MODELO DE ROTAÇÃO DE ESTAÇÕES DE APRENDIZAGEM EM BANDA NO ENSINO SUPERIOR
ALCANÇAR UM EQUILÍBRIO ENTRE O ENSINO EM LINHA E PRESENCIAL
Palavras-chave:
aprendizagem mista, modelo rotativo, universidades, Ucrânia, digitalizaçãoResumo
O objetivo do artigo é testar empiricamente a eficácia do modelo de ensino com estações rotativas num formato misto com base num inquérito realizado entre professores de instituições de ensino superior na Ucrânia. O estudo utiliza uma abordagem de metodologia mista para recolher dados qualitativos e quantitativos de 65 professores durante o primeiro semestre do ano letivo de 2023-2024. Os principais instrumentos são questionários e entrevistas a professores que utilizaram o Modelo de Rotação de Estações. As respostas foram processadas utilizando estatísticas descritivas e análise comparativa para identificar quaisquer diferenças significativas nos resultados. Os resultados sublinham a necessidade de uma seleção cuidadosa de uma plataforma para o ensino à distância e a aquisição de competências na criação de recursos electrónicos. Os resultados do estudo fornecem recomendações práticas para a implementação do modelo no contexto do ensino superior ucraniano. Isto é importante para as instituições de ensino superior ucranianas que procuram métodos óptimos de aprendizagem mista. Os resultados do estudo sublinham a atitude positiva dos professores em relação ao modelo proposto, que promove a individualização da aprendizagem e o desenvolvimento de competências digitais. No entanto, a implementação requer tempo adicional e competências digitais avançadas dos professores. A pontuação média da eficácia do modelo (aproximadamente 4,046) e a distribuição percentual nas pontuações 4 (47,69%) e 5 (30,77%) indicam um elevado nível de aceitação desta abordagem pelos professores modernos. As recomendações baseiam-se na experiência individual dos professores, indicando a variabilidade na escolha do melhor meio de comunicação com os alunos. O estudo contribui para a compreensão da eficácia do modelo de ensino misto e ajudará a otimizar a sua aplicação mais ampla.
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Copyright (c) 2024 Violetta Yukhymenko, Svitlana Borysova, Olena Bazyl, Halyna Hubal, Uliana Barkar
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