Related Experiment Video
Updated: Jun 9, 2025
12:55
Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
Published on: September 27, 2020
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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.
Scientific Reports
|October 30, 2024
Summary
Adaptive microlearning (AML) systems enhance professional development by personalizing content and learning paths. This study found AML significantly reduced cognitive load and improved learning adaptability in in-service personnel.
Area of Science:
- Educational Technology
- Workforce Development
Background:
- Adaptive microlearning (AML) is vital for upskilling in-service personnel.
- Traditional microlearning (CML) lacks personalized learning experiences.
Purpose of the Study:
- To design and develop an innovative adaptive microlearning (AML) system.
- To compare the effectiveness of AML against conventional microlearning (CML).
Main Methods:
- A quasi-experimental study was conducted with 111 in-service personnel.
- Participants were divided into AML (N=56) and CML (N=55) groups.
- Analysis of Covariance (ANCOVA) was used to compare outcomes.
Main Results:
- The AML system significantly reduced unnecessary cognitive load (mean difference = -20.02, p < 0.05).
- The AML system significantly improved learning adaptability (mean difference = 40.72, p < 0.05).
Conclusions:
- Adaptive microlearning systems effectively overcome learning barriers for professionals.
- AML supports lifelong learning and professional development in diverse work environments.
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