On Temporal Cluster Analysis for Early Identifying In-trouble Students in an Academic Credit System

Early in-trouble student identification in an academic credit system is a challenging popular task in the educational data mining field. Only the first few semesters of the students can be observed for the task so that the in-trouble students can be recognized soon and have enough time for improving their study performance. The task can be tackled with different machine learning approaches. In this paper, we use the unsupervised learning approach to determine those students with higher effectiveness and no preparation of other labeled data sets. In this approach, a temporal cluster analysis method is proposed in our work based on the temporal clusters returned by dynamic topic models. In addition, we consider temporal characteristics in the study performance of each student to form a pattern from the temporal clusters he/she belongs to over the time. Similar students share similar patterns and therefore, allowing us to determine the pattern types of in-trouble students and recognize them more accurately. In an evaluation study, experimental results show that our method outperforms the other unsupervised and supervised learning methods with higher Recall and F-measure values. It also obtains better temporal clusters with dynamic topic modeling. As a result, our method is suitable for the early in-trouble student identification task.