| Autor: |
Bognár, László, Ágoston, György, Bacsa-Bán, Anetta, Fauszt, Tibor, Gubán, Gyula, Joós, Antal, Juhász, Levente Zsolt, Kocsó, Edina, Kovács, Endre, Maczó, Edit, Mihálovicsné Kollár, Anita Irén, Strauber, Györgyi |
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| Zdroj: |
Education Sciences; Sep2024, Vol. 14 Issue 9, p974, 21p |
| Abstrakt: |
The primary goal of this research was to empirically identify and validate the factors influencing student engagement in a learning environment where AI-based chat tools, such as ChatGPT or other large language models (LLMs), are intensively integrated into the curriculum and teaching–learning process. Traditional educational theories provide a robust framework for understanding diverse dimensions of student engagement, but the integration of AI-based tools offers new personalized learning experiences, immediate feedback, and resource accessibility that necessitate a contemporary exploration of these foundational concepts. Exploratory Factor Analysis (EFA) was utilized to uncover the underlying factor structure within a large set of variables, and Confirmatory Factor Analysis (CFA) was employed to verify the factor structure identified by EFA. Four new factors have been identified: "Academic Self-Efficacy and Preparedness", "Autonomy and Resource Utilization", "Interest and Engagement", and "Self-Regulation and Goal Setting." Based on these factors, a new engagement measuring scale has been developed to comprehensively assess student engagement in AI-enhanced learning environments. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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