Abstract
Creating a well-structured and cohesive curriculum is essential for providing students with a meaningful and organized educational experience. However, the complexity and scale of curricula and the need for regular updates make developing a curriculum for a single major a highly challenging task. In this paper, we aim to construct a comprehensive tool designed to automatically analyze existing curricula and identify areas of incoherence. The presented approach utilizes unsupervised learning techniques and large language models (LLMs) to extract the main concepts covered by courses and their prerequisites directly from course syllabi. The extracted entities are then linked to Wikidata entities, ensuring both their correctness and uniqueness. By constructing a knowledge graph based on the relationships between courses and main educational concepts, the cohesion of the curriculum can be systematically verified. The verification process involves checking if all cohesion logic predicates are satisfied, thereby ensuring that the curriculum is both comprehensive and logically structured. The results demonstrate the efficacy of our approach in identifying inconsistencies within educational curricula, thereby aiding in the development of more cohesive and well-organized educational programs.
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References
Hulth, A.: Improved automatic keyword extraction given more linguistic knowledge. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pp. 216–223 (2003). https://aclanthology.org/W03-1028
Kim, S.N., Medelyan, O., Kan, M.-Y., Baldwin, T.: SemEval-2010 task 5: automatic keyphrase extraction from scientific articles. In: Proceedings of the 5th International Workshop on Semantic Evaluation, pp. 21–26, Uppsala, Sweden (2010). Association for Computational Linguistics. https://aclanthology.org/S10-1004
Augenstein, I., Das, M., Riedel, S., Vikraman, L., McCallum, A.: SemEval 2017 Task 10: ScienceIE - extracting keyphrases and relations from scientific publications. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 546–555, Vancouver, Canada (2017). Association for Computational Linguistics. https://doi.org/10.18653/v1/S17-2091, https://aclanthology.org/S17-2091
Yuan, X., et al.: One size does not fit all: generating and evaluating variable number of keyphrases. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7961–7975, Online (2020). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.710, https://aclanthology.org/2020.acl-main.710
Porter, M.F.: An Algorithm for Suffix Stripping. In: Program, vol. 14, pp. 130–137 (1980). https://doi.org/10.1108/eb046814
Rose, S., Engel, D., Cramer, N., Cowley, W.: Automatic keyword extraction from individual documents. In: Text Mining: Applications and Theory, pp. 1–20 (2010). https://doi.org/10.1002/9780470689646.ch1
Campos, R., Mangaravite, V., Pasquali, A., Jorge, A., Nunes, C., Jatowt, A.: YAKE! Keyword extraction from single documents using multiple local features. In: Information Sciences 509, 257–289 (2020). https://doi.org/10.1016/j.ins.2019.09.013, https://www.sciencedirect.com/science/article/pii/S0020025519308588
Mihalcea, R., Tarau, P.: TextRank: bringing order into text. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, pp. 404–411, Barcelona, Spain (2004). Association for Computational Linguistics, https://aclanthology.org/W04-3252
Florescu, C., Caragea, C.: PositionRank: an unsupervised approach to keyphrase extraction from scholarly documents. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1105–1115, Vancouver, Canada (2017). Association for Computational Linguistics. https://doi.org/10.18653/v1/P17-1102, https://aclanthology.org/P17-1102
Mendes, P.N., Jakob, M., García-Silva, A., Bizer, C.: DBpedia spotlight: shedding light on the web of documents. In: Proceedings of the 7th International Conference on Semantic Systems, pp. 1–8, Graz, Austria (2011). Association for Computing Machinery. https://doi.org/10.1145/2063518.2063519.
Yu, X., Stahr, M., Chen, H., Yan, R.G.: Design and implementation of curriculum system based on knowledge graph. In: 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE), pp. 767–770 (2020). https://api.semanticscholar.org/CorpusID:229363688
Chen, P., Yu, L., Zheng, V.W., Chen, X., Yang, B.: KnowEdu: a system to construct knowledge graph for education. In: IEEE Access vol. 6, pp. 31553–31563 (2018). https://doi.org/10.1109/ACCESS.2018.2839607
Francis, N., et al.: Cypher: an evolving query language for property graphs. In: Proceedings of the 2018 International Conference on Management of Data, pp. 1433–1445, Houston, TX, USA (2018). Association for Computing Machinery. https://doi.org/10.1145/3183713.3190657.
Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. In: Communications of the ACM, vol. 57, no. 10, pp. 78–85 (2014). https://doi.org/10.1145/2629489
Webber, J.: A Programmatic Introduction to Neo4j. In: Proceedings of the 3rd Annual Conference on Systems, Programming, and Applications: Software for Humanity, pp. 217–218, Tucson, Arizona, USA (2012). Association for Computing Machinery. https://doi.org/10.1145/2384716.2384777.
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186, Minneapolis, Minnesota (2019). Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1423, https://aclanthology.org/N19-1423
Aldhalaan, G., Berri, J.: Supporting curriculum designers in developing balanced outcome-based programs using knowledge graphs. In: International Journal of Advanced Trends in Computer Science and Engineering (2023). https://api.semanticscholar.org/CorpusID:264088404
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Gacek, P., Adrian, W.T. (2025). Automatic Curriculum Cohesion Analysis Based on Knowledge Graphs. In: Bruno, P., Calimeri, F., Cauteruccio, F., Terracina, G. (eds) Hybrid Models for Coupling Deductive and Inductive Reasoning. HYDRA 2024. Communications in Computer and Information Science, vol 2492. Springer, Cham. https://doi.org/10.1007/978-3-031-89366-7_6
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