Identifying Students' Characteristic Learning Behaviors in an Intelligent Tutoring System Fostering Self-Regulated Learning

Identification of student learning behaviors, especially those that characterize or distinguish students, can yield important insights for the design of adaptation and feedback mechanisms in Intelligent Tutoring Systems (ITS). In this paper, we analyze trace data to identify distinguishing patterns of behavior in a study of 51 college students learning about a complex science topic with an agent-based ITS that fosters self-regulated learning (SRL). Preliminary analysis with an Expectation-Maximization clustering algorithm revealed the existence of three distinct groups of students, distinguished by their test and quiz scores (low for the first group, medium for the second group, and high for the third group), their learning gains (low, medium, high), the frequency of their note-taking (rare, frequent, rare) and note-checking (rare, rare, frequent), the proportion of sub-goals attempted (low, low, high), and the time spent reading (high, high, low). In this paper, we extend this analysis to identify characteristic learning behaviors and strategies that distinguish these three groups of students. We employ a differential sequence mining technique to identify differentially frequent activity patterns between the student groups and interpret these patterns in terms of relevant learning behaviors. The results of this analysis reveal that high-performing students tend to be better at quickly identifying the relevance of a page to their subgoal, are more methodical in their exploration of the pedagogical content, rely on system prompts to take notes and summarize, and are more strategic in their preparation for the post-test (e.g., using the end of their session to briefly review pages). These results provide a first step in identifying the group to which a student belongs during the learning session, thus making possible a real-time adaptation of the system.