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Topic tracking model for analyzing student-generated posts in SPOC discussion forums
International Journal of Educational Technology in Higher Education ( IF 7.611 ) Pub Date : 2020-09-02 , DOI: 10.1186/s41239-020-00211-4
Xian Peng , Chengyang Han , Fan Ouyang , Zhi Liu

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.

中文翻译:

用于分析 SPOC 论坛中学生生成的帖子的主题跟踪模型

由于在小型私人在线课程 (SPOC) 中存在大量学生生成的论坛帖子,学生和教师发现有效导航和跟踪有价值的信息(例如主题的演变、情绪和行为变化)既耗时又具有挑战性。话题的关系。为了解决这个问题,本研究使用改进的动态主题模型时间信息-情绪行为模型 (TI-EBTM) 分析了大量讨论帖子。时间、情感和行为特征被纳入主题建模过程,这允许在 SPOC 讨论论坛中自动跟踪和理解时间主题变化的概述。对 30 门 SPOC 课程数据的实验表明,TI-EBTM 优于其他动态主题模型,并且可以有效地随着时间的推移提取突出主题。此外,我们进行了深入的时间主题分析,以调查 TI-EBTM 在案例研究中的效用。案例研究的结果表明,我们的方法论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),并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末发起对话来表达对有关数据查询方法的话题的更多负面情绪。分析结果可以为教师提供宝贵的课程论坛发展见解,使他们能够微调课程论坛以满足学生的需求,这将有助于加强讨论互动和学生的学习体验。我们进行了深入的时间主题分析,以调查 TI-EBTM 在案例研究中的效用。案例研究的结果表明,我们的方法论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),并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末发起对话来表达对有关数据查询方法的话题的更多负面情绪。分析结果可以为教师提供宝贵的课程论坛发展见解,使他们能够微调课程论坛以满足学生的需求,这将有助于加强讨论互动和学生的学习体验。我们进行了深入的时间主题分析,以调查 TI-EBTM 在案例研究中的效用。案例研究的结果表明,我们的方法论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),并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末发起对话来表达对有关数据查询方法的话题的更多负面情绪。分析结果可为教师提供对课程论坛发展的宝贵见解,并使他们能够微调课程论坛以满足学生的需求,这将有助于加强讨论互动和学生的学习体验。
更新日期:2020-09-02
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