This study adopted a novel text mining (TM) technique in curriculum studies to analyze the multi-layered South Korean curriculum document (CD) system from 2012 to 2017. A total of 716 CDs from the national, regional, and school levels corresponding to 23.4 million Korean characters were examined through keyword frequency analysis, topic modeling with Latent Dirichlet Allocation (LDA), construction of text network with clusters, and document vectorization. The results reveal the decentralized nature of each CD layer; i.e., the regional CD (RCD) mediates the national CD (NCD) to the school CD (SCD), and the SCD plays a key role in the implementation of diverse student-centered learning experiences. However, when the similarity (reversely distance) between the average CD vectors of each layer was examined, the SCD showed a constant trend of recentralization toward NCD. This study suggests that the adoption of TM techniques in curriculum studies would enhance the methodological progress of this field in accordance with conventional CD understanding, revealing novel aspects with reproducible and consistent results from a much larger dataset.

这项研究在课程研究中采用了一种新颖的文本挖掘（TM）技术来分析2012年至2017年的多层韩国课程文档（CD）系统。来自国家，地区和学校各级的716张CD对应于23.4通过关键字频率分析，使用潜在狄利克雷分配（LDA）进行主题建模，构建具有聚类的文本网络以及文档矢量化等方法，检查了百万韩语字符。结果揭示了每个CD层的分散性质；例如，区域CD（RCD）将国家CD（NCD）中介到学校CD（SCD），并且SCD在实施以学生为中心的多样化学习体验中扮演着关键角色。但是，在检查各层的平均CD向量之间的相似度（反向距离）时，SCD向NCD展示了重新集中化的持续趋势。这项研究表明，按照传统的CD理解，在课程研究中采用TM技术将增强该领域的方法学进展，从而揭示具有更大数据集的可重现性和一致性的新颖方面。