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Web-Based Training

2007· other· en· W7040158960 sur OpenAlexaboutno aff

Notice bibliographique

RevueAUSpace (Athabasca University) · 2007
Typeother
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTrainerQuality (philosophy)Training (meteorology)ConfusionThe Internet
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Current Web-based training (WBT) is based upon systematic research and experience with strategies for improving learning
\nand instruction, beginning in the early part of the 20th century and continuing to the present. Use of the World-Wide Web for
\ndelivery may improve access to training, but the effectiveness of the resulting training and the usefulness of the outcomes is
\nchiefly dependent upon the quality of the instructional design and the completeness of the support package provided. Factors
\nthat impact WBT quality, and which must be addressed in design and implementation processes, include assessment and
\naccommodation of trainees’ previous learning experiences, training expectations, and overall readiness for new training;
\navailability and familiarity to trainees and trainers of appropriate delivery technologies; presence of technical support;
\nopportunities for interaction with the trainer and other trainees; the preparation and practices of trainers; corporate support and
\nrecognition; trainees’ capacities and expectations for independent and self-directed learning; and the presence of relevant,
\nquality online training materials.
\nWBT creates changes and may thus produce stresses in the training environment, as well as efficiencies. Reduction in travel
\nand subsistence requirements means cost savings, but may also be seen by trainees as depriving them of opportunities to meet
\nwith each other face-to-face; self-pacing means trainees may proceed independently and at their own rate, but also that group
\nsupport may be reduced (unless a cohort model is adopted); use of the Internet for delivery of training materials may foster
\ntrainee independence, but may also result in confusion for some trainees used to print materials and a paced, group delivery
\nmodel; trainers no longer have to lecture as materials (always high quality, and often multimedia-based) are prepared in
\nadvance, but some may resent the loss of their role at center-stage; trainees are more responsible for their own learning, which
\nmay reflect the autonomy of adult responsibility common in the other areas of their lives, but this may be different from the
\nexpectations of some for how training should be conducted.
\nTo achieve the efficiencies and advantages well-designed and -managed WBT may offer, adopting organizations must make
\nadjustments. Managers may need to show concrete support for online training by permitting trainees to use corporate
\nresources during company time, to assure access to adequate bandwidth. Trainers may need to master new skills and be
\nwilling to adopt new roles less concerned with information dissemination and more involved with meeting individual trainees’
\nexpressed needs. Trainees themselves may also need new skills, and may need to exercise more independence and selfdirection
\nin their learning.
\nAs technologies become more available to support WBT, and as more models of successful WBT are available, the
\ncommitment to this delivery model is predicted to continue to grow. The previous corporate experience of the “productivity
\nparadox” in relation to computers, in which some succeeded in improving productivity while others did not—and some even
\nexperienced productivity losses—will need to be avoided, especially in relation to promising innovations such as reusable
\nlearning objects. Similarly, arrival of the noncommercial “new Internets” in Canada and the United States constitute a fresh
\nstart, an opportunity to demonstrate the value of these resources for academic and research purposes.
\n2
\nChoices of the right technologies, effective use of these choices, attention to security and privacy concerns, adequate training
\nand support of users at all levels, assurance of timely and convenient technology access, and

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 candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0080,003
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0160,014

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 Opus0,024
Tête enseignante GPT0,230
Écart entre enseignants0,206 · 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

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
Publié2007
Routes d'admission1
Résumé présentoui

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