期刊名称：Australasian Journal of Educational Technology
印刷版ISSN：1449-3098
电子版ISSN：1449-5554
出版年度：2021
卷号：37
期号：2
页码：81-95
DOI：10.14742/ajet.6749
语种：English
出版社：Australasian Society for Computers in Learning in Tertiary Education
摘要：Sentiment evolution is a key component of interactions in blended learning. Although interactions have attracted considerable attention in online learning conte10.14742/ajet.ts, there is scant research on e10.14742/ajet.amining sentiment evolution over different interactions in blended learning environments. Thus, in this study, sentiment evolution at different interaction levels was investigated from the longitudinal data of five learning stages of 38 postgraduate students in a blended learning course. Specifically, te10.14742/ajet.t mining techniques were employed to mine the sentiments in different interactions, and then epistemic network analysis (ENA) was used to uncover sentiment changes in the five learning stages of blended learning. The findings suggested that negative sentiments were moderately associated with several other sentiments such as joking, confused, and neutral sentiments in blended learning conte10.14742/ajet.ts. Particularly in relation to deep interactions, student sentiments might change from negative to insightful ones. In contrast, the sentiment network built from social-emotion interactions shows stronger connections in joking-positive and joking-negative sentiments than the other two interaction levels. Most notably, the changes of co-occurrence sentiment reveal the three periods in a blended learning process, namely initial, collision and sublimation, and stable periods. The results in this study revealed that students’ sentiments evolved from positive to confused/negative to insightful.
