Ji Hao, Suo Lingling, Chen Hua
Management College, Beijing Union University, Beijing, China.
Front Psychol. 2024 Dec 16;15:1447680. doi: 10.3389/fpsyg.2024.1447680. eCollection 2024.
Blended learning combines the strengths of online and offline teaching and has become a popular approach in higher education. Despite its advantages, maintaining and enhancing students' continuous learning motivation in this mode remains a significant challenge.
This study utilizes questionnaire surveys and structural equation modeling to examine the role of AI performance assessment in influencing students' continuous learning motivation in a blended learning environment.
The results indicate that AI performance assessment positively influences students' continuous learning motivation indirectly through expectation confirmation, perceived usefulness, and learning satisfaction. However, AI performance assessment alone does not have a direct impact on continuous learning motivation.
To address these findings, this study suggests measures to improve the effectiveness of AI performance assessment systems in blended learning. These include providing diverse evaluation metrics, recommending personalized learning paths, offering timely and detailed feedback, fostering teacher-student interactions, improving system quality and usability, and visualizing learning behaviors for better tracking.
混合式学习结合了线上和线下教学的优势，已成为高等教育中一种流行的教学方法。尽管有其优点，但在这种模式下维持和增强学生的持续学习动机仍然是一项重大挑战。
本研究采用问卷调查和结构方程模型，以检验人工智能绩效评估在混合式学习环境中对学生持续学习动机的影响作用。
结果表明，人工智能绩效评估通过期望确认、感知有用性和学习满意度间接对学生的持续学习动机产生积极影响。然而，仅人工智能绩效评估本身对持续学习动机没有直接影响。
为应对这些研究结果，本研究提出了提高人工智能绩效评估系统在混合式学习中有效性的措施。这些措施包括提供多样化的评估指标、推荐个性化学习路径、提供及时详细的反馈、促进师生互动、提高系统质量和可用性，以及可视化学习行为以便更好地跟踪。
