Notice bibliographique
Résumé
Interactive whiteboards allow manipulation of a projected computer display using physical objects (colored pens and eraser) or finger traces and taps to mimic a mouse. This makes the whiteboard into a large scale touch-sensitive computer screen. My introduction to this technology was an article by a local schoolteacher titled “Let me have a turn”1. This article described how the technology had transformed the author's traditional teaching into more enjoyable interactive sessions. This teacher taught classes in biology and chemistry and used a combination of commercial, public domain, and self-prepared lessons in PowerPoint. As lessons progressed, key words were added and calculations completed in response to student input. Finished lessons can be saved and shared. I was amazed to find how universally popular and common interactive whiteboards have become in school teaching2, with a survey carried out in the United Kingdom revealing that 98% of secondary and 100% of primary schools have the technology3. The company Smart Technologies from Calgary Canada claim to have created the technology in 1991 using an overhead projector coupled with an LCD filter screen. They persisted in refining the technology as more appropriate hardware became available and now claim to be the market leaders. This is a credible claim with many countries having user groups centered on SmartBoards, produced by Smart Technologies, and sharing substantial regional curriculum support packages4. An interactive whiteboard communicates with a computer through a USB or wireless connection and establishment of the link can be entirely automatic. The sensors to follow finger actions can be either surface responsive or optical tracking. The technical details are well described in a Wikipedia article3. Even better is to take an introductory tutorial recorded using an interactive whiteboard5. An obvious problem for the front projection is the shading of the projection by the user's hand and body. The shading and projector placement problems can be overcome with rear projection systems that also mean that the presenter does not have to look into the projector light while speaking to the audience. Pictures of these systems can be found at commercial sites4, 6 along with the prices that surprised me for their affordability. The teachers who adopt this technology are reported to use it intensively in the same way that most of my university colleagues use projected PowerPoint lectures as their dominant teaching technology. Differences between school settings and universities are in part the smaller class size in schools and a tendency by school teachers to adopt repetitive drills and progression linked to class comprehension. These factors have favored interactive whiteboards to overcome the traditional conservatism of schoolteachers and thereby make these boards widely adopted internationally in schools. I asked a senior medical-education IT administrator about the use of interactive whiteboards at my university. His rather dismissive response was that interactive whiteboards had been around for years and raised no interest outside the arts faculty. Interactive whiteboard technology is clearly going to be very familiar to the students entering our universities, because they will have been in class rooms using them through all of their school years. I suspect that for teaching science at universities, interactive whiteboards will be most appropriate in laboratory classes.
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 | 0,011 | 0,092 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,012 | 0,014 |
| Communication savante | 0,009 | 0,013 |
| Science ouverte | 0,010 | 0,006 |
| Intégrité de la recherche | 0,100 | 0,090 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,036 | 0,023 |
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 ».