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Integrating OER in the design of educational material: Blended learning and linked-open-educational-resources-data approach

机译:将OER整合到教材设计中:混合学习和链接开放式教育资源数据方法

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Teaching and Learning takes place in (or in a combination of) different educational environments: classroom, online, or mobile. Blended learning, or hybrid learning, is a formal educational program that integrates face-to-face learning with technology-based, digital instruction. On the other hand, Open Educational Resources (OER) provides a strategic opportunity to improve the quality of education as well as facilitate policy dialog, knowledge sharing, and capacity building. OERs are actual resources/tools that can help enrich any classroom environment and push student thinking and comprehension. One of the fundamental concepts of OER is "the ability to freely adapt and reuse existing pieces of knowledge", and therefore be a way to create more economic and personalized learning. The OER movement has challenged the traditional value chain by employing new methods to deliver high-quality educational content. The purpose of this work is to show a way to enhance the face-to-face classrooms with integration of OERs, and thus create blended learning instruction. Reuse of OERs by both individuals and organizations may have significant creative and economic benefit for learning environments. The approach is based on Linked Data for describe and publish OER. In this new paradigm for educational content consumption and integration, OER are expected to play a decisive and productive role for blended learning. The approach presented can be used to support different blended-learning models.
机译:教与学是在不同的教育环境中(或结合在一起)进行的:教室,在线或移动。混合学习或混合学习是一种正式的教育计划,将面对面学习与基于技术的数字教学相结合。另一方面,开放式教育资源(OER)为提高教育质量以及促进政策对话,知识共享和能力建设提供了战略机遇。 OER是实际的资源/工具,可以帮助丰富任何教室环境并推动学生的思考和理解。 OER的基本概念之一是“自由适应和重用现有知识的能力”,因此是创建更经济和个性化学习的一种方式。 OER运动采用新方法交付高质量的教育内容,从而挑战了传统的价值链。这项工作的目的是展示一种通过整合OER来增强面对面教室,从而创建混合学习指导的方法。个人和组织都可以重复使用OER,这对于学习环境可能具有重大的创造和经济利益。该方法基于用于描述和发布OER的链接数据。在这种教育内容消费和整合的新范式中,OER有望在混合学习中发挥决定性和富有成效的作用。提出的方法可用于支持不同的混合学习模型。

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