Abstract
Question answering is an indispensable link in high school teaching. Through question answering, on the one hand, it can solve students’ learning doubts, on the other hand, it can provide teachers with teaching feedback. However, through the investigation and research, it is found that with the expansion of student size, the effect of question answering in high school is not satisfactory. This paper analyzes the current situation of question answering in high school, and designs an intelligent question answering system for high school teaching based on constructivism learning theory and cognitive structure learning theory. The system, which is the first innovative application in the field of high school teaching, integrates knowledge graph technology and intelligent question answering technology, introduces big data technology. It can solve students’ questions in time and accurately, link the knowledge points related to the questions to help students construct knowledge network graph, and the big data technology is used to analyze the students’ questioning behavior and to predict students’ learning behavior in order to feedback the teaching effect.
Similar content being viewed by others
References
Miftachul H, Andino M, Pardimin A, Maragustam S, Roslee A, Jasmi Kamarul A, Nor MNH (2018) Big data emerging technology: insights into innovative environment for online learning resources[J]. Int J Emerg Technol Learn 13(01):23
Paul PK, Ghose MK (2018) A novel educational proposal and strategies toward promoting cloud computing, big data, and human–computer interaction in engineering colleges and universities[M]. Advances in Smart Grid and Renewable Energy
Knobloch J, Kaltenbach J, Bruegge B (2018) Increasing student engagement in higher education using a context-aware Q&A teaching framework[C]. the 40th International Conference
Zhiyun C, Yue S, Dongming Q (2018) Research on intelligent question answering system based on knowledge map [J]. Comput Appl Software 35(02):178–182
Cheng C, Jie Z, Jinyu Q, Jia J, Wu H, Tingting C (2018) Research on intelligent question answering technology based on TCM knowledge map [J]. China New Commun 20(02):204–207
Chen P, Lu Y, Zheng VW, Xiyang C, Boda Y (2018) KnowEdu: a system to construct knowledge graph for education[J]. IEEE Access PP(99):1–1
Wei, QF, Cao, CZ, Wang, FR, Luo, CS (2017) Q&A system development based on agricultural knowledge map and multi-level association model[J]. Adv Soc, Educ Hum 61(−):536–538
Liu Y, Xu B, Yang Y, Tonglee C, Zhang P (2019) Constructing a hybrid automatic Q&A system integrating knowledge graph and information retrieval technologies[M]. Foundations and Trends in Smart Learning
Ye D, Xing Z, Kapre N (2017) The structure and dynamics of knowledge network in domain-specific Q&A sites: a case study of stack overflow[J]. Empir Softw Eng 22(1):375–406
Huang Huan M, Tingting H, Linjing W (2019) A study on the construction of curriculum knowledge map for adaptive learning system -- taking "Java programming foundation" as an example [J]. Modern Educ Technol 29(12):89–95
Mingyu C, Qingqing L, Yang Z, Wang L, Yin Z, Lin H, Wang J (2019) Knowledge query system of primary liver cancer based on knowledge map [J]. Chin J Inform 33(06):88–93
Zhe L, Meng C, Takanori M, Li J (2018) Construction and application of korean-english-japanese multilingual teaching aid system based on knowledge map[J]. Int J Distance Educ Technol 16(4):1–14
Peng Z, Wei Z, Xianming Y (2019) Auto-construction of course knowledge graph based on course knowledge[J]. Int J Performability Eng 15(8):2228
Sharma Y, Gupta S (2018) Deep learning approaches for question answering system[J]. Procedia Computerence 132:785–794
Sun G, Chen T, Su Y, Li C (2018) Internet traffic classification based on incremental support vector machines[J]. Mobile Networks Appl 23(4):789–796
Sun G, Li J, Dai J, Song Z, Lang F (2018) Feature selection for IoT based on maximal information coefficient[J]. Future Gen Comput Syst-Int J Esci 89:606–616
Wen J, Zhang W, Shu W (2018) A cognitive learning model in distance education of higher education institutions based on chaos optimization in big data environment[J]. J Supercomput
Zhang W, Jiang L (2018) Algorithm analysis for big data in education based on depth learning[J]. Wirel Pers Commun
Qiao L, Yang L, Hong D, Yao L, Zhiguang Q (2016) Overview of knowledge map construction technology [J]. Comput Res Develop 53(03):582–600
Acknowledgments
The research is supported by Program for Yunnan Key Laboratory of Smart Education, and Program for innovative research team(in Science and Technology) in University of Yunnan Province.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Yang, Z., Wang, Y., Gan, J. et al. Design and Research of Intelligent Question-Answering(Q&A) System Based on High School Course Knowledge Graph. Mobile Netw Appl 26, 1884–1890 (2021). https://doi.org/10.1007/s11036-020-01726-w
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11036-020-01726-w