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Exploration on the Reform of Curriculum-Based Ideological and Political Education Teaching Resources in Digital Transformation

Jul 2026 · Education Insights · Vol 3, pp. 123-131 · 0 citations · 12 references

TL;DR

A dual-drive ontology model is proposed that integrates disciplinary knowledge with ideological and political dimensions and significantly reduces students' cognitive load during learning, confirming the model's efficacy in enhancing the quality of ideological and political education through digital transformation.

Abstract

The digital transformation of education presents unprecedented opportunities for advancing the ideological and political construction of curricula across higher education institutions in the western regions of China. However, current pedagogical practice continues to encounter several substantive difficulties, including the fragmentation of ideological and political teaching resources, the superficial integration of professional content with ideological and political elements, and the absence of objective, data-driven mechanisms for evaluating the internalization of values among learners. To address these critical challenges, this study introduces knowledge graph technology and ontology construction methodology, proposing a dual-drive ontology model that integrates disciplinary knowledge with ideological and political dimensions. This model aims to achieve semantic representation and deep structural association of implicit ideological and political elements embedded within course materials. The research establishes systematic labeling norms for ideological and political elements through the Delphi method, constructs a domain knowledge graph encompassing core legal concepts and value dimensions, and designs a semantic association mechanism for integrating multi-source educational resources. On this foundation, a sixteen-week quasi-experimental study was conducted to evaluate the effectiveness of the proposed model. The results demonstrate that the model significantly reduces students' cognitive load during learning. Furthermore, the experimental group outperformed the control group in both knowledge transfer ability and value recognition, confirming the model's efficacy in enhancing the quality of ideological and political education through digital transformation.

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