Abstract
With the continuous development of information technology in education, Python course has a pivotal position in the current information technology curriculum system. But there are the following problems in the process of teaching: it is difficult for students to construct meaningful knowledge; the teaching materials cannot effectively stimulate the interest in learning; content taught in the classroom cannot meet the education needs either. Therefore, it is urgent to construct a Python Knowledge Gragh (KG) that covers heterogeneous data from multiple sources and enables knowledge visualization. We constructed a Python KG based on the Neo4j graph database and interactive graph framework from the perspectives of innovating how to present teaching content, improve learning interest, and expand teaching resources. We demonstrated by designing teaching experiments that the Python KG could address the issues of information overload and passive learning by making Python more accessible, engaging, and personalized.
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References
Xu **gcheng. Ministry of Education Issues Education Informatization 2.0 Action Plan[J]. Information Technology Education in Primary and Secondary Schools,2018,197(05):4
Ye Yuting. Smart education will break through the boundaries of schooling[N]. China Youth Daily,2023-02-14(002)
Tao W, **angwei Z, Lifeng Z, et al. A graph-based interpretability method for deep neural networks[J]. Neurocomputing,2023,555
Gu Yile. Patent analysis of knowledge map** technology based on patent information measurement[J]. Journal of Library and Intelligence, 2021,6(07):45-52+61
Suchanek F M, Kasneci G, Weikum G. Yago: a core of semantic knowledge[C]. Proceedings of the 16th International Conference on World Wide Web. 2007: 697-706
Bizer, C., et al.: DBpedia - A crystallization point for the Web of Data[J]. Web Semantics: Science, Services and Agents on the World Wide Web 7(3), 154–165 (2009)
Bollacker K, Evans C, Paritosh P, et al. Freebase: a collaboratively created graph database for structuring human knowledge[C]. Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. 2008: 1247-1250
Auer S, Bizer C, Kobilarov G, et al. Dbpedia: a nucleus for a web of open data[C]. The Semantic Web: 6th International Semantic Web Conference, 2nd Asian Semantic Web Conference, ISWC 2007+ ASWC 2007, Busan, Korea, November 11-15, 2007. Proceedings. Springer Berlin Heidelberg, 2007: 722-735
Herder E, Dietze S, D’Aquin M. LinkedUp-Linking Web data for adaptive education.[C]. UMAP Workshops. 2013
Dong **aoxiao, G., Hengnian, Z.D.: New research progress in knowledge graph and its application challenges in education[J]. Digital Education 8(05), 10–17 (2022)
Niu, X., Sun, X., Wang, H., Zhishi. me-weaving chinese linking open data[C]. The Semantic Web-ISWC, et al.: 10th International Semantic Web Conference, Bonn, Germany, October 23–27, 2011, Proceedings, Part II 10. Springer, Berlin Heidelberg 2011, 205–220 (2011)
Xu B, Xu Y, Liang J, et al. CN-DBpedia: A never-ending Chinese knowledge extraction system[C]. Advances in Artificial Intelligence: from Theory to Practice: 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, Arras, France, June 27-30, 2017, Proceedings, Part II. Cham: Springer International Publishing, 2017: 428 -438
Amr A, Garcia J D F, Maribel A, et al. SMART-KG: Hybrid Ship** for SPARQL Querying on the Web[J]. 2020
Huang, H., Wang, H., Wang, X.: An analysis framework of research frontiers based on the large-scale open academic graph[J]. Proceedings of the Association for Information Science and Technology 57(1), e307 (2020)
Yajun, X., Jian, G.: Personalized learning resources recommendation for K12 learning platform[J]. Computer System Applications 29(07), 217–221 (2020)
Jiarui, L., Huayu, L., Yang, Y., et al.: Construction and application of knowledge map** for discipline construction based on event extraction[J]. Computer System Applications 31(11), 100–110 (2022)
Yu, X.M.: Software and educational information service. Shandong People’s Publishing House 2022, 05 (2022)
Jiao, X., Yu, X., Peng, H., Zhang, X.: A Smart Learning Assistant to Promote Learning Outcomes in a Programming Course. International Journal of Software Science and Computational Intelligence (IJSSCI) 14(1), 1–23 (2022)
Xuanchi C, **angwei Z, Kai S, et al. Self-supervised vision transformer-based few-shot learning for facial expression recognition[J]. Information Sciences,2023,634
Yongqiang Y, **angwei Z,Bin H, et al. EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM[J]. Applied Soft Computing Journal,2021,100
Gong, Z., Yu, X., Fu, W., Che, X., Mao, Q., Zheng, X. (2021). The Construction of Knowledge Graph for Personalized Online Teaching. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1454. Springer, Singapore. https://doi.org/10.1007/978-981-16-7502-7-11
Acknowledgement
This work is supported by Shandong Provincial Project of Graduate Education Quality Improvement (No. SDYJG21104, No. SDYJG19171), the Key R &D Program of Shandong Province, China (NO. 2021SFGC0104, NO. 20 21CXGC010506), the Natural Science Foundation of Shandong Province, China (No. ZR2020LZH008, ZR2021MF118, ZR2022LZH003), the National Natural Science Foundation of China under Grant (NO. 62101311, No. 62072290), the Postgraduate Quality Education and Teaching Resources Project of Shandong Province (SDYKC2022053, SDYAL2022060), the Shandong Normal University Research Project of Education and Teaching (No. 2019XM48), and Industry-University Cooperation and Education Project of Ministry of Education (No. 220602695231855).
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Jiao, X., Yu, X., Peng, H., Gong, Z., Zhao, L. (2024). The Design and Implementation of Python Knowledge Graph for Programming Teaching. In: Vaidya, J., Gabbouj, M., Li, J. (eds) Artificial Intelligence Security and Privacy. AIS&P 2023. Lecture Notes in Computer Science, vol 14510. Springer, Singapore. https://doi.org/10.1007/978-981-99-9788-6_9
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DOI: https://doi.org/10.1007/978-981-99-9788-6_9
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