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Enregistrement W2056356212 · doi:10.5339/qfarf.2013.sshp-012

Training model to develop the Qatar workforce using emerging learning technologies

2013· article· en· W2056356212 sur OpenAlex

Pourquoi ce travail est dans la base

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affAu moins un auteur déclare une institution canadienne dans l'instantané OpenAlex épinglé.
aboutLe titre ou le résumé porte un signal canadien du lexique géographique.

Notice bibliographique

RevueQatar Foundation Annual Research Forum Volume 2013 Issue 1 · 2013
Typearticle
Langueen
DomaineEngineering
ThématiqueAdvanced Data Processing Techniques
Établissements canadiensAthabasca University
Organismes subventionnairesnon disponible
Mots-clésWorkforceFlexibility (engineering)Presentation (obstetrics)EngineeringEngineering managementKnowledge managementBusinessComputer scienceManagementEconomic growthMedicine

Résumé

récupéré en direct d'OpenAlex

The Qatar National Vision aims at “transforming Qatar into an advanced country by 2030, capable of sustaining its own development and providing for a high standard of living for all of its people for generations to come”. The grand challenge of Human Capacity Development aims to develop sustainable talent for Qatar's knowledge economy in order to meet the needs for a high-quality workforce. In order for Qatar to achieve its 2030 National Vision and become an advanced country by 2030, it has to train its citizens to function in a globalized and competitive world. Important skills for Qatari to function in the 21st century are communication and use of emerging technologies skills. This presentation will propose a training model to develop the Qatar workforce for the 21st century using emerging learning technologies. The training model was based on a mobile learning research project funded by the Qatar Foundation through the Qatar National Research Fund. The project is a collaborative research project with Qatar University, Qatar Petroleum, Qatar Wireless Innovation Centre, and Athabasca University, Canada. The project developed and implemented training lessons on Communication Skills for the oil and gas industry using mobile technology to deliver the training. The workers were employed at Qatar Petroleum and completed the training as part of their professional development to improve their English communication skills. Results from the project showed that workers performance improved after they completed the training and they reported that use of mobile technology to deliver the training provides flexibility for learning on the job. They suggested that the training should be more interactive and game-like. This is important since today's young workers are comfortable using mobile technologies and they need to be motivated to learn using the mobile technologies. The proposed Qatar National Training Model (QNTM) (Figure 1) is based on the mobile learning research project funded by the Qatar Foundation through the Qatar National Research Fund. In the QNTM, the learner/trainee/worker is at the center of the learning since the goal of training is to provide the knowledge and skills to improve workers' performance on the job. The design of the training must follow good learning design principles including preparing the learner for the training, providing activities for the learners to complete to improve their knowledge and skills, allowing learners to practice to improve their performance, certifying learners based on their performance, and providing opportunities for learners to transfer what they learn to the job environment. The delivery of the training should be flexible using a blended approach that includes face-to-face, hands-on, E-learning, mobile learning, and online learning. A variety of learning strategies such as practice with feedback, tutorials, simulations, games, and problem solving can be used depending on the learning outcomes to be achieved. The proposed Qatar National Training Model will allow for learner-centered training, lifelong learning, just-in-time learning, learning in context, developing skills required for 21st century learning, and interaction between learners and between learners and the trainer using social media.

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.

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)0,002
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Méthodes · Signal consensuel: aucune
Score de désaccord entre enseignants0,646
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,000
Communication savante0,0010,003
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,002

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 Opus0,105
Tête enseignante GPT0,390
Écart entre enseignants0,285 · 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