Textual data, as a key carrier of learning feedback, is continuously
produced by many students within course forums. The temporal nature of discussion requires students’ emotions and concerned aspects (e.g. teaching styles, learning activities, etc.) to be dynamically tracked for understanding learning requirements. To characterize dynamics of emotion-aspects, this paper presents an unsupervised ... [Show full abstract] model, namely temporal emotion-aspect model (TEAM), modeling time jointly with emotions and aspects to capture emotion-aspect evolutions over time. Especially, the primary output of TEAM is two-fold: emotion-specific aspect probabilistic distributions and their evolutions over time. With the discussion data from two online course forums, we validated the performance of TEAM and utilized this model to discover most concerned emotion-aspects as well as their evolutionary trends for the whole learning group and different achievement levels of groups, respectively. The results indicated that, (1) content-related aspects were the main focus with higher probabilities to negative and confused emotions; (2) there were higher probabilities of emotional expressions in the initial and final stages of a semester; (3) compared with high- and medium-achieving students, low-achieving students were less active in emotional engagement on the whole, and tended to express more confusion in the final stage of a semester.