Modeling the Dynamics of Online Learning Activity

Autor:	Mavroforakis, Charalampos, Valera, Isabel, Rodriguez, Manuel Gomez
Rok vydání:	2016
Předmět:	Statistics - Machine Learning Computer Science - Learning Computer Science - Social and Information Networks
Druh dokumentu:	Working Paper
Popis:	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. Comment: Python implementation of the proposed HDHP is available at https://github.com/Networks-Learning/hdhp.py
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1610.05775 Zobrazit plný text záznamu View this record from Arxiv