A Knowledge Graph-Based Approach to Enhancing Grounded Theory Research

Traditional grounded theory (GT) heavily depends on researchers' cognitive abilities, making theoretical construction highly subjective and inconsistent. This reliance on individual expertise results in significant variations in research quality, limiting the interpretability, reproducibility, and scalability of GT. Recent advancements in knowledge extraction technologies have demonstrated the potential to bridge this gap by enabling non-expert researchers to achieve expert-level analytical capabilities. To address these challenges, this study proposes a computational grounded theory method based on knowledge graphs (CGT-KG), integrating knowledge graph techniques to enhance theory construction. By systematically representing multi-dimensional concept-theory relationships, CGT-KG reduces subjectivity, improves transparency, and strengthens theoretical validation. Taking the construction of a psychological structure model for efficient mathematics learning as a case, the paper verifies that the knowledge hypergraph strengthens grounded theory in three aspects: ① multi-dimensional concept-theory relationship, the knowledge hypergraph overcomes the limitations of binary relationships, making theory construction richer and more structured, and the visualized results promote knowledge sharing among researchers; ② enhanced theory verification, the color coding of nodes and edges provides a new perspective for theoretical saturation test and theoretical sampling; ③ automatic hypothesis discovery, a new theory generation framework based on analogical reasoning is proposed, making systematic exploration of new theories possible.