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Updated: Jun 9, 2025

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
Published on: September 27, 2020
Optimizing cognitive load and learning adaptability with adaptive microlearning for in-service personnel
Bo Zhu1, Kien Tsong Chau2, Nur Azlina Mohamed Mokmin2
1Centre for Instructional Technology and Multimedia, Universiti Sains Malaysia, Penang, Malaysia. zhubo0425@student.usm.my.
Abstract:
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 (NAML = 56, NCML = 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.
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