Optimizing cognitive load and learning adaptability with adaptive microlearning for in-service personnel

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) s...

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Vydáno v:Scientific reports Ročník 14; číslo 1; s. 25960 - 18
Hlavní autoři: Zhu, Bo, Chau, Kien Tsong, Mokmin, Nur Azlina Mohamed
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 29.10.2024
Nature Publishing Group
Nature Portfolio
Témata:
631/477/2811
639/705/117
639/705/794
Adaptability
Adaptive learning
Algorithms
Cognitive load
Constructivism
Distance learning
Education
Educational objectives
Humanities and Social Sciences
Independent study
Information overload
Instructional design
Knowledge
Learning
Lifelong learning
multidisciplinary
Personnel
Professional development
Science
Science (multidisciplinary)
ISSN:2045-2322, 2045-2322
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Shrnutí: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 AML = 56, N CML = 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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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-77122-1