A Comparison of Different Communication Tools for Distance Learning in Nuclear Education
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Notice bibliographique
Résumé
Nuclear Engineering Education has seen a recent surge in activity in the past 10 years in Canada due in part to a Nuclear Renaissance. The Nuclear Industry workforce is also aging significantly and requires a significant turnover of staff due to the expected retirements in the next few years. The end result is that more students need to be prepared for work in all aspects of the Nuclear Industry. The traditional training model used for nuclear engineering education has been an option in an existing undergraduate program such as Chemical Engineering, Engineering Physics, or Mechanical Engineering with advanced training in graduate school. The education model was mostly lecture style with a small number of experimental laboratories due to the small number of research reactors that could be used for experimentation. While the traditional education model has worked well in the past, there are significantly more advanced technologies available today that can be used to enhance learning in the classroom. Most of the advancement in nuclear education learning has been through the use of computers and simulation related tasks. These have included use of industry codes, or simpler tools for analysis of the complex models used in the Nuclear Industry. While effective, these tools address the analytical portion of the program and do not address many of the other skills needed for nuclear engineers. In this work, a set of tools are examined that can be used to augment or replace the traditional lecture method. These tools are Mediasite, Adobe Connect, Elluminate, and Camtasia. All four tools have recording capabilities that allow the students to experience the exchange of information in different ways. The students now have more options in how they obtain and share information. Students can receive information in class, review it later at home or while in transit, or view/participate it live at a remote location. These different options allow for more flexibility in delivery of material. The purpose of this paper is to compare recent experiences with each of these tools in providing Nuclear Engineering Education and to determine the various constraints and impacts on delivery.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
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 découle