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    经合组织数字教育生态塑造:策略框架与核心议题

    Shaping Digital Education Ecosystem: OECD's Strategic Framework and

    [浏览次数:63]

【作      者】:

梁 林 梅


【关 键 词 】:

经济合作与发展组织; 数字教育生态; 基础教育数字化转型; 人工智能; 数字鸿沟; 战略规划


【栏      目】:

教育数字化


【中文摘要】:

经济合作与发展组织(OECD)作为全球技术促进教育变革的探路者和引领者,近年来围绕教育数字化转型和数字教育生态系统建设开展了系列研究,形成了数字教育生态塑造的策略框架,包括目标层(迈向促进学习者发展的数字教育生态系统),先决条件层(物理基础设施),核心要素层(数字工具、数字资源、数字素养和数字应用)和保障层(政府主导的多方协同、战略规划的顶层设计、资金投入和公共采购、教师的激励与专业支持、监测和评估);此外,还分析和探讨了数字教育生态塑造的三大核心议题:弥合数字鸿沟、聚焦数字治理和关注人工智能。关照OECD已有经验,基于我国基础教育数字化转型的现实需求,可以从制定国家数字教育战略规划、深化学习/教学和评价场景的数字应用创新及加强人工智能教育大模型的研究和实践探索,推动我国教育数字化转型的可持续发展。


【英文摘要】:

As a pioneer and leader in leveraging technology to drive educational transformation globally, OECD has conducted extensive researches in recent years on digital transformation of education and the construction of digital education ecosystems, and has formed a strategic framework for shaping digital education ecosystems, which consists of a goal layer (advancing towards a digital education ecosystem that fosters learner development), a prerequisite layer (physical infrastructure), a core element layer (digital tools, digital resources, digital literacy and digital applications), and a guarantee layer (government-led multi-stakeholder collaboration, top-level strategic planning, financial investment and public procurement, teacher incentives and professional support, monitoring and evaluation). Additionally, three core issues for shaping the digital education ecosystem are analyzed and discussed: bridging the digital divide, focusing on digital governance and paying attention to artificial intelligence. Drawing on OECD's experience and considering the practical needs of the digital transformation of basic education in China, the sustainable advancement of digital transformation of education in China can be promoted through formulating a national strategic plan for digital education, deepening digital application innovation in learning/teaching and evaluation scenarios, and enhancing the research and practical exploration of AI educational large models.

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