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Enregistrement W4385875820 · doi:10.5539/jel.v12n5p208

Development in Designing Competency-Based Learning Management According to the Guidelines for Driving the Economy (BCG Model) by Using the Concept of Proactive Learning Management for Students Practice Teaching Professional Experience

2023· article· en· W4385875820 sur OpenAlexvenueno aff
Kamolchart Klomim, Boonsong Kuayngern

Notice bibliographique

RevueJournal of Education and Learning · 2023
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueVocational and Entrepreneurial Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésInternshipCurriculumPsychologyAttendanceKnowledge managementExperiential learningSocial learningMathematics educationActive learning (machine learning)Professional developmentMedical educationPedagogyComputer scienceArtificial intelligenceMedicine

Résumé

récupéré en direct d'OpenAlex

This research piece has the following goals: (1) to create a curriculum for creating a competency-based learning management system based on the economically motivated approach (BCG Model) by utilizing the proactive learning management concept for students practice teaching professional experience, (2) to assess the success of the curriculum in creating competency-based learning management utilizing the idea of proactive learning management for student practice teaching professional experience in accordance with the economic driving model (BCG Model), namely; (2.1) to compare the understanding of developing compe-tency-based learning management in accordance with the economic driving model (BCG Model) utilizing the idea of proactive learning management before and after learning, and (2.2) to assess the capacity to create a competency-based learning man-agement plan utilizing the proactive learning management concept in accordance with the economy-driven approach (BCG Model) compared to the criteria of 80%. In the second semester of the academic year 2022, there are 50 first-year teaching professional internship students in attendance. In the second semester of the academic year 2022, a total of 30 first-year teacher training students made up the sample. It makes use of a straightforward random sampling technique and a research and development (R&D) paradigm, which is typical in behavioral and social science research. The following resources were used in the study: (1) a test of knowledge on competency-based learning management system design, and (2) an operational capacity assessment form for creating a competency-based learning management strategy. Using proactive learning management concepts and knowledge comparison in the design of learning management, manage pre-learn and post-learn competency-based learning, using the t-test for dependent, data analysis is used to evaluate the suitability and effectiveness of the curriculum in the design of competency-based learning management in accordance with the economic-driven approach (BCG Model). Then use the t-test for one sample to assess the capacity to create a learning management plan based on post-learning competency versus the threshold of 80%. The results of the study showed that; (1) the development of a curriculum in the design of competency-based learning management according to the economic-driven approach (BCG Model) using the concept of proactive learning management for students practice teaching professional experience, the innovation of competency-based learning management combined with work to develop competence for students, practice, teaching, and professional experience was successful at 81.74/82.19, the mean was 4.60, the standard deviation was 0.50, and the level at which it was most suited was 4.60, and (2) use the idea of proactive learning management for students’ practice teaching professional experience to evaluate the efficiency of the curriculum in de-veloping competency-based learning management in accordance with the economic driving model (BCG Model), it was found that (2.1) students practice teaching professional experience knowledge in designing a competency-based learning management after learning was significantly higher than before learning at the .01 level, and (2.2) students practice teaching professional experience had the ability to prepare a learning management plan based on post-learning competency higher than the criteria of 80 percent at the statistical significance level of .01.

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.

Comment cette classification a été obtenuedéplier

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)0,006
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,578
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0060,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0020,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,091
Tête enseignante GPT0,467
Écart entre enseignants0,376 · 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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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