BUILDING KNOWLEDGE GRAPHS FOR MOOC SELECTION BASED ON THE INTEGRATION OF LABOR MARKET DEMANDS AND EDUCATIONAL PROGRAMS

The article presents the development of an integration model based on knowledge graphs and machine learning methods for analyzing relationships between educational programs, labor market requirements, and online courses (MOOCs). The relevance of the study is driven by rapid changes in the information technology sector, which lead to a mismatch between graduate competencies and employer expectations. To reduce this gap, a heterogeneous knowledge graph integrating data from university curricula, job vacancies, and online courses is proposed. The model implements a meta-path connecting skills, job requirements, and learning courses, enabling recommendations for educational and career development for different categories of users. Multilingual transformers were applied to process textual descriptions in Russian and English. The data sources include competency maps of educational programs, job postings from the hh.kz platform, and courses from Coursera, all integrated into the Neo4j graph database. The obtained results demonstrate the capability to identify missing competencies and automatically recommend relevant courses for their acquisition. The proposed model can be applied in curriculum design, development of learning trajectories, and decision-making support within educational environments.

1. Zhao, T., et al. Mathgraph: A knowledge graph for automatically solving mathematical exercises. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (Eds.), Database Systems for Advanced Applications. DASFAA 2019, 629–633 (2019). https://doi.org/10.1007/978-3-030-18576-3_45

2. Li, W., et al. Constructing low-redundant and high-accuracy knowledge graphs for education. In: González-González, C.S., et al. Learning Technologies and Systems. ICWL SETE 2022, 139–150 (2023). https://doi.org/10.1007/978-3-031-33023-0_13

3. Agrawal, G., Deng, Y., Park, J., Liu, H., and Chen, Y.-C. Building knowledge graphs from unstructured texts: Applications and impact analyses in cybersecurity education. Information, 13 (11), 526 (2022). https://doi.org/10.3390/info13110526

4. Su, Y., and Zhang, Y. Automatic construction of subject knowledge graph based on educational big data. Proceedings of the 2020 3rd International Conference on Big Data and Education (ICBDE ‘20), 129–135 (2020). https://doi.org/10.1145/3396452.3396458

5. Qin, Y., Cao, H., and Xue, L. Research and application of knowledge graph in teaching: Take the database course as an example. Journal of Physics: Conference Series, 1607, 012127 (2020). https://doi.org/10.1088/1742-6596/1607/1/012127

6. Rizun, M. Knowledge graph application in education: A literature review. Acta Universitatis Lodziensis. Folia Oeconomica, 3 (342), 7–19 (2019). https://doi.org/10.18778/0208-6018.342.01

7. Sun, Y., Liang, J., and Niu, P. Generation of personalized knowledge graphs based on GCN. Journal of Computer and Communications, 9 (9), 108–119 (2021). https://doi.org/10.4236/jcc.2021.99008

8. Meissner, R., and Thor, A. Creation and utilisation of domain specific knowledge graphs (DSKG) for e-learning. DELFI 2021, 151–162 (2021). https://dl.gi.de/server/api/core/bitstreams/b99e4bf0-10aa-4e4ea0f1-96c1497bf3ee/content

9. Chen, P., Lu, Y., Zheng, V.W., Chen, X., and Yang, B. Knowedu: A system to construct knowledge graph for education. IEEE Access, 6, 31553–31563 (2018). https://doi.org/10.1109/ACCESS.2018.2839607

10. Ke, Q., and Lin, J. Dynamic generation of knowledge graph supporting STEAM learning theme design. Applied Sciences, 12 (21), 11001 (2022). https://doi.org/10.3390/app122111001

11. Yu, X., Stahr, M., Chen, H., and Yan, R.G. Design and implementation of curriculum system based on knowledge graph. 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE), 767–770 (2020). https://arxiv.org/ftp/arxiv/papers/2012/2012.12522.pdf

12. Aliyu, I., Kana, A.F., and Aliyu, S. Development of knowledge graph for university courses management. International Journal of Education and Management Engineering, 10 (2), 1–10 (2020). https://doi.org/10.5815/ijeme.2020.02.01

13. Tel’nov, V.P., and Korovin, Ju.A. Semanticheskij veb i grafy znanij kak obrazovatel’naja tehnologija podgotovki kadrov dlja jadernoj jenergetiki [Semantic Web and Knowledge Graphs as an Educational Technology for Nuclear Engineering Training]. Izvestija vysshih uchebnyh zavedenij. Jadernaja jenergetika, (2), 219–229 (2019). https://doi.org/10.26583/npe.2019.2.19 (in Russian).

