Main Article Content
Abstract
This study employs computational linguistic methods to compare continuing education regulatory frameworks in Korea and China via systematic text comparison. Applying algorithmic methods like thematic decomposition, sentiment analysis, and semantic correlation measures to the government reports of the two countries, we develop an innovative cross-cultural assessment framework. The analytical process integrates entity extraction, vector-based semantic mapping, and quantitative content mining in order to identify regulatory patterns and efficacy signals within policy documents from 2010 to 2023. Empirical results show notable divergence in administrative priorities, discursive frameworks, and governance styles, with Chinese regulations showing centralized coordination characteristics in contrast to Korea's market-responsive institutions. The research adds to the policy analysis literature by demonstrating computational methodologies' ability to identify obscured administrative priorities and operational nuances outside of conventional analytical grasp. The contribution enhances computational policy studies through the creation of replicable, unbiased processes for comparative cross-country regulation, inferring useful implications for administrators and researchers constructing streamlined continuing education models. The research confirms computational linguistics as an effective means of evidence-based policy analysis in multilingual settings.
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References
- Tang M, Cheng L. The evolving landscape of continuing education policies in East Asia: A comparative analysis[J]. International Journal of Lifelong Education, 2022, 41(3): 285-302.
- UNESCO Institute for Lifelong Learning. Embracing a culture of lifelong learning: Contribution to the Futures of Education initiative [R]. Hamburg: UIL, 2020. ISBN: 978-92-820-1243-7. Available from: https://unesdoc.unesco.org/ark:/48223/pf0000374112 [Accessed April 2023]. Document code: UIL/2020/PI/H/14
- Kim J H, Park S Y. Digital transformation in lifelong learning: Korea's continuing education policy reforms 2018-2023[J]. Asia Pacific Education Review, 2023, 24(2): 189-205.
- Xiao Y, Zhang L. Comparative policy analysis methods in cross-cultural contexts: Challenges and solutions[J]. Policy Sciences, 2021, 54(3): 567-589.
- Lazer D, Pentland A, Watts D J, et al. Computational social science: Obstacles and opportunities[J]. Science, 2020, 369(6507): 1060-1062.
- Wang Q, Li H. Translation challenges in cross-national policy comparison: A methodological perspective[J]. Journal of Comparative Policy Analysis, 2022, 24(4): 412-430.
- Chen H, Zhang W. Natural language processing applications in policy research: A systematic review[J]. Government Information Quarterly, 2023, 40(2): 101725.
- Roberts M E, Stewart B M, Tingley D. stm: An R package for structural topic models[J]. Journal of Statistical Software, 2019, 91(2): 1-40.
- Yang L, Wu J. Policy evaluation theories in the digital age: Integrating computational methods[J]. Public Administration Review, 2022, 82(5): 912-927.
- Lee S K, Kim H J. Evolution of continuing education policies in Korea: From democratization to digitalization[J]. Adult Education Quarterly, 2021, 71(4): 332-350.
- Zhang Y, Liu B. Sentiment analysis of policy documents: A multilingual approach[J]. ACM Transactions on Asian and Low-Resource Language Information Processing, 2023, 22(3): 1-23.
- Wang X, Zhou Y. Computational text analysis in policy studies: Methods and applications[J]. Policy Studies Journal, 2022, 50(2): 234-256.
- Han J, Lee K. Cross-cultural policy transfer in East Asia: The case of lifelong learning[J]. Asia Pacific Journal of Education, 2020, 40(4): 485-501.
- Mikolov T, Chen K, Corrado G, et al. Distributed representations of words and phrases and their compositionality[C]//Advances in Neural Information Processing Systems, 2019, 32: 3111-3119.
- Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of NAACL-HLT, 2019: 4171-4186.
- Liu Y, Ott M, Goyal N, et al. RoBERTa: A robustly optimized BERT pretraining approach[J]. arXiv preprint arXiv:1907.11692, 2019.
- Sun C, Qiu X, Xu Y, et al. How to fine-tune BERT for text classification?[C]//China National Conference on Chinese Computational Linguistics. Springer, 2019: 194-206.
