Problems and Prospects for the Art Education Development in Higher Educational Institutions Based on Big Data Technologies and Digital Platforms
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Notice bibliographique
Résumé
In order to build an effective system of providing educational services in the art direction, it is necessary to possess an assessment of the results of Big Data initial implementation. Previously conducted studies on the specifics of implementing digital platforms in the educational art space are incomplete and insufficient. Most universities of different countries introduce these systems independently, and, therefore, there are no methods and principles for implementing such measures in the educational process. The purpose of the present research is to assess the role of Big Data and digital platforms in improving the quality and efficiency of art education. An analysis of assessing the implementation and functioning of digital systems in the leading higher educational institutions of the art direction in Poland, the Czech Republic, and Ukraine was carried out. This made it possible to generalize the experience gained and identify the main trends of this process. This made it possible to generalize the experience gained and identify the main trends of this process. In particular, 70% of students have a positive attitude towards using digital platforms that allow them to expand their awareness and informativeness, and 73% note these platforms as a source of obtaining available information. This made it possible to generalize the experience gained and identify the main tendencies of this process. As a result, modelling of the concept of using Big Data in higher education in the art direction has been presented. The main methods and examples of using Big Data in art education have been defined and characterized. Prospective directions for further application and implementation of innovative digital systems have been indicated. The use of research results creates opportunities for more flexible expansion of existing digital systems and the formation of new directions for subsequent implementation in the educational process. The research pointed to the most significant problem of comprehensive using Big Data by students due to ignorance and lack of awareness of the potential of Big Data in terms of planning, forecasting behavioral actions both in the process of learning, and in future professional activities. Further development of the research topic should focus on the quantitative and qualitative assessment of existing systems and the formation of a detailed methodology for the introduction of the latest educational services in the art education system.
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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 | 0,001 | 0,000 |
| 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 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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