{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/modeling-the-dynamics-of-online-learning","title":"Modeling the Dynamics of Online Learning Activity","arxiv_id":"1610.05775","date":"2016-10-18","proceeding":null,"authors":["Charalampos Mavroforakis","Isabel Valera","Manuel Gomez Rodriguez"],"abstract":"People are increasingly relying on the Web and social media to find solutions\nto their problems in a wide range of domains. In this online setting, closely\nrelated problems often lead to the same characteristic learning pattern, in\nwhich people sharing these problems visit related pieces of information,\nperform almost identical queries or, more generally, take a series of similar\nactions. In this paper, we introduce a novel modeling framework for clustering\ncontinuous-time grouped streaming data, the hierarchical Dirichlet Hawkes\nprocess (HDHP), which allows us to automatically uncover a wide variety of\nlearning patterns from detailed traces of learning activity. Our model allows\nfor efficient inference, scaling to millions of actions taken by thousands of\nusers. Experiments on real data gathered from Stack Overflow reveal that our\nframework can recover meaningful learning patterns in terms of both content and\ntemporal dynamics, as well as accurately track users' interests and goals over\ntime.","url_abs":"http://arxiv.org/abs/1610.05775v1","url_pdf":"http://arxiv.org/pdf/1610.05775v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"modeling-the-dynamics-of-online-learning","repo_url":"https://github.com/Networks-Learning/hdhp.py","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}