Daejeon, July 27–31
The normalization of diplomatic relations between Korea and China in 1992 served as a catalyst for the rapid proliferation of China-related departments across Korean universities. By the early 2000s, numerous institutions had established new programs and overhauled their curricula under the explicit banner of training "China specialists" — a goal firmly anchored in the paradigm of Chinese Studies (Area Studies) (Jeong 2000; Park 2004). Since the foundational critiques of Park (2004) and Lee (2005), however, scholars have consistently argued that curricula remain disproportionately oriented toward the transmission of traditional Chinese language and literature studies, rather than fostering the interdisciplinary, practice-oriented competencies that Area Studies demands.
The sources of this structural mismatch have been diagnosed from multiple perspectives. Yang (2003) and Jung (2023a) identify two primary factors: a misalignment between formally declared educational objectives and the curricula actually delivered, and a persistent overconcentration of course offerings in linguistics and literary studies. Building on these findings, Yu / Kim (2022) and Jung (2023b) underscore the urgency of a more systematic assessment — one that examines whether China-related programs genuinely adopt "training China specialists" as their central educational mission, and whether their curricular configurations substantively reflect that mission.
Against this backdrop, the present study seeks to move beyond conventional statistical overviews of Chinese Studies programs in Korean higher education. Grounded in Digital Humanities (DH) methodology, it employs a dual-methodological design — combining quantitative text mining with qualitative Knowledge Graph analysis — to elucidate the specific structural patterns and curricular densities of these programs in a multidimensional manner. This undertaking constitutes a foundational step toward the construction of a data-driven "Curricular Map of Chinese Studies Programs" — a scholarly infrastructure designed to render visible the latent architectures of disciplinary knowledge across institutions.
To pursue this dual-methodological agenda, the study first delineated its empirical scope. Drawing on the "2024 Academic Year Admission Quotas by Program Unit for Four-Year Universities" published by the Ministry of Education (2024), a total of 99 China-related programs across 94 universities were identified as the analytical scope. For each program, two categories of data were systematically collected via departmental websites and university bulletins: (a) formally stated departmental educational objectives, and (b) curricular structures, operationalized as complete listings of course offerings.
A text mining analysis was first applied to the educational objectives of these programs. The results revealed a broadly convergent Ideal Graduate Profile across Korean universities, which can be synthesized as follows: "a global leader (China specialist) with a comprehensive perspective on Chinese language, culture, and society, grounded in humanistic literacy."
To assess whether actual curricular configurations substantiate this declared objective, a total of 4,397 course titles were subjected to preprocessing and then classified into eight domains following the taxonomy established by Yu / Kim (2022): Literature, Linguistics, Chinese Language Practice, Culture, Chinese Studies, Education, Classical Chinese Characters / Classical Chinese, and Others. At the aggregate level, the analysis confirmed a surface-level correspondence between stated objectives and curricular composition, particularly with respect to the diversity and proportional distribution of course titles across these domains.
This macro-level quantitative approach, however, exposes a significant methodological limitation. While it effectively captures overall tendencies across the population of China-related programs, it is inherently unable to disaggregate and analyze the distinctive curricular characteristics of individual programs — characteristics that are, moreover, undergoing continuous and heterogeneous transformation. It is precisely this limitation that motivates the qualitative turn in the subsequent phase of the study.
To overcome the methodological constraints of the macro-level quantitative analysis identified above and to interrogate the relationship between stated educational objectives and actual curricular structures at a finer granularity, the present study introduces a Knowledge Graph as an additional analytical layer. Prior to graph construction, the dataset was enriched along two axes. First, course description texts were systematically harvested from departmental web resources, supplementing the course-title data employed in the initial phase. Second, a comprehensive set of metadata was integrated for each program, encompassing university founding type, geographic region, founding year of both the institution and the department, the designated year of enrollment for each course, and faculty profiles. The resulting analytical pipeline proceeds in two stages.
Stage 1 — Ontology Design and Data Reclassification. The first stage involves the design of an Ontology whose Schema formally specifies the classes, properties, and relations required to capture the multi-layered structure of the target domain: University → Department → Educational Objectives → Curriculum → Faculty. Guided by this ontological framework, the data undergo a theoretically motivated reclassification. Departments are categorized into six types — Language and Literature, Linguistics, Chinese Studies, Commerce and Trade, Education, and Content Studies — while individual courses are reassigned to six subject areas: Linguistics, Literature, Culture, Society, Technology, and Others. This reclassification departs from the eight-domain taxonomy of Yu / Kim (2022) applied in the preceding phase, reflecting a shift from surface-level categorization toward a structurally informed typology aligned with the relational logic of the Ontology.
Figure {1} Ontology Design
Stage 2 — Graph Database Construction and Interpretive Analysis. In the second stage, the restructured dataset is implemented as a Neo4j Graph Database, translating the Ontology into a computationally traversable knowledge structure. As Jang / Ryu (2021) argue, such a Knowledge Graph constitutes far more than a static repository of data; it provides a foundational environment for the flexible and intuitive exploration of the complex, multi-relational fabric of university educational systems. Operationally, the Ontology-driven data model enables the formulation of Cypher Queries capable of traversing the graph along diverse analytical paths. These include, but are not limited to: (a) assessing the degree of alignment between declared educational objectives and delivered curricula; (b) conducting comparative analyses of curricular configurations across structurally similar departments; (c) mapping correspondences between faculty research specializations and course offerings; and (d) performing year-level analyses of curricular sequencing and density. In this manner, the Knowledge Graph transforms the compiled dataset into a qualitative, interpretive instrument — one that renders the latent relational architectures of Chinese Studies education amenable to systematic, multidimensional inquiry.
The principal contribution of this study resides in its dual-methodological design, which brings two complementary Digital Humanities instruments to bear on a single empirical domain. Text mining, operating as a quantitative channel, established the macro-level landscape of educational objectives and curricular composition across 99 China-related programs; the Knowledge Graph, functioning as a qualitative, interpretive channel, then enabled a fine-grained, relational exploration of the structural patterns and densities that underlie those aggregate distributions. Together, these two analytical layers afford a multidimensional diagnosis of curricular realities in Chinese Studies programs across Korean higher education — one that neither method could achieve in isolation.
This study moves beyond past conventional macro-level assessments of Chinese Studies education in Korea and offers a data-driven foundation upon which a more consequential question can be pursued: How should China specialists be trained at the level of higher education in an era increasingly shaped by artificial intelligence? By rendering the curricular architectures of Chinese Studies programs computationally visible and analytically tractable, this study contributes to sketching a curricular blueprint that the field of Chinese language and literature studies in Korea will need to develop going forward.