Addressing diverse learner preferences and intelligences with emerging technologies: Matching models to online opportunities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper critically reviews various learning preferences and human intelligence theories and models with a particular focus on the implications for online learning. It highlights a few key models, Gardner’s multiple intelligences, Fleming and Mills’ VARK model, Honey and Mumford’s Learning Styles, and Kolb’s Experiential Learning Model, and attempts to link them to trends and opportunities in online learning with emerging technologies. By intersecting such models with online technologies, it offers instructors and instructional designers across educational sectors and situations new ways to think about addressing diverse learner needs, backgrounds, and expectations. Learning technologies are important for effective teaching, as are theories and models and theories of learning. We argue that more immense power can be derived from connections between the theories, models and learning technologies. Résumé : Cet article passe en revue de manière critique les divers modèles et théories sur les préférences d’apprentissage et l’intelligence humaine, avec un accent particulier sur les implications qui en découlent pour l’apprentissage en ligne. L’article présente quelques-uns des principaux modèles (les intelligences multiples de Gardner, le modèle VAK de Fleming et Mills, les styles d’apprentissage de Honey et Mumford et le modèle d’apprentissage expérientiel de Kolb) et tente de les relier à des tendances et occasions d’apprentissage en ligne qui utilisent les nouvelles technologies. En croisant ces modèles avec les technologies Web, les instructeurs et concepteurs pédagogiques dans les secteurs de l’éducation ou en situation éducationnelle se voient offrir de nouvelles façons de tenir compte des divers besoins, horizons et attentes des apprenants. Les technologies d’apprentissage sont importantes pour un enseignement efficace, tout comme les théories et les modèles d’apprentissage. Nous sommes d’avis qu’en établissant des liens entre les théories, les modèles et les technologies d’apprentissage, il est possible d’obtenir un résultat plus puissant. This article has been translated to Armenian. Click here for the translation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it