- Anastasopoulos L J, Badirli S, Lopes G, et al. Machine learning for public policy: Do we need to sacrifice interpretability to achieve fairness?[J]. The American Review of Public Administration, 2022, 52(4): 271-286.
- Li M, Zhou L. Policy document analysis using transformer-based models: A comparative study[J]. Digital Government: Research and Practice, 2023, 4(1): 1-18.
- Kim Y, Lee H. Cross-lingual sentiment analysis for policy evaluation: Chinese and Korean contexts[J]. Computer Speech & Language, 2022, 71: 101275.
- Zhang X, Zhao J, LeCun Y. Character-level convolutional networks for text classification[C]//Advances in Neural Information Processing Systems, 2020, 33: 649-657.
- Grootendorst M. BERTopic: Neural topic modeling with a class-based TF-IDF procedure[J]. arXiv preprint arXiv:2203.05794, 2022.
- Reimers N, Gurevych I. Sentence-BERT: Sentence embeddings using Siamese BERT-networks[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, 2019: 3982-3992.
- Park S, Kim J. Structural topic modeling for policy analysis: Applications in East Asian education policies[J]. Journal of Educational Policy Analysis, 2021, 43(2): 178-202.
- Li W, Zhang H. China's lifelong education policy evolution: A critical discourse analysis (1978-2020)[J]. International Journal of Educational Development, 2021, 80: 102303.
- Ministry of Education of China. National Medium and Long-term Education Reform and Development Plan (2010-2020)[R]. Beijing: MOE, 2020.
- Zhang X, Wang Y. Multi-level governance in China's continuing education: Policy analysis and implications[J]. Chinese Education & Society, 2022, 55(1-2): 45-67.
- Kim K H, Park H J. The development of lifelong education in Korea: Historical perspectives and current challenges[J]. International Review of Education, 2020, 66(5-6): 661-683.
- Ministry of Education of Korea. The 4th Basic Plan for Lifelong Education Promotion (2018-2022) [R]. Seoul: Ministry of Education, 2019. Policy Document No.: MOE-2019-LLL-456. Available from: https://english.moe.go.kr/sub/infoRenewal.do?m=0307&page=0307&s=english [Accessed March 2023].
- Cho J Y, Lee S M. Digital transformation in Korean continuing education: Policy responses and outcomes[J]. Educational Technology Research and Development, 2023, 71(3): 1189-1212.
- Dolowitz D P, Marsh D. Learning from abroad: The role of policy transfer in contemporary policy-making[J]. Governance, 2020, 33(2): 313-329.
- Zhou Y, Chen L. Deep learning for policy text analysis: Current applications and future directions[J]. Information Processing & Management, 2023, 60(2): 103189.
References
Tang M, Cheng L. The evolving landscape of continuing education policies in East Asia: A comparative analysis[J]. International Journal of Lifelong Education, 2022, 41(3): 285-302.
UNESCO Institute for Lifelong Learning. Embracing a culture of lifelong learning: Contribution to the Futures of Education initiative [R]. Hamburg: UIL, 2020. ISBN: 978-92-820-1243-7. Available from: https://unesdoc.unesco.org/ark:/48223/pf0000374112 [Accessed April 2023]. Document code: UIL/2020/PI/H/14
Kim J H, Park S Y. Digital transformation in lifelong learning: Korea's continuing education policy reforms 2018-2023[J]. Asia Pacific Education Review, 2023, 24(2): 189-205.
Xiao Y, Zhang L. Comparative policy analysis methods in cross-cultural contexts: Challenges and solutions[J]. Policy Sciences, 2021, 54(3): 567-589.
Lazer D, Pentland A, Watts D J, et al. Computational social science: Obstacles and opportunities[J]. Science, 2020, 369(6507): 1060-1062.
Wang Q, Li H. Translation challenges in cross-national policy comparison: A methodological perspective[J]. Journal of Comparative Policy Analysis, 2022, 24(4): 412-430.
Chen H, Zhang W. Natural language processing applications in policy research: A systematic review[J]. Government Information Quarterly, 2023, 40(2): 101725.
Roberts M E, Stewart B M, Tingley D. stm: An R package for structural topic models[J]. Journal of Statistical Software, 2019, 91(2): 1-40.
