User clustering algorithms have been introduced to analyze users' learning behaviors and help to provide personalized learning guides in traditional Web-based learning systems. However, the explicit and implicit coupled interactions, which means the correlations between user attributes generated from learning actions, are not considered in these algorithms. Much significant and useful information which can positively affect clustering accuracy is neglected. To solve the above issue, we proposed a coupled user clustering algorithm for Wed-based learning systems. It respectively takes into account intra-coupled and inter-coupled relationships of learning data, and utilizes Taylor-like expansion to represent their integrated coupling correlations. The experiment result demonstrates the outperformance of the algorithm in terms of efficiently capturing correlations of learning data and improving clustering accuracy. [For the full proceedings, see ED592609.]