Monkeys on the Screen?: Multicultural Issues in Instructional Message Design
Bibliographic record
Abstract
With the shift in numbers between Canadian-born students in the university classroom and the increased number of international students, it is a primary concern for instructors and instructional designers to know and understand learner characteristics in order to create effective instructional messages and materials. Recognizing how culture might shape cognition and learning, we can value and design for the diversity of students and maximize their learning while improving the learning environment for all students. To celebrate cultural diversity and meet the challenges associated with designing for diverse learning styles and educational experiences, this paper offers a review of the literature and proposes a systematic three-fold approach to the creation and evaluation of multicultural instructional messages and materials: first, “Do no harm”; second, “Know your learner”; and third, “Incorporate global concepts and images into instructional messages.” Résumé Avec le renversement des proportions d’étudiants nés au Canada et d’étudiants internationaux qui sont de plus en plus nombreux dans nos universités, connaître et comprendre les caractéristiques des apprenants constitue maintenant une préoccupation majeure pour les instructeurs et les concepteurs pédagogiques afin de créer des messages et du matériel pédagogiques efficaces. En prenant en considération la façon dont la culture peut influencer la cognition et l’apprentissage, nous pouvons tenir compte de la diversité des étudiants lors de la conception et ainsi maximiser leur apprentissage tout en améliorant l’environnement d’apprentissage pour tous les étudiants. Dans le but de célébrer la diversité culturelle tout en relevant les défis associés à la conception pour divers styles d’apprentissage et d’expériences éducatives, le présent article présente un examen de la documentation et propose une approche systématique en trois volets pour la création et l’évaluation de messages et de matériel pédagogiques : premièrement, « ne pas nuire »; deuxièmement, « connaître l’apprenant »; et troisièmement, « incorporer des concepts et des images universels dans les messages pédagogiques ».
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 | 0.041 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".