CourseKG: An Educational Knowledge Graph Based on Course Information for Precision Teaching
Source abstract
Abstract
With the rapid development of advanced technologies, such as artificial intelligence and deep learning, educational informatization has entered a new era. However, the explosion of information has brought numerous challenges. Knowledge graphs, as a crucial component of artificial intelligence, can contribute to the quality of teaching. This study proposes an educational knowledge graph based on course information named CourseKG for precision teaching. Precision teaching seeks to individualize the curriculum for each learner and optimize learning efficiency. CourseKG aims to establish a correct and comprehensive curriculum knowledge system and promote personalized learning paths. CourseKG can address the issue that current general-purpose knowledge graphs are not suitable for the education field. Particularly, this study proposes a framework for educational entity recognition based on the pre-trained BERT model. This framework captures relevant information in the educational domain using the BERT model and combines it with the BiGRU and multi-head self-attention mechanism to extract multi-scale and multi-level global dependency relationships. In addition, the CRF is used for character-label decoding. Further, a relationship extraction method based on the BERT model, which integrates sentence features and educational entities and estimates the similarity between knowledge pairs using cosine similarity, is proposed. The proposed CourseKG is verified by experiments using real-world C programming course data. The experimental results demonstrate the effectiveness of CourseKG. Finally, the results show that the proposed CourseKG can significantly enhance the precision teaching quality and realize multi-directional adaptation among teachers, courses, and students.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Graph Neural Networks · Semantic Web and Ontologies · Intelligent Tutoring Systems and Adaptive Learning
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science and Engineering, Beihang University, Beijing 100191, China
Faculty of Information, Beijing University of Technology, Beijing 100124, China
Funding
National Key Research and Development Project of China · Engineering Research Center of Integration and Application of Digital Learning Technology, Ministry of Education · National Natural Science Foundation of China · Beijing Postdoctoral Research Foundation
References (52)
Show source-provided references
1. Past, present, and future of smart learning: A topic-based bibliometric analysis10.1186/s41239-020-00239-6 ↗
2. Schmidt, J.T., and Tang, M. (2020). Führen und Managen in der Digitalen Transformation: Trends, Best Practices und Herausforderungen, Springer.
3. A critical look at educational technology from a distance education perspective10.1344/der.2020.37.208-229 ↗
4. Twyman, J.S. (2016). Handbook on Personalized Learning for States, Districts, and Schools, Temple University.
5. Li, Y., Qiu, J., Gui, S., Song, Y., and Liu, T. (2022, January 8–11). Analytics 2.0 for Precision Education Driven by Knowledge Map. Proceedings of the 2022 IEEE Frontiers in Education Conference (FIE), Uppsala, Sweden.10.1109/FIE56618.2022.9962461 ↗
6. Named entity extraction for knowledge graphs: A literature overview10.1109/ACCESS.2020.2973928 ↗
7. Knowledge graph-based manufacturing process planning: A state-of-the-art review10.1016/j.jmsy.2023.08.006 ↗
8. Web Semantic10.1038/scientificamerican0501-34 ↗
9. Bizer, C., Heath, T., and Berners-Lee, T. (2011). Chapter Linked Data: The Story So Far, Information Science Reference.10.4018/978-1-60960-593-3.ch008 ↗
10. Introducing the knowledge graph: Things, not strings
11. Dbpedia-a crystallization point for the web of data10.1016/j.websem.2009.07.002 ↗
12. Dbpedia–a large-scale, multilingual knowledge base extracted from wikipedia10.3233/SW-140134 ↗
13. Erxleben, F., Günther, M., Krötzsch, M., Mendez, J., and Vrandečić, D. (2014, January 19–23). Introducing wikidata to the linked data web. Proceedings of the The Semantic Web–ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy. Proceedings, Part I 13.10.1007/978-3-319-11964-9_4 ↗
14. Yago: A large ontology from wikipedia and wordnet10.1016/j.websem.2008.06.001 ↗
15. Bollacker, K., Cook, R., and Tufts, P. (2007, January 22–26). Freebase: A shared database of structured general human knowledge. Proceedings of the AAAI, Vancouver, BC, Canada.
16. Bollacker, K., Evans, C., Paritosh, P., Sturge, T., and Taylor, J. (2008, January 9–12). Freebase: A collaboratively created graph database for structuring human knowledge. Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, Vancouver, BC, Canada.10.1145/1376616.1376746 ↗
17. A survey on application of knowledge graph
18. Berant, J., Chou, A., Frostig, R., and Liang, P. (2013, January 18–21). Semantic parsing on freebase from question-answer pairs. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Washington, DC, USA.
