Sentiment evolution with interaction levels in blended learning environments: Using learning analytics and epistemic network analysis

Sentiment evolution is a key component of interactions in blended learning. Although interactions have attracted considerable attention in online learning contexts, there is scant research on examining 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, text 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 contexts. 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. Implications for practice or policy: Learning analytics can be used to identify the sentiments and interactions from discussions. Instructors should guide students to experience slightly negative and confused sentiments for deep interactions at the beginning of blended learning. Social-emotion interactions can alleviate the influence caused by confused sentiments when completing learning activities. Deep interactions can play an important role in improving problem-solving abilities, and when problems are settled, sentiments shift from negative/confused to positive/insightful sentiments.