{"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/mathematics-content-understanding-for","title":"Mathematics Content Understanding for Cyberlearning via Formula Evolution Map","arxiv_id":"1812.11786","date":"2018-12-31","proceeding":null,"authors":["Jiang Zhuoren","Gao Liangcai","Yuan Ke","Gao Zheng","Tang Zhi","Liu Xiaozhong"],"abstract":"Although the scientific digital library is growing at a rapid pace,\nscholars/students often find reading Science, Technology, Engineering, and\nMathematics (STEM) literature daunting, especially for the\nmath-content/formula. In this paper, we propose a novel problem, ``mathematics\ncontent understanding'', for cyberlearning and cyberreading. To address this\nproblem, we create a Formula Evolution Map (FEM) offline and implement a novel\nonline learning/reading environment, PDF Reader with Math-Assistant (PRMA),\nwhich incorporates innovative math-scaffolding methods. The proposed\nalgorithm/system can auto-characterize student emerging math-information need\nwhile reading a paper and enable students to readily explore the formula\nevolution trajectory in FEM. Based on a math-information need, PRMA utilizes\ninnovative joint embedding, formula evolution mining, and heterogeneous graph\nmining algorithms to recommend high quality Open Educational Resources (OERs),\ne.g., video, Wikipedia page, or slides, to help students better understand the\nmath-content in the paper. Evaluation and exit surveys show that the PRMA\nsystem and the proposed formula understanding algorithm can effectively assist\nmaster and PhD students better understand the complex math-content in the class\nreadings.","url_abs":"http://arxiv.org/abs/1812.11786v1","url_pdf":"http://arxiv.org/pdf/1812.11786v1.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":"mathematics-content-understanding-for","repo_url":"https://github.com/GraphEmbedding/FEM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"graph-mining","task_name":"Graph Mining"},{"task_slug":"math","task_name":"Math"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}