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    推动学习范式革新,加快新质人才培养 ——从预先学习向按需学习转变

    Promoting the Innovation of Learning Paradigms and Accelerating the Cultivation of New Quality Talents

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【作      者】:

王竹立,石晓芬


【关 键 词 】:

新质人才; 新质教育; 预先学习; 按需学习; 学习范式转型


【栏      目】:

理论探讨


【中文摘要】:

发展新质生产力需要新质人才。为了探讨如何培养新质人才、如何早出人才、快出人才这一重大问题,文章首先对预先学习范式与按需学习范式进行了概念辨析和特征分析,认为预先学习存在跟不上知识更新速度、难以适应职业的变化、忽视个体差异、学习效率低与资源浪费等缺点。按需学习则具有以学习者为中心、学习内容灵活多样、学习路径个性化、学习资源利用率高、以实践与应用为导向等优点。当下日新月异的数智技术为按需学习提供了可行性。文章通过对预先学习与按需学习的分析与比较,提出了教育应从预先学习为主向按需学习为主转型的主张与策略,同时列举了按需学习的主要类型,剖析了按需学习的典型案例,阐述了按需学习对发展新质教育、加快新质人才培养的有利之处,并指出了按需学习有可能遇到的挑战、问题及相应的对策。


【英文摘要】:

The development of new quality productivity requires new quality talents. In order to explore how to cultivate new quality talents and how to produce talents early and quickly, this paper firstly analyzes the concepts and characteristics of the anticipatory learning paradigm and the on-demand learning paradigm. It is believed that anticipatory learning has the shortcomings of failing to keep up with the speed of knowledge updating, being difficult to adapt to the changes of occupation, ignoring individual differences, and wasting resources and low learning efficiency. On-demand learning, on the other hand, has the advantages of being learner-centered, flexible and diverse learning content, personalized learning paths, high utilization of learning resources, and being practice- and application-oriented. The current rapidly changing digital intelligence technology provides the feasibility of on-demand learning. By analyzing and comparing anticipatory learning and on-demand learning, this paper proposes the advocacy and strategy that education should be transformed from anticipatory learning to on-demand learning, and at the same time enumerates the main types of on-demand learning, analyzes the typical cases of on-demand learning, explains the benefits of on-demand learning for developing new quality education and accelerating the cultivation of new quality talents, and also points out the challenges, problems, and countermeasures that may be encountered in on-demand learning.

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