[1]张 瑾,张夷楠,叶海智,等.教师在线学习社区中会话主题发现及演化分析[J].远程教育杂志,2021,(02):085-94.[doi:10.15881/j.cnki.cn33-1304/g4.2021.02.009]
 Zhang Jin,Zhang Yinan,Ye Haizhi,et al.Discovery of Conversational Topics and Analysis of Evolution in Teachers’ Online Learning Community[J].Distance Education Journal,2021,(02):085-94.[doi:10.15881/j.cnki.cn33-1304/g4.2021.02.009]
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教师在线学习社区中会话主题发现及演化分析()
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《远程教育杂志》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2021年02期
页码:
085-94
栏目:
专题研究
出版日期:
2021-03-20

文章信息/Info

Title:
Discovery of Conversational Topics and Analysis of Evolution in Teachers’ Online Learning Community
作者:
张 瑾; 张夷楠; 叶海智; 朱 珂; 张 思
1.河南师范大学 教育学部,河南新乡 453007;2.华中师范大学 教育信息技术学院,湖北武汉 430079
Author(s):
Zhang Jin; Zhang Yinan; Ye Haizhi; Zhu Ke; Zhang Si
1.Faculty of Education , Henan Normal University , Xinxiang Henan 453007;2. Central China Normal University , Wuhan Hubei , 430079
关键词:
教师网络研修MOOCWord2VecK-means在线讨论主题模型
Keywords:
Teacher’s Online Training MOOC Word2Vec K-Means Topic Model Online Discussion
分类号:
G420
DOI:
10.15881/j.cnki.cn33-1304/g4.2021.02.009
文献标志码:
A
摘要:
教师在参与网络研修的过程中产生的交互式文本数据,成为分析教师学习状态、学习关注点、自身不足的重要依据。这些数据具有篇幅短小、价值呈隐性、价值密度低的特点,经典的LDA主题模型更适用于篇章级文本的主题信息抽取,应用于短文本主题抽取时生成的主题信息并不准确。首先,利用爬虫技术在教师在线学习社区中收集话题帖子作为实验数据;其次,使用Word2Vec词向量技术对教师在线学习社区中的数据帖子进行词向量建模,并采用K-means聚类方法将词向量进行聚类,实现了隐含主题信息抽取,并根据不同主题表达内容的分离程度将主题归纳为六类;最后,通过设计会话主题演化的可视化方法,探索教师在参与会话讨论时会话主题的演化规律。采用Word2Vec+K-means方法抽取教师在线学习社区中会话帖子的主题信息,能够为社区管理者预测和干预社区教师学习状态提供借鉴和参考。
Abstract:
The interactive text data generated by teachers in the process of participating in online training has become an important basis for analyzing teachers’ learning status, learning focus, and their own shortcomings. These data have the characteristics of short size, hidden value, and low value density. The classic LDA topic model is more suitable for chapter-level topic extraction, but it is difficult to accurately extract topic from short texts. Firstly, the crawler technology is used to collect topic posts in the teachers’ online learning community as experimental data. Secondly, Word2Vec technology was used to model the word-vector of the data posts in the teachers’ online learning community, and k-means clustering method was adopted to cluster the vectors, realizing the extraction of the implied topic information, and the topics were classified into six categories according to the degree of separation of the content expressed by different topics. Finally, by designing a visualization method of the evolution of conversation, the rules of evolution of topics are explored when teachers participate in conversation discussions. This research explores the feasibility of Word2Vec combined with K-Means method to extract the topic information of posts in teachers’ online learning communities, and provides data support for community managers to improve the learning community by analyzing topic posts in teachers’ online learning communities.

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备注/Memo

备注/Memo:
基金项目:本文系全国教育科学规划国家一般课题“人工智能助推教师专业发展的机制与策略研究”(项目编号:BGA190050);教育部人文社会科学研究规划基金项目“网络学习空间中学习共同体组织策略与优化机制研究”(项目编号:20YJAZH128);河南省社科规划项目“‘互联网+’视阈下促进教育资源区域性均衡发展的理论与实证研究”(项目编号:2018BJY015)的研究成果。
更新日期/Last Update: 1900-01-01