research-article

Mathematics Content Understanding for Cyberlearning via Formula Evolution Map

Authors:

Xiaozhong LiuAuthors Info & Claims

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

Pages 37 - 46

https://doi.org/10.1145/3269206.3271694

Published: 17 October 2018 Publication History

Abstract

Although the scientific digital library is growing at a rapid pace, scholars/students often find reading Science, Technology, Engineering, and Mathematics (STEM) literature daunting, especially for the math-content/formula. In this paper, we propose a novel problem, "mathematics content understanding", for cyberlearning and cyberreading. To address this problem, we create a Formula Evolution Map (FEM) offline and implement a novel online learning/reading environment, PDF Reader with Math-Assistant (PRMA), which incorporates innovative math-scaffolding methods. The proposed algorithm/system can auto-characterize student emerging math-information need while reading a paper and enable students to readily explore the formula evolution trajectory in FEM. Based on a math-information need, PRMA utilizes innovative joint embedding, formula evolution mining, and heterogeneous graph mining algorithms to recommend high quality Open Educational Resources (OERs), e.g., video, Wikipedia page, or slides, to help students better understand the math-content in the paper. Evaluation and exit surveys show that the PRMA system and the proposed formula understanding algorithm can effectively assist master and PhD students better understand the complex math-content in the class readings.

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Cited By

Kambhamettu HMetaxa DJohnson KHead A(2024)Explainable Notes: Examining How to Unlock Meaning in Medical Notes with Interactivity and Artificial IntelligenceProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642573(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642573
Duan ZGu HKe YZhou D(2024)EBERT: A lightweight expression-enhanced large-scale pre-trained language model for mathematics educationKnowledge-Based Systems10.1016/j.knosys.2024.112118300(112118)Online publication date: Sep-2024
https://doi.org/10.1016/j.knosys.2024.112118
Head ALo KKang DFok RSkjonsberg SWeld DHearst MKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and SymbolsProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445648(1-18)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445648
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Index Terms

Mathematics Content Understanding for Cyberlearning via Formula Evolution Map
1. Information systems
  1. Information retrieval
    1. Specialized information retrieval
      1. Structure and multilingual text search
        Mathematics retrieval
  2. Information systems applications
    1. Data mining

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cover image ACM Conferences

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

October 2018

2362 pages

ISBN:9781450360142

DOI:10.1145/3269206

General Chair:
Alfredo Cuzzocrea
University of Trieste, Italy
,
Program Chairs:
James Allan
University of Massachusetts, USA
,
Norman Paton
University of Manchester, United Kingdom
,
Divesh Srivastava
AT&T Labs Research, USA
,
Rakesh Agrawal
Data Insights Lab, USA
,
Andrei Broder
Google Research, USA
,
Mohammed Zaki
Rensselaer Polytechnic Institute, USA
,
Selcuk Candan
Arizona State University, USA
,
Alexandros Labrinidis
University of Pittsburgh, USA
,
Assaf Schuster
Technion, Israel
,
Haixun Wang
Google Research, USA

Copyright © 2018 ACM.

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Publication History

Published: 17 October 2018

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Guangdong Province Frontier and Key Technology Innovative Grant
Opening Project of State Key Laboratory of Digital Publishing Technology
National Natural Science Foundation of China

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CIKM '18

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CIKM '18: The 27th ACM International Conference on Information and Knowledge Management

October 22 - 26, 2018

Torino, Italy

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CIKM '18 Paper Acceptance Rate 147 of 826 submissions, 18%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Kambhamettu HMetaxa DJohnson KHead A(2024)Explainable Notes: Examining How to Unlock Meaning in Medical Notes with Interactivity and Artificial IntelligenceProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642573(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642573
Duan ZGu HKe YZhou D(2024)EBERT: A lightweight expression-enhanced large-scale pre-trained language model for mathematics educationKnowledge-Based Systems10.1016/j.knosys.2024.112118300(112118)Online publication date: Sep-2024
https://doi.org/10.1016/j.knosys.2024.112118
Head ALo KKang DFok RSkjonsberg SWeld DHearst MKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and SymbolsProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445648(1-18)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445648
Gkorgkolis NBourbakis N(2021)Evaluating Methodologies on Deep Understanding of Mathematical Formulas in Technical Documents2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI52525.2021.00148(920-926)Online publication date: Nov-2021
https://doi.org/10.1109/ICTAI52525.2021.00148
Yuan KGao LJiang ZTang Z(2021)Formula Citation Graph Based Mathematical Information RetrievalDocument Analysis and Recognition – ICDAR 202110.1007/978-3-030-86549-8_40(631-647)Online publication date: 2-Sep-2021
https://doi.org/10.1007/978-3-030-86549-8_40
Zhao WGao LYan ZPeng SDu LZhang Z(2021)Handwritten Mathematical Expression Recognition with Bidirectionally Trained TransformerDocument Analysis and Recognition – ICDAR 202110.1007/978-3-030-86331-9_37(570-584)Online publication date: 2-Sep-2021
https://doi.org/10.1007/978-3-030-86331-9_37
Jiang ZGao ZLan JYang HLu YLiu X(2020)Task-Oriented Genetic Activation for Large-Scale Complex Heterogeneous Graph EmbeddingProceedings of The Web Conference 202010.1145/3366423.3380230(1581-1591)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380230
Jiang ZWang JZhao LSun CLu YLiu XZhu WTao DCheng XCui PRundensteiner ECarmel DHe QXu Yu J(2019)Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation LearningProceedings of the 28th ACM International Conference on Information and Knowledge Management10.1145/3357384.3357989(289-298)Online publication date: 3-Nov-2019
https://dl.acm.org/doi/10.1145/3357384.3357989
He GKang YGao ZJiang ZSun CLiu XLu WZhang QSi LPiwowarski BChevalier MGaussier EMaarek YNie JScholer F(2019)Finding Camouflaged Needle in a Haystack?Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331197(365-374)Online publication date: 18-Jul-2019
https://dl.acm.org/doi/10.1145/3331184.3331197

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