement voire jamais certains de ces criteres communement consideres comme ayant un impact positif sur la reussite des etudiants.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".