A Comparative Study of Comments in Discussion Forums of LMOOC from the Perspective of Big Data:Sentiment Analysis and Content Analysis
This study employed sentiment analysis and content analysis to analyze students'comments posted in discussion forums of two types of Language Massive Online Open Courses(LMOOCs)(30 quality LMOOCs and 30 regular LMOOCs),and compared students'sentiment,course rating,and the distribution of the themes summarized from the comments.The results show that the sentiment values of student comments and the course rating of quality LMOOCs are both significantly higher than those of regular LMOOCs.Five major themes are identified from the student comments in the discussion forums of LMOOCs:attitudes towards LMOOCs,reviews on LMOOCs,assessment of LMOOC instruction and instructors,learning outcomes,and suggestions.Students'positive attitudes towards quality LMOOCs and positive assessment of instruction and instructors in these LMOOCSs are higher than in regular LMOOCs.However,there is no significant difference in the distribution of students'reviews on LMOOCs and learning outcomes between the two types of LMOOCs.These findings underscore the important role of teacher-student interaction,bilingual subtitles,and instructors'oral proficiency in LMOOC pedagogical design.
LMOOCsstudents'commentssentiment analysiscontent analysiscomparative study
NETL
NSTL
万方数据