14. Nair, L.S., Shivani, M.K., and Cheriyan, S.J. Enabling remote school education using knowledge graphs and deep learning techniques. Procedia Computer Science, 215, 618–625 (2022). https://doi.org/10.1016/j.procs.2022.12.064

15. Chi, Y., Qin, Y., Song, R., and Xu, H. Knowledge graph in smart education: A case study of entrepreneurship scientific publication management. Sustainability, 10 (4), 995 (2018). https://doi.org/10.3390/su10040995

16. Giabelli, A., Malandri, L., Mercorio, F., Mezzanzanica, M., and Seveso, A. Skills2Job: A recommender system that encodes job offer embeddings on graph databases. Applied Soft Computing, 101, 107049 (2020). https://doi.org/10.1016/j.asoc.2020.107049

17. Groot, M., Schutte, J., and Graus, D. Job posting-enriched knowledge graph for skills-based matching. arXiv preprint, arXiv:2109.02554 (2021). https://doi.org/10.48550/arXiv.2109.02554

18. Fettach, Y., Ghogho, M., and Benatallah, B. Knowledge graphs in education and employability: A survey on applications and techniques. IEEE Access, 10, 85857–85877 (2022). https://doi.org/10.1109/ ACCESS.2022.3194063

19. Weichselbraun, A., Waldvogel, R., Fraefel, A., van Schie, A., and Kuntschik, P. Building knowledge graphs and recommender systems for suggesting reskilling and upskilling options from the web. Information, 13 (11), 510 (2022). https://doi.org/10.3390/info13110510

20. Zhu, G., Chen, Y., and Wang, S. Graph-community-enabled personalized course-job recommendations with cross-domain data integration. Sustainability, 14 (12), 7439 (2022). https://doi.org/10.3390/su14127439

21. Börner, K., Scrivner, O., Gallant, M., Ma, S., Liu, X., Chewning, K., and Evans, J.A. Skill discrepancies between research, education, and jobs reveal the critical need to supply soft skills for the data economy. Proceedings of the National Academy of Sciences, 115 (50), 12630–12637 (2018). https://doi.org/10.1073/pnas.1804247115

22. Chau, H.K. Explainable Course Recommendation: Connecting College Education to Knowledge and Careers Through Skills [Dissertation] (Pittsburgh, PA: University of Pittsburgh, Department of Informatics and Networked Systems, School of Computing and Information, 2024).

23. Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., de Melo, G., Gutierrez, C., Kirrane, S., Labra Gayo, J.E., Navigli, R., Neumaier, S., Ngonga Ngomo, A.-C., Polleres, A., Rashid, S.M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., and Zimmermann, A. Knowledge graphs. ACM Computing Surveys, 54 (4), Article 71 (2021). https://doi.org/10.1145/3447772

24. Ramazanova, V.S., Sambetbaeva, M.A., and Zagorul’ko, Ju.A. Obzor i analiz predstavlenij grafov znanij [Review and Analysis of Knowledge Graph Representations]. Trudy universiteta, (1), 414–422 (2024). https://doi.org/10.52209/1609-1825_2024_1_414 (in Russian).

25. IEEE Standard for Framework of Knowledge Graphs. IEEE Std 2807-2022, 1–52 (2023). https://doi.org/10.1109/IEEESTD.2022.10017167

26. Zagorulko, G.B. Model, Methods, and Tools for Comprehensive Support of DSS Development in Poorly Formalized Subject Areas [Dissertation] (Novosibirsk: A.P. Ershov Institute of Informatics Systems, Siberian Branch of the Russian Academy of Sciences, 2020). (in Russian).