计算机科学
学习分析
大型网络公开课
分析
情绪分析
2019年冠状病毒病(COVID-19)
课程(导航)
在线课程
万维网
在线讨论
在线学习
数学教育
多媒体
数据科学
心理学
工程类
人工智能
病理
航空航天工程
传染病(医学专业)
医学
疾病
作者
Tehmina Amjad,Zainab Shaheen,Ali Daud
标识
DOI:10.1109/tcss.2022.3174640
摘要
The use of Massive Online Open Courses (MOOCs) has been noticeably increased in recent times, especially after the COVID-19 pandemic. In the absence of one-to-one interaction with the students, the instructors are no longer able to understand the demands of their students in an intrinsic way. To overcome this problem, the MOOC platforms provide a discussion forum in which students can share their thoughts and problems about the course. The instructors must closely monitor the performance of their students so that they can improve their teaching methodology to enhance the students' understanding. The instructors must go through the long chats in the discussion forums to identify specific problem areas faced by students. In this study, we propose a method that first categorizes discussion threads into topics and subtopics with the help of topic modeling and then performs sentiment analysis on comments to identify the sentiment of the posts. The primary objective of the study is to facilitate the instructors so that they can improve their teaching methodology, thus enhancing the understanding level of the students.
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