Notice bibliographique
Résumé
The Learning in Depth project, or LiD, is a recent attempt by Kieran Egan to address a widespread educational problem by way of a simple classroom-based project.Students suffer from a breadth of what can only loosely (and very generously) be called knowledge and a lack of depth of knowledge.The basic premise of the project that addresses the problem is: students will receive a topic from a pre-selected list that they will research in-depth to develop some expertise in something, and along the way develop skills, habits, and dispositions that only learning in depth can provide.While students can begin the project at any point, the intention is that students will receive their topics in their first year of school and continue developing expertise in this area throughout their K-12 years.As with Egan's other book, Getting it Wrong From the Beginning: Our Progressivist Inheritance from Herbert Spencer, John Dewey, and Jean Piaget (2002), Learning in Depth begins by framing the educational problem that he aims to solve, "'The kids these days' know nothing" (p. 1).Unlike Getting it Wrong, however, the problem is briefly and succinctly described, and the majority of the book is dedicated to the practicalities of implementing the solution.Egan's major concern is that students are not learning anything in the regular curriculum that they find valuable or relevant enough to remember; thus, they are not truly learning anything.Egan's premise is that the breadth of topics in public school curricula is taught at the expense of the depth required for critical thinking to truly occur.He frames this argument in terms of how society defines an educated person and posits that this is an essential component of learning that is not being reached in schools.An educated person must have both depth and breadth of knowledge.Egan goes on to argue that the depth of knowledge that one accumulates on one topic spans far beyond that one topic, providing access to the nature of knowledge itself."With regard to the knowledge we learn in breadth, we rely always on the expertise of others; when learning in depth, we develop our own expertise.It is assumed that learning something in depth carries over to a better understanding of all our other, 'breadth,' knowledge."(p.6, emphasis in original).Although LiD is an addition to rather than a replacement for the regular curriculum that is primarily student-driven and takes up minimal class time, it appears to offer benefits in every aspect of a child's learning.This approach brings to mind everyone's favourite educational catch-term: critical thinking.From kindergarten to postsecondary education, many educators hear the argument that content is secondary and what students really need to learn are critical thinking skills that they will then
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,004 | 0,008 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».