Notice bibliographique
Résumé
On the Nature of Expertise in SoTLIn 2004, David Pace published a paper in American Historical Review making a compelling case for the growth of a Scholarship of Teaching and Learning (SoTL) in history.In his title, Pace made reference to "the amateurs in the operating room" to describe the gap between the expertise historians bring to their research and that which they bring to their teaching.It is, of course, a gap that is not restricted to history.The phrase "amateurs in the operating room" resonated with many of us back in 2004, and in many ways it still does.We like to think that the gap between disciplinary research expertise and what Lee Shulman (1986) called "pedagogical content knowledge" has narrowed somewhat in the last 12 years, especially with the proliferation of programs for graduate students and post-doctoral fellows to help them develop instructional skills and knowledge.But the gap still exists.And another gap has entered the discourse-the perceived gap between disciplinary research expertise and SoTL-based research expertise.Many SoTL practitioners, including authors found in the pages of Teaching & Learning Inquiry, started out conducting their inquiries in a discipline other than SoTL.They may have received intensive training for this research as they pursued advanced degrees.Now they conduct inquiries into teaching and learning.From whence springs the expertise to do so?This question is fundamental to SoTL's impact and credibility.To answer it, we must take a close look at the concept of expertise.For many, expertise is something that one attains through hard work and exposure to other experts.Perhaps that work involves the 10,000 trials advocated in Malcom Gladwell's (2008) book, Outliers.However it is attained, the assumption is that, once attained, this expertise is then employed to make and disseminate further discoveries.We might call this the "all-or-none law" of expertise.Expertise is viewed as a milestone after which everything about us is changed.We get our 10,000 trials t-shirt.From this view, expertise is akin to licensing.You have it for life, perhaps pending occasional re-tests to be sure you haven't lost it.Academics who have been approached by members of the media to offer an expert opinion have probably encountered this view of expertise.Reporters are often unimpressed by answers to questions that are prefaced by "Actually, I'm still learning about this."They want to know: Are you an expert or are you not?They hope you are, because it is much more compelling to say that "experts have concluded…" than "learners have concluded…."Even so, it is precisely the "all-or-none law" that makes us uncomfortable about the notion of expertise.It doesn't make sense to think of expertise as something fully attained and sustained.The whole idea of the academy is to push boundaries and grow in new directions.Yet it is proponents of the "all-or-none law" who will argue that SoTL research is conducted by non-experts-amateurs in a new operating theatre.
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 | 0,010 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,004 | 0,045 |
| Communication savante | 0,008 | 0,024 |
| Science ouverte | 0,001 | 0,008 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 ».