基于文本挖掘的线上课程教学质量评价——以我国大学MOOC为例  

Evaluation of Online Course Teaching Quality Based on Text Mining——A Case Study of Chinese University MOOC

作　　者：陈艳萍[1] 康人堂 Chen Yanping;Kang Rentang(Business School of Hohai University,Nanjing 211100,China)

机构地区：[1]河海大学商学院,南京211100

出　　处：《办公自动化》2024年第23期34-36,共3页Office Informatization

摘　　要：作为“互联网+教育”的典范,大规模开放在线课程(MOOC)在推动教育公平和高等教育数字化转型中扮演至关重要的角色。文章以我国大学MOOC平台为例,爬取大量评论文本,采用LDA主题模型识别潜在主题信息,识别出课程内容、教学风格和课程感受三个主题,大部分用户的体验感较好。随后,对国家精品课程和非国家精品课程的教学质量进行综合评估,非国家精品课程评论数明显较国家精品评论数低,从评价结果看并无明显的区别。文章准确反映线上课程的教学质量,对提升MOOC教学质量至关重要。As a model of "Internet + Education," Massive Open Online Courses(MOOCs) play a crucial role in promoting educational equity and the digital transformation of higher education.This study takes the Chinese University MOOC platform as an example,collecting a large number of review texts.Using the LDA topic model,we identify three key themes:course content,teaching style,and course experience,with the majority of users reporting a positive experience.Subsequently,a comprehensive evaluation of the teaching quality of national-level and non-national-level excellent courses was conducted.The number of reviews for non-national excellent courses is significantly lower than that for national excellent courses,and the evaluation results show no substantial differences.This research accurately reflects the teaching quality of online courses and is essential for enhancing the quality of MOOC instruction.

关 键 词：MOOC LDA主题模型 文本挖掘 教学质量 

分 类 号：G434[文化科学—教育学] G642[文化科学—教育技术学]