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AI summary · arXiv: 2010.08995v1 · cs.DB

Construction and Application of Teaching System Based on Crowdsourcing Knowledge Graph

Jinta Weng, Ying Gao, Jing Qiu, Guozhu Ding, Huanqin Zheng
Original paper · DOI 10.1007/978-981-15-1956-7_3 Submitted to arXiv on 18 Oct. 2020 4th China Conference on Knowledge Graph and Semantic Computing, CCKS 2019 Number of references:15 Classification code:903.3 Information Retrieval and Use Conference code: 235759 License: NONEXCLUSIVE-DISTRIB 1.0

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The essentials

  • Combination of crowdsourcing knowledge graph and teaching systems
  • Research methods to generate a knowledge graph and its applications in teaching systems
  • Utilization of crowdsourcing task distribution and reverse captcha generation for constructing a comprehensive hierarchical knowledge graph
  • Nodes representing entities such as schools, students, teachers, courses, knowledge points, and exercise types in the constructed knowledge graph
  • Active participation from multiple users while considering teachers' guidance and users' mobilization issues
  • Analysis of three subgraphs - prominent teacher, student learning situation, and suitable learning route - for valuable insights visualization
  • Personalized exercise recommendation model based on algorithms derived from the knowledge graph for personalized learning experience
  • Collaborative creation model to facilitate crowdsourcing construction mechanism
  • Challenges include learners' unfamiliarity with the learning mode of a knowledge graph and limited attention to the knowledge structure itself
  • High acceptance among both students and teachers for this system based on Crowdsourcing Knowledge Graph

Key points written by AI: they may contain errors, check them against the paper.

AI-generated keywords: Crowdsourcing Knowledge Graph Teaching System Personalized Exercise Recommendation Collaborative Creation Model Comprehensive Hierarchical Knowledge Graph

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The paper titled "Construction and Application of Teaching System Based on Crowdsourcing Knowledge Graph" explores the combination of crowdsourcing knowledge graph and teaching systems. The authors propose research methods to generate a knowledge graph and its applications in the field of teaching systems. They utilize two crowdsourcing approaches, namely crowdsourcing task distribution and reverse captcha generation, to construct a comprehensive hierarchical knowledge graph in the teaching domain. The constructed knowledge graph consists of nodes representing various entities such as schools, students, teachers, courses, knowledge points, and exercise types. The collaborative nature of the crowdsourcing approach requires active participation from multiple users while considering teachers' guidance and users' mobilization issues. By analyzing three subgraphs of the knowledge graph - prominent teacher, student learning situation, and suitable learning route - valuable insights can be visualized. To personalize the learning experience for students, the authors employ a personalized exercise recommendation model that formulates exercises based on algorithms derived from the knowledge graph. Additionally, they develop a collaborative creation model to facilitate the construction mechanism of crowdsourcing. Despite potential challenges such as learners' unfamiliarity with the learning mode of a knowledge graph and their limited attention to the knowledge structure itself, this system based on Crowdsourcing Knowledge Graph receives high acceptance among both students and teachers. In conclusion, this paper presents an innovative approach to leverage crowdsourced knowledge graphs in teaching systems. By involving multiple users in constructing a comprehensive hierarchical knowledge graph and utilizing it for personalized exercise recommendations tailored to individual student needs; this system offers valuable insights into teaching practices while catering to individual student needs.
Created on 24 Aug. 2023

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