Modeling the Dynamics of Online Learning Activity
This addresses the challenge of analyzing online learning behaviors for platforms like Stack Overflow, though it appears incremental as it builds on existing clustering and temporal modeling approaches.
The paper tackles the problem of identifying learning patterns from online activity data by introducing the hierarchical Dirichlet Hawkes process (HDHP), a modeling framework that clusters continuous-time grouped streaming data to uncover diverse patterns from detailed traces, scaling to millions of actions by thousands of users and recovering meaningful patterns in content and temporal dynamics on Stack Overflow data.
People are increasingly relying on the Web and social media to find solutions to their problems in a wide range of domains. In this online setting, closely related problems often lead to the same characteristic learning pattern, in which people sharing these problems visit related pieces of information, perform almost identical queries or, more generally, take a series of similar actions. In this paper, we introduce a novel modeling framework for clustering continuous-time grouped streaming data, the hierarchical Dirichlet Hawkes process (HDHP), which allows us to automatically uncover a wide variety of learning patterns from detailed traces of learning activity. Our model allows for efficient inference, scaling to millions of actions taken by thousands of users. Experiments on real data gathered from Stack Overflow reveal that our framework can recover meaningful learning patterns in terms of both content and temporal dynamics, as well as accurately track users' interests and goals over time.