Understanding Automated Feedback in Learning Processes by Mining Local Patterns

Abstract

Process mining, and in particular process discovery, provides useful tools for extracting process models from event-based data. Nevertheless, certain types of processes are too complex and unstructured to be able to be represented with a start-to-end process model. For such cases, instead of extracting a model from a complete event log, it is interesting to zoom in on some parts of the data and explore behavioral patterns on a local level. Recently, local process model mining has been introduced, which is a technique in-between sequential pattern mining and process discovery. Other process mining methods can also used for mining local patterns, if combined with certain data preprocessing. In this paper, we explore discovery of local patterns in the data representing learning processes. We exploit real-life event logs from JMermaid, a Smart Learning Environment for teaching Information System modeling with built-in feedback functionality. We focus on a specific instance of feedback provided in JMermaid, which is a reminder to simulate the model, and locally explore how students react to this feedback. Additionally, we discuss how to tailor local process model mining to a certain case, in order to avoid the computationally expensive task of discovering all available patterns, by combining it with other techniques for dealing with unstructured data, such as trace clustering and window-based data preprocessing.