Zhu Bo, Chau Kien Tsong, Mokmin Nur Azlina Mohamed
Centre for Instructional Technology and Multimedia, Universiti Sains Malaysia, Penang, Malaysia.
Sci Rep. 2024 Oct 29;14(1):25960. doi: 10.1038/s41598-024-77122-1.
Adaptive microlearning has emerged as a crucial approach for enhancing the working skills of in-service personnel. This study introduces the design and development of an innovative adaptive microlearning (AML) system and investigates its effectiveness compared to a conventional microlearning (CML) system. The main distinguishing feature of an AML system from a CML system is its adaptive features that tailor the learning experience to individual needs, including personalized content delivery, real-time feedback, and adaptive learning paths. A quasi-experimental study involving 111 in-service personnel (N = 56, N = 55) was conducted. ANCOVA results confirmed that the AML system significantly reduced unnecessary cognitive load due to inappropriate instructional design (mean difference of -20.02, p < 0.05) and significantly improved learning adaptability (mean difference of 40.72, p < 0.05). These findings highlight the potential of adaptive microlearning systems to overcome barriers to effective learning, thereby supporting lifelong learning and professional development in various working contexts.
适应性微学习已成为提高在职人员工作技能的关键方法。本研究介绍了一种创新的适应性微学习（AML）系统的设计与开发，并将其与传统微学习（CML）系统相比，研究其有效性。AML系统与CML系统的主要区别特征在于其适应性特征，即根据个人需求定制学习体验，包括个性化内容交付、实时反馈和适应性学习路径。进行了一项涉及111名在职人员（N = 56，N = 55）的准实验研究。协方差分析结果证实，AML系统显著降低了因教学设计不当而产生的不必要认知负荷（平均差异为-20.02，p < 0.05），并显著提高了学习适应性（平均差异为40.72，p < 0.05）。这些发现凸显了适应性微学习系统克服有效学习障碍的潜力，从而在各种工作环境中支持终身学习和职业发展。