Yang L, Wu J. Policy evaluation theories in the digital age: Integrating computational methods[J]. Public Administration Review, 2022, 82(5): 912-927.
Lee S K, Kim H J. Evolution of continuing education policies in Korea: From democratization to digitalization[J]. Adult Education Quarterly, 2021, 71(4): 332-350.
Zhang Y, Liu B. Sentiment analysis of policy documents: A multilingual approach[J]. ACM Transactions on Asian and Low-Resource Language Information Processing, 2023, 22(3): 1-23.
Wang X, Zhou Y. Computational text analysis in policy studies: Methods and applications[J]. Policy Studies Journal, 2022, 50(2): 234-256.
Han J, Lee K. Cross-cultural policy transfer in East Asia: The case of lifelong learning[J]. Asia Pacific Journal of Education, 2020, 40(4): 485-501.
Mikolov T, Chen K, Corrado G, et al. Distributed representations of words and phrases and their compositionality[C]//Advances in Neural Information Processing Systems, 2019, 32: 3111-3119.
Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of NAACL-HLT, 2019: 4171-4186.
Liu Y, Ott M, Goyal N, et al. RoBERTa: A robustly optimized BERT pretraining approach[J]. arXiv preprint arXiv:1907.11692, 2019.
Sun C, Qiu X, Xu Y, et al. How to fine-tune BERT for text classification?[C]//China National Conference on Chinese Computational Linguistics. Springer, 2019: 194-206.
Anastasopoulos L J, Badirli S, Lopes G, et al. Machine learning for public policy: Do we need to sacrifice interpretability to achieve fairness?[J]. The American Review of Public Administration, 2022, 52(4): 271-286.
Li M, Zhou L. Policy document analysis using transformer-based models: A comparative study[J]. Digital Government: Research and Practice, 2023, 4(1): 1-18.
Kim Y, Lee H. Cross-lingual sentiment analysis for policy evaluation: Chinese and Korean contexts[J]. Computer Speech & Language, 2022, 71: 101275.
Zhang X, Zhao J, LeCun Y. Character-level convolutional networks for text classification[C]//Advances in Neural Information Processing Systems, 2020, 33: 649-657.
Grootendorst M. BERTopic: Neural topic modeling with a class-based TF-IDF procedure[J]. arXiv preprint arXiv:2203.05794, 2022.
Reimers N, Gurevych I. Sentence-BERT: Sentence embeddings using Siamese BERT-networks[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, 2019: 3982-3992.
Park S, Kim J. Structural topic modeling for policy analysis: Applications in East Asian education policies[J]. Journal of Educational Policy Analysis, 2021, 43(2): 178-202.
Li W, Zhang H. China's lifelong education policy evolution: A critical discourse analysis (1978-2020)[J]. International Journal of Educational Development, 2021, 80: 102303.
Ministry of Education of China. National Medium and Long-term Education Reform and Development Plan (2010-2020)[R]. Beijing: MOE, 2020.
Zhang X, Wang Y. Multi-level governance in China's continuing education: Policy analysis and implications[J]. Chinese Education & Society, 2022, 55(1-2): 45-67.
Kim K H, Park H J. The development of lifelong education in Korea: Historical perspectives and current challenges[J]. International Review of Education, 2020, 66(5-6): 661-683.
Ministry of Education of Korea. The 4th Basic Plan for Lifelong Education Promotion (2018-2022) [R]. Seoul: Ministry of Education, 2019. Policy Document No.: MOE-2019-LLL-456. Available from: https://english.moe.go.kr/sub/infoRenewal.do?m=0307&page=0307&s=english [Accessed March 2023].
Cho J Y, Lee S M. Digital transformation in Korean continuing education: Policy responses and outcomes[J]. Educational Technology Research and Development, 2023, 71(3): 1189-1212.
Dolowitz D P, Marsh D. Learning from abroad: The role of policy transfer in contemporary policy-making[J]. Governance, 2020, 33(2): 313-329.
Zhou Y, Chen L. Deep learning for policy text analysis: Current applications and future directions[J]. Information Processing & Management, 2023, 60(2): 103189.