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首页> 外文会议>IEEE International Conference on computer supported cooperative work in design >A Survey of Segmentation, Annotation, and Recommendation Techniques in Micro Learning for Next Generation of OER
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A Survey of Segmentation, Annotation, and Recommendation Techniques in Micro Learning for Next Generation of OER

机译：下一代OER的微观学习中的细分，注释和推荐技术研究

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摘要

With the fast development of Internet technologies and mobile devices, space-time boundaries of people's daily activities become blurred. This makes people utilizing their fragmented time become possible. In recent years, micro learning, which aims to make good use of people's fragmented time and deliver micro-format of open education resource to learners, has drawn wider attention. The generation and delivery of micro learning materials are two essential steps for the micro learning service. This paper first discusses the characteristics of three significant stages of a sophisticated micro learning system: segmentation, annotation, and recommendation, for learning materials. Then various state-of-the-art techniques for different processing stages are reviewed in this survey. Different segmentation and annotation strategies based on different information sources (such as content and users' interaction) are demonstrated and analysed. Soft computing, transfer learning, reinforcement learning, and context-aware techniques are also compared and discussed for solving different difficulties in recommending scenarios. We contribute this paper as the first work focusing on the three-phased techniques in micro learning.
机译：随着Internet技术和移动设备的快速发展，人们日常活动的时空界限变得模糊。这使人们可以利用零散的时间。近年来，旨在充分利用人们零散的时间并向学习者提供开放式教育资源的微观形式的微观学习受到了广泛的关注。微型学习材料的生成和交付是微型学习服务的两个基本步骤。本文首先讨论了复杂的微学习系统的三个重要阶段的特征：学习材料的细分，注释和推荐。然后，在此调查中回顾了用于不同处理阶段的各种最新技术。演示并分​​析了基于不同信息源（例如内容和用户交互）的不同细分和注释策略。还对软计算，迁移学习，强化学习和上下文感知技术进行了比较和讨论，以解决推荐方案中的各种困难。我们将本论文作为关注微学习中的三相技术的第一篇论文进行贡献。

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