A dual‐mode university instructional design model for academic development
Bibliographic record
Abstract
Online learning is expanding rapidly today in many Canadian universities. Fuelled by Canada's unprecedented broadband access, online teaching is creating new challenges for faculty and new responsibilities for educational developers. Although there is adequate literature with regard to faculty thinking about course planning in general, there are few publications on how they actually plan for online teaching. The purpose of this applied research study was to develop an instructional design model adapted to the needs and resources of faculty at a dual‐mode university (offering courses both on‐campus and online). Using a case study methodology, this project documented the prototype‐building, its implementation and gradual modification in response to faculty feedback. Results indicate that faculty preferred a more traditional, discipline‐based course design model for online course planning and shunned high‐level instructional design, opting for lighter‐weight, dialog‐rich instructional design emphasizing real‐time, faculty–student interaction. De nos jours, l'apprentissage en ligne gagne rapidement du terrain dans plusieurs universités. Et l'apprentissage en ligne amène de nouveaux défis pour les enseignants tout comme de nouvelles responsabilités pour les conseillers pédagogiques. L'objectif de cette recherche appliquée était de développer un modèle de design pédagogique qui est adapté aux besoins et aux ressources des enseignants provenant d'une université dont les cours sont dispensés à la fois sur le campus et en ligne. A l'aide de la méthode des études de cas, cette recherche a documenté le développement d'un prototype, sa mise en oeuvre et sa modification graduelle en réponse au feedback obtenu auprès des enseignants. Les résultats indiquent que les enseignants préfèrent un modèle de design pédagogique davantage traditionnel, axé sur la discipline, pour la planification des cours en ligne et qu'ils ont évité le design pédagogique de haut niveau.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".