优化认知负载和学习适应性,通过适应性微学习为现役人员优化认知负载和学习适应性
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
概括
适应式微型学习 (AML) 系统通过个性化内容和学习路径来增强专业发展. 这项研究发现,AML显著降低了在职人员的认知负载,并提高了学习适应能力.
科学领域:
- 教育技术的教育技术
- 劳动力发展 劳动力发展
背景情况:
- 适应式微型学习 (AML) 对于服务人员升级技能至关重要.
- 传统的微型学习 (CML) 缺乏个性化的学习体验.
研究的目的:
- 设计和开发一个创新的自适应式微型学习 (AML) 系统.
- 为了比较AML与传统微型学习 (CML) 的有效性.
主要方法:
- 对111名现役人员进行了一项准实验性研究.
- 参与者被分为AML (N=56) 和CML (N=55) 组.
- 协差分析 (ANCOVA) 用于比较结果.
主要成果:
- 在AML系统显著减少不必要的认知负载 (平均差异=-20.02,p < 0.05).
- 洗钱系统显著提高了学习适应能力 (平均差异 = 40.72,p < 0.05).
结论:
- 适应式微型学习系统有效地克服了专业人员的学习障碍.
- 资金洗钱支持在各种工作环境中终身学习和专业发展.
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