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. Résumé Nous avons mené une enquête dont les questions portaient sur les 28 critères de qualité (identifiés dans le cadre d’une revue de littérature) des cours en ligne dans l’enseignement supérieur. Cette étude cherchait à faire ressortir dans quelle mesure les bonnes pratiques définies concernant la qualité sont mises en œuvre par les concepteurs pédagogiques et les enseignants, aux Etats-Unis, dans leur processus de conception des cours et le contenu de ces derniers. Bien que les répondants indiquent, pour la plupart d’entre eux, qu’ils intègrent la majorité des critères de qualité dans les cours qu’ils conçoivent ou enseignent, un nombre significatif d’entre eux n’intègre que rarement voire jamais certains de ces critères communément considérés comme ayant un impact positif sur la réussite des étudiants.
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 | 0.044 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".