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Enregistrement W2070629633 · doi:10.1002/bmb.20160

Commentary: Interactive whiteboards

2008· article· en· W2070629633 sur OpenAlexaboutno aff
Graham R. Parslow

Notice bibliographique

RevueBiochemistry and Molecular Biology Education · 2008
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueEducation and Technology Integration
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésInteractive whiteboardWhiteboardLaptopComputer scienceUSBOverhead projectorMultimediaCurriculumHuman–computer interactionVisual artsSociologySoftwarePedagogy

Résumé

récupéré en direct d'OpenAlex

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,301
Score d'incertitude au seuil0,258

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,009
Tête enseignante GPT0,333
Écart entre enseignants0,324 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2008
Routes d'admission1
Résumé présentoui

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