Wicked ID: Conceptual Framework for Considering Instructional Design as a Wicked Problem
Bibliographic record
Abstract
The process of instructional design has parallels in other design disciplines. Software design is one that has experienced intense attention in the last 30 or so years, and many lessons learned there can be applied to ID. Using software design as a springboard, this concept paper seeks to propose a new approach to ID. It suggests that instructional design is almost always a Wicked Problem. The connection is formed between Wicked Problems as first described by Rittel and Webber in 1973, and the models in and processes of instructional design. The areas of social planning, organizational management and software design all posses some accepted and tested approaches to the solution of Wicked Problems. These will be described, and how they can be applied to ID will be explained. Finally, this paper will propose a meta-model for ID and explain how it can be used in the current context. Résumé : Le processus de conception pédagogique a des similitudes avec d’autres disciplines. La conception de logiciels a fait l’objet d’une grande attention dans les trente dernières années et de nombreuses leçons tirées peuvent s’appliquer à la conception pédagogique. En se basant sur la conception de logiciels, cet article cherche à proposer une nouvelle méthode à la conception pédagogique. Il laisse entendre que la conception pédagogique constitue presque toujours un problème épineux. Le lien est formé entre les problèmes épineux tels que décrits par Rittel et Webber en 1973 et les modèles et processus de la conception pédagogique. Les secteurs de l’organisation sociale, de la gestion des organisations et de la conception de logiciels comptent des méthodes acceptées et validées à la résolution de problèmes épineux. Nous les décrirons et expliquerons comment ils peuvent servir à la conception pédagogique. Enfin, l’article proposera un meta-modèle de la conception pédagogique et expliquera comment il peut servir dans le contexte actuel.
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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 | 0.028 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".