MAS-KCL: Knowledge component graph structure learning with large language model-based agentic workflow
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910956992331776 |
|---|---|
| author | Jiang, Yuan-Hao Tang, Kezong Chen, Zi-Wei Wei, Yuang Liu, Tian-Yi Wu, Jiayi |
| author_facet | Jiang, Yuan-Hao Tang, Kezong Chen, Zi-Wei Wei, Yuang Liu, Tian-Yi Wu, Jiayi |
| contents | Knowledge components (KCs) are the fundamental units of knowledge in the field of education. A KC graph illustrates the relationships and dependencies between KCs. An accurate KC graph can assist educators in identifying the root causes of learners' poor performance on specific KCs, thereby enabling targeted instructional interventions. To achieve this, we have developed a KC graph structure learning algorithm, named MAS-KCL, which employs a multi-agent system driven by large language models for adaptive modification and optimization of the KC graph. Additionally, a bidirectional feedback mechanism is integrated into the algorithm, where AI agents leverage this mechanism to assess the value of edges within the KC graph and adjust the distribution of generation probabilities for different edges, thereby accelerating the efficiency of structure learning. We applied the proposed algorithm to 5 synthetic datasets and 4 real-world educational datasets, and experimental results validate its effectiveness in learning path recognition. By accurately identifying learners' learning paths, teachers are able to design more comprehensive learning plans, enabling learners to achieve their educational goals more effectively, thus promoting the sustainable development of education. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14126 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MAS-KCL: Knowledge component graph structure learning with large language model-based agentic workflow Jiang, Yuan-Hao Tang, Kezong Chen, Zi-Wei Wei, Yuang Liu, Tian-Yi Wu, Jiayi Machine Learning Computers and Society Human-Computer Interaction Multiagent Systems Knowledge components (KCs) are the fundamental units of knowledge in the field of education. A KC graph illustrates the relationships and dependencies between KCs. An accurate KC graph can assist educators in identifying the root causes of learners' poor performance on specific KCs, thereby enabling targeted instructional interventions. To achieve this, we have developed a KC graph structure learning algorithm, named MAS-KCL, which employs a multi-agent system driven by large language models for adaptive modification and optimization of the KC graph. Additionally, a bidirectional feedback mechanism is integrated into the algorithm, where AI agents leverage this mechanism to assess the value of edges within the KC graph and adjust the distribution of generation probabilities for different edges, thereby accelerating the efficiency of structure learning. We applied the proposed algorithm to 5 synthetic datasets and 4 real-world educational datasets, and experimental results validate its effectiveness in learning path recognition. By accurately identifying learners' learning paths, teachers are able to design more comprehensive learning plans, enabling learners to achieve their educational goals more effectively, thus promoting the sustainable development of education. |
| title | MAS-KCL: Knowledge component graph structure learning with large language model-based agentic workflow |
| topic | Machine Learning Computers and Society Human-Computer Interaction Multiagent Systems |
| url | https://arxiv.org/abs/2505.14126 |