Training and Instruction Skills Through the Test of Time
Notice bibliographique
Résumé
This study involves e-learning skills via educational software, compared to instruction via educational software with the mediation of an instructor. In the last two decades, the role of the teacher-lecturer has changed, from teaching to guidance and instruction. The technological tools have changed the nature of the learning space and the manner in which the teacher interacts with his students. Educational software is a collection of digital pages, packaged as a learning unit, and is a common tool for delivering self-instruction in academia on a range of issues. This is despite the fact that the effectiveness of this tool in academia has not yet been tested. In addition, the educational software is a technological tool but it is not being updated regularly, therefore the development of the topics in educational software is low. The key motif of technological advancement is to enable constant updates, and therefore, the effectiveness of this learning tool, which has the potential of countering the need for the dynamics of content transfer with its static nature, must be examined. The current study aims to examine the use of this tool in teaching and instruction, and to examine the ways to bridge over this gap of a "static" tool and a "dynamic" learning world. The study focuses on a case study in the Israeli Air Force and integrates instruction with technologies means. We have looked into the skills of e-learning through educational software, as well as the contribution of the instructor to the teaching process. The study's literature reveals that e-learning focuses on the cognitive aspect of learning and on the knowledge of the instruction field. Yet there are studies that engage in reinforcing the in-person communication, meaning, the significance of a “face-to-face” encounter between the student and his instructor. We examined the probability and the extent of the added value of the teacher/instructor in e-learning through educational software. An examination of e-learning through educational software is conducted by a test that consists of questions broken down into levels according to the STEM Model. The findings of the study demonstrate the contribution of educational software as a means of instruction, when it is combined with an in-person encounters between the students and their instructor. We found that combining the in-person meetings with the educational software practice has vastly improved the motivation of the technicians in training, their learning experience and the learner’s ability to understand the learning material.The results of our study shed a spotlight on the instruction, which are a major part of the teaching process in general, as well as the use of educational software as a relevant and applicable mean in the training process in particular. The case study, conducted in the Israeli Air Force, which guides the training processes that are held in the army, is the first case study of its kind, which tracks the use of educational software as a means of instructional work. Our assumption is that training work using educational software has a high influence in the context of teaching and training in different and diverse institutions and organizations, such as in academia.
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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 | 0,003 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,004 |
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 ».