MAS-KCL: Knowledge component graph structure learning with large language model-based agentic workflow
This work addresses the need for targeted instructional interventions in education by improving KC graph accuracy, though it appears incremental as it builds on existing structure learning with LLM-based agents.
The paper tackles the problem of learning accurate knowledge component (KC) graphs to identify root causes of poor learner performance, resulting in a method that effectively recognizes learning paths on synthetic and real-world educational datasets.
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.