Impact of Convergence of Smart-Technology as Compared to Traditional Methodological Tools on Fostering Cognitive Aspects of Leadership Competencies in the Process of Vocational Training of Students
Notice bibliographique
Résumé
The main objective of this research is to explore how effective and efficient the convergent use of traditional and smart technology tools could be when deployed in fostering leadership competencies of the students in the settings of tertiary vocational education. The experiment involved the students of two universities doing the elective course “Do Better Your Leadership Skills Up”. Having been split up into two halves, the first part of the focus group used the traditional forms of educational process, while the second one additionally used the software like CogniFit, Lumosity, BrainHQ, NeuroNation, Brain Metrix, Eidetic, Fit Brains, BrainExer 2.0. At the entry stage, the pedagogic surveys had been used as well as the cognitive function test to study the cognitive capabilities of the focus group students. We used a multi method approach of combining the close-ended and open-ended questions to get the feedback and the above cognitive test to measure the output of the study. Quantitative methods had been used to analyze the data and such Covariance-based Structural Equation Modeling (SEM) software as SPSS AMOS had been applied to evaluate the results because cognitive function of a person includes sub-components of latent constructs. Textalyzer software had been used to process the students’ responses to open-ended questions of the questionnaire for the most commonly used positive words in the texts, which helped us to identify broad categories of responses. Here, the most commonly used words we had distinguished were “involvement”, “improvement”, “gamification”, “motivation”, “speed”, “concentration”, “memory”, “current studies”, “future job”. Then we distributed the answers by the frequency of the identified words. The responses, which fell under no category, had been analyzed manually. The experimentally obtained data shows that integration of the smart technology into traditional learning environment increases students’ involvement by 23%, personal transformation by 18% and motivation by 17%. Our study proves that the convergent mode of instruction brings more benefits to the students in terms of fostering cognitive aspects of leadership competencies in the process of vocational training than the traditional mode. We found that the converged pedagogical mode enhances the collaboration and involvement of all the stakeholders of educational process. It makes students achieve the greatest personal satisfaction through enhanced self-esteem, efficiency gains, a sense of continuous personal achievement and enhanced autonomy and experimenting with their own learning strategies. We suggest universities (of Ukraine, specifically) to provide training to the teachers with all the latest technology, which seems essential for teaching. Academic institutions (of Ukraine) should also invest into research in the area of the educational-purpose use of smart technology.
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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 | 0,010 | 0,041 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 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 ».