Topic tracking model for analyzing student-generated posts in SPOC discussion forums
Topic tracking model for analyzing student-generated posts in SPOC discussion forums
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DOI：
10.1186/s41239-020-00211-4
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发表时间：
2020-09-02
影响因子：
8.6
通讯作者：
Liu, Zhi
中科院分区：
文献类型：
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作者：
Peng, Xian;Han, Chengyang;Liu, Zhi
由于在小型私人在线课程（spoc）中有大量学生生成的论坛帖子，学生和教师发现有效地导航和跟踪有价值的信息既耗时又具有挑战性，例如主题的演变，与主题相关的情绪和行为变化。为了解决这一问题，本研究使用改进的动态话题模型——时间信息-情绪行为模型（TI-EBTM）分析了大量的讨论帖子。将时间、情绪和行为特征纳入主题建模过程，从而可以对SPOC论坛中时间主题变化的自动跟踪和理解进行概述。基于30个SPOC课程数据的实验表明，TI-EBTM模型优于其他动态主题模型，能够有效地提取出随时间变化的突出主题。此外，我们进行了深入的时间主题分析，以一个案例研究来调查TI-EBTM的效用。案例研究的结果表明，我们的方法和分析揭示了学生的时间焦点（即话题强度和话题内容的变化），反映了话题的情绪和行为倾向的演变。例如，学生倾向于通过在学期结束时发起对话来表达更多关于数据查询方法的负面情绪。分析结果可以为课程论坛的发展提供有价值的见解，使教师能够根据学生的需求对课程论坛进行微调，从而有助于加强讨论互动，提高学生的学习体验。
Due to an overwhelming amount of student-generated forum posts in small private online courses (SPOCs), students and instructors find it time-consuming and challenging to effectively navigate and track valuable information, such as the evolution of topics, emotional and behavioral changes in relation to topics. For solving this problem, this study analyzed plenty of discussion posts using an improved dynamic topic model, Time Information-Emotion Behavior Model (TI-EBTM). Time, emotion, and behavior characteristics were incorporated into the topic modeling process, which allowed for an overview of automatic tracking and understanding of temporal topic changes in SPOC discussion forums. The experiment on data from 30 SPOC courses showed that TI-EBTM outperformed other dynamic topic models and was effective in extracting prominent topics over time. Furthermore, we conducted an in-depth temporal topic analysis to investigate the utility of TI-EBTM in a case study. The results of the case study demonstrated that our methodology and analysis shed light on students' temporal focuses (i.e., the changes of topic intensity and topic content) and reflected the evolution of topics' emotional and behavioral tendencies. For example, students tended to express more negative emotions toward the topic about the method of data query by initiating the conversation at the end of the semester. The analytical results can provide instructors with valuable insights into the development of course forums and enable them to fine-tune course forums to suit students' requirements, which will subsequently be helpful in enhancing discussion interaction and students' learning experience.