Re-Evaluating Components of Classical Educational Theories in AI-Enhanced Learning: An Empirical Study on Student Engagement

被引：0

作者：

Bognar, Laszlo ^{[1
]}

Agoston, Gyorgy ^{[1
]}

Bacsa-Ban, Anetta ^{[2
]}

Fauszt, Tibor ^{[3
]}

Guban, Gyula ^{[2
]}

Joos, Antal ^{[1
]}

Juhasz, Levente Zsolt ^{[2
]}

Kocso, Edina ^{[2
]}

Kovacs, Endre ^{[3
]}

Maczo, Edit ^{[2
]}

Kollar, Anita Iren Mihalovicsne ^{[1
]}

Strauber, Gyorgyi ^{[1
]}

机构：

[1] Univ Dunaujvaros, Inst Informat Technol, Tancsics M St 1-a, H-2400 Dunaujvaros, Hungary

[2] Univ Dunaujvaros, Teacher Training Ctr, Tancsics M St 1-a, H-2400 Dunaujvaros, Hungary

[3] Budapest Business Univ, Dept Business Informat Technol, Buzogany u 10-12, H-1149 Budapest, Hungary

来源：

EDUCATION SCIENCES | 2024年 / 14卷 / 09期

关键词：

higher education; university students; explanatory factor analysis; confirmatory factor analysis; structural equation modeling; EFA; CFA; SEM; AI-enhanced learning; student engagement; self-efficacy; autonomy in learning; self-regulation; PERCEIVED SELF-EFFICACY; MOTIVATION; BELIEFS;

D O I：

10.3390/educsci14090974

中图分类号：

G40 [教育学];

学科分类号：

040101 ; 120403 ;

摘要：

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.

引用

页数：21