19. Dong, L., Wei, F., Zhou, M., and Xu, K. (2015, January 26–31). Question answering over freebase with multi-column convolutional neural networks. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Beijing, China.10.3115/v1/P15-1026 ↗
20. Bordes, A., Chopra, S., and Weston, J. (2014). Question answering with subgraph embeddings. arXiv.10.3115/v1/D14-1067 ↗
21. Yao, X., and Van Durme, B. (2014, January 22–27). Information extraction over structured data: Question answering with freebase. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Baltimore, MD, USA.10.3115/v1/P14-1090 ↗
22. Explainable reasoning over knowledge graphs for recommendation
23. Wang, H., Zhang, F., Xie, X., and Guo, M. (2018, January 23–27). DKN: Deep knowledge-aware network for news recommendation. Proceedings of the 2018 World Wide Web Conference, Lyon, France.10.1145/3178876.3186175 ↗
24. Building a knowledge graph to enable precision medicine10.1038/s41597-023-01960-3 ↗
25. Liu, K., Wang, F., Ding, Z., Liang, S., Yu, Z., and Zhou, Y. (2022). A review of knowledge graph application scenarios in cyber security. arXiv.
26. Wang, S. (April, January 29). On the Analysis of Large Integrated Knowledge Graphs for Economics, Banking and Finance. Proceedings of the EDBT/ICDT Workshops, Edinburgm, UK.
27. Semantic knowledge graphs for the news: A review10.1145/3543508 ↗
28. He, Q., Yang, J., and Shi, B. (2020, January 20–24). Constructing knowledge graph for social networks in a deep and holistic way. Proceedings of the Companion Proceedings of the Web Conference 2020, Taipei, Taiwan.10.1145/3366424.3383112 ↗
29. Knowledge graph application in education: A literature review10.18778/0208-6018.342.01 ↗
30. Knowedu: A system to construct knowledge graph for education10.1109/ACCESS.2018.2839607 ↗
31. Su, Y., and Zhang, Y. (2020, January 1–3). Automatic construction of subject knowledge graph based on educational big data. Proceedings of the 2020 the 3rd International Conference on Big Data and Education, London, UK.10.1145/3396452.3396458 ↗
32. Dalipi, F., Imran, A.S., and Kastrati, Z. (2018, January 17–20). MOOC dropout prediction using machine learning techniques: Review and research challenges. Proceedings of the 2018 IEEE Global Engineering Education Conference (EDUCON), Santa Cruz de Tenerife, Spain.10.1109/EDUCON.2018.8363340 ↗
33. Sun, K., Liu, Y., Guo, Z., and Wang, C. (2016, January 24–26). EduVis: Visualization for education knowledge graph based on web data. Proceedings of the 9th International Symposium on Visual Information Communication and Interaction, Dallas, TX, USA.10.1145/2968220.2968227 ↗
34. Chen, P., Lu, Y., Zheng, V.W., Chen, X., and Li, X. (2018, January 26–28). An automatic knowledge graph construction system for K-12 education. Proceedings of the Fifth Annual ACM Conference on Learning at Scale, London, UK.10.1145/3231644.3231698 ↗
35. A learning path recommendation model based on a multidimensional knowledge graph framework for e-learning10.1016/j.knosys.2020.105618 ↗
36. Shi, D., Zhou, J., Wang, D., and Wu, X. (2022). Research Status, Hotspots, and Evolutionary Trends of Intelligent Education from the Perspective of Knowledge Graph. Sustainability, 14.10.3390/su141710934 ↗
37. Knowledge map-based online micro-learning: Impacts on learning engagement, knowledge structure, and learning performance of in-service teachers10.1080/10494820.2021.1903932 ↗
38. Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., and Dyer, C. (2016). Neural architectures for named entity recognition. arXiv.10.18653/v1/N16-1030 ↗
39. Yadav, V., and Bethard, S. (2019). A survey on recent advances in named entity recognition from deep learning models. arXiv.
40. Aydar, M., Bozal, O., and Ozbay, F. (2020). Neural relation extraction: A survey. arXiv.
41. Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv.
42. Che, W., Feng, Y., Qin, L., and Liu, T. (2021, January 7–11). N-LTP: An Open-source Neural Language Technology Platform for Chinese. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Punta Cana, Dominican Republic.10.18653/v1/2021.emnlp-demo.6 ↗
43. Dey, R., and Salem, F.M. (2017, January 6–9). Gate-variants of gated recurrent unit (GRU) neural networks. Proceedings of the 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), Boston, MA, USA.10.1109/MWSCAS.2017.8053243 ↗
44. An introduction to conditional random fields10.1561/2200000013 ↗
45. Mastery learning
46. Computing semantic similarity of concepts in knowledge graphs10.1109/TKDE.2016.2610428 ↗
47. (2024, March 10). CourseGrading. Available online: https://www.educg.net/.
48. (2024, March 10). Educoder. Available online: https://www.educoder.net/.
49. (2024, March 10). Jieba. Available online: https://github.com/fxsjy/jieba/.
50. (2024, March 10). Neo4j. Available online: https://neo4j.com/.
51. (2024, March 10). Vue. Available online: https://vuejs.org/.
52. (2024, March 10). D3. Available online: https://d3js.org/.