Main Article Content

Abstract

Background. Program Learning Outcomes (PLOs) in undergraduate degrees describe broad, macro-level competencies. Online job advertisements do something else. They list narrow tasks and named software tools in fine detail. Reading literal keyword overlap as graduate competence can misdiagnose curriculum gaps.


Objective. To quantify how explicitly industry competency concepts appear in the PLO documents of Indonesian undergraduate Data Science programs, using a bilingual mapping to the European Skills, Competences, Qualifications and Occupations (ESCO v1.2.0) taxonomy and the Indonesian National Work Competency Standards (SKKNI Data Science).


Methods. An exploratory computational content analysis covered 485 deduplicated Indonesian job advertisements collected in March–April 2025: JobStreet (271 descriptions across 210 employer clusters) and Glints (214 skill-tag postings). These were set against 64 PLO statements from six universities (46 in four dated primary curricula). Twenty-five operational domains were formed from 21 SKKNI units (consolidated into 17 workflow domains) and eight ESCO skill groups. Uncertainty was estimated with 4,000 employer-cluster bootstrap replications. Role divergence was tested with Fisher's exact tests and Benjamini–Hochberg false discovery rate (FDR) correction.


Results. Employer demand led with database and digital storage (68.6%), computer programming (62.4%), teamwork (60.9%), and professional communication (60.5%). Demand-weighted PLO coverage ranged from 52.5% (95% CI: 50.9–54.1%) to 73.8% (95% CI: 72.2–75.6%). Core analytical domains appeared in 4/4 primary curricula. Model evaluation (18.1%) and deployment (13.3%) did not (0/4 explicit detection).


Conclusion. Observed gaps are consistent with textual granularity asymmetry rather than graduate incompetence, an explanation not tested here, and offer curriculum committees an empirical agenda for hierarchical alignment.

Keywords

Content analysis Curriculum alignment Data science education ESCO SKKNI

Article Details

How to Cite
Sugandhi, I., & Serikova, A. (2026). Aligning Undergraduate Data Science Program Learning Outcomes with Indonesian Industry Competency De-mands: An Exploratory Bilingual Content Analysis Based on ESCO and SKKNI Frameworks. Enigma in Education, 4(2), 32-49. https://doi.org/10.61996/edu.v4i2.134

References

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