Influencing Factors For Learners’ Willingness To Give Negative Comments Based On Social Network

Proceedings of the 4th Africa-Asia Dialogue Network (AADN) International Conference on Advances in Business Management and Electronic Commerce Research Pub Date : 2022-11-25 DOI:10.1145/3572996.3572999

Wang Rui, Ling Hai-Li, Xiao Yang-Cai

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Abstract

Extracting the correlations between online course quality elements and measurement elements from learners’ negative comments are important to effectively guide the quality iteration and learners’ satisfaction improvement of online courses. This study uses web crawler technology to collect learning comments about programming courses on Chinese university MOOC platforms, calculates sentiment polarity of online comments based on natural language processing technology of Baidu AI open platform, uses LDA topic model to extract quality factors from negative comment texts, and subsequently introduces social network analysis methods to construct thematic social networks. The results show that the nodes of "course explanation" and "knowledge difficulty" have high degree centrality, proximity centrality and intermediary centrality; The performance of the other elements in the central indicators is different.

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基于社交网络的学习者负面评价意愿的影响因素

从学习者的差评中提取在线课程质量要素与测量要素之间的相关性，对于有效指导在线课程质量迭代和学习者满意度提升具有重要意义。本研究利用网络爬虫技术收集中国高校MOOC平台编程课程的学习评论，基于百度AI开放平台的自然语言处理技术计算在线评论的情感极性，利用LDA主题模型从负面评论文本中提取品质因素，随后引入社交网络分析方法构建主题社交网络。结果表明:“课程解释”和“知识难度”节点具有较高的中心性、邻近中心性和中介中心性;中心指标中其他要素的表现不同。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the 4th Africa-Asia Dialogue Network (AADN) International Conference on Advances in Business Management and Electronic Commerce Research

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