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    什么样的在线课程受学习者欢迎? ——基于15门在线课程评论的文本挖掘与组态分析

    What Kind of Online Courses are Popular with Learners?-Based on Text Mining and Configuration Analysis of 15 Online Course Reviews

    浏览次数:20194

【作      者】:

肖 婉, 卢 锋, 季一木


【关 键 词 】:

高质量; 在线课程; 在线教育; 慕课; 学习者


【栏      目】:

网络教育


【中文摘要】:

在教育信息化深入发展和教育数字化转型持续推进的背景下,大力推动高质量在线开放课程建、用、学,是实现我国教育质量提升和教育强国建设的重要途径。而学习者对在线课程的体验和评价能反映用户视角下在线课程的内在质量,从而反向赋能在线课程的优化设计和持续发展。文章通过对15门在线课程评论的文本挖掘和组态分析,探索了在线课程受欢迎的主要因素,揭示了专业基础课程和素养提升课程受欢迎的具体机制——“内容导向—成效为王”及“魅力吸引—体验至上”。文章提出了在线开放课程建设及应用的具体策略:明确内在目标,针对专业教育与素养培养有的放矢;提升核心引力,促进教师讲授与知识内容联通对话;优化技术工具,赋能基础适配与资源支持双向驱动;完善建设体系,实现成效为王与体验至上协同发展。


【英文摘要】:

In the context of the in-depth development of educational informationization and the continuous promotion of the digital transformation of education, vigorously promoting the construction, use and learning of high-quality of massive open online courses is an important way to realize the improvement of education quality and the construction of an educational power in China. Learners' experience and evaluation of online courses can reflect the intrinsic quality of online courses from the perspective of users, thus empowering the optimal design and sustainable development of online courses. Through text mining and configuration analysis of 15 online course reviews, this study explores the main factors for the popularity of online courses, and reveals the mechanisms of two types of popular online courses, which emphasize "content and efficiency", "charm and experience". This paper proposes specific strategies for constructing and implementing online courses from four aspects: clarifying the intrinsic goals, targeting professional education and literacy cultivation, enhancing the core attraction, promoting the dialogue between teachers' lectures and knowledge content linkage, optimizing technical tools, empowering the two-way drive of basic adaptation and resource support, and improving the construction system, realizing the synergistic development of efficiency and experience.


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什么样的在线课程受学习者欢迎? ——基于15门在线课程评论的文本挖掘与组态分析

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