한국어 학습자의 실제적, 자연적 언어 습득 메커니즘 분석 및 통합 교수-학습 모델 연구

Authors

  • Yoon Dongkyeon Uzbekistan State World Languages Universit Oriental Philology Faculty Faculty of Korean Philology 2025년 9월 29일 Author

Keywords:

한국어 교육, 제2 언어 습득, 과제 중심 교수법, 입력-출력 균형, 교정적 피드백, 언어 내면화

Abstract

본 연구는 한국어 학습자의 실제적 언어 내면화 메커니즘을 규명하고 통합적 교수-학습 모델을 제안한다. Krashen의 입력 가설, Swain의 출력 가설, Long의 상호작용 가설을 종합하여 한국어 교육 맥락에서 분석하였다. 실증 연구 결과, 학습자는 조사와 종결 어미에서 높은 오류 빈도를 보이며(Jin, 2021; Park, 2007), 효과적인 피드백 전략이 내면화 성공률을 결정한다. 과제 중심 교수법(TBLT)이 전통적 PPP 모델보다 유창성과 복잡성 발달에 우수하며, 입력-출력 균형 설계가 필수적이다. 초급 단계에서는 사용 기반 접근과 내러티브 구성, 중급 단계에서는 TBLT 기반 복합 기능 통합과 협상 피드백을 강조하였다. 본 연구는 SLA 이론과 한국어 교육 실천을 연결하는 이론적·실천적 기반을 제공한다.

References

주요 제2언어 습득 이론 문헌

1. Krashen, S. D. (1982). Principles and practice in second language acquisition. Pergamon Press.

2. Krashen, S. D. (1985). The input hypothesis: Issues and implications. Longman.

3. Krashen, S. D., & Scarcella, R. C. (1978). On routines and patterns in language acquisition and performance. Language Learning, 28(2), 283-300.

4. Long, M. H. (1996). The role of the linguistic environment in second language acquisition. In W. C. Ritchie & T. K. Bhatia (Eds.), Handbook of second language acquisition (pp. 413-468). Academic Press.

5. Long, M. H. (1983). Native speaker/non-native speaker conversation and the negotiation of comprehensible input. Applied Linguistics, 4(2), 126-141.

6. Swain, M. (1985). Communicative competence: Some roles of comprehensible input and comprehensible output in its development. In S. Gass & C. Madden (Eds.), Input in second language acquisition (pp. 235-253). Newbury House.

7. Swain, M. (1995). Three functions of output in second language learning. In G. Cook & B. Seidlhofer (Eds.), Principle and practice in applied linguistics: Studies in honour of H. G. Widdowson (pp. 125-144). Oxford University Press.

8. Pica, T. (1987). Second-language acquisition, social interaction, and the classroom. Applied Linguistics, 8(1), 3-21.

9. Lyster, R., & Ranta, L. (1997). Corrective feedback and learner uptake: Negotiation of form in communicative classrooms. Studies in Second Language Acquisition, 19(1), 37-66.

한국어 교육 및 학습자 오류 분석 연구

10. Jin, Y. (2021). Corpus-informed application based on Korean Learners' Corpus: substitution errors of topic and nominative markers. Asian-Pacific Journal of Second and Foreign Language Education, 6(1), 1-17. https://doi.org/10.1186/s40862-021-00112-7

11. Park, S. K. (2007). Error analysis of postposition in learner's corpus. Teaching Korean as a Foreign Language, 27, 543-570.

12. Ellis, R. (2003). Task-based language learning and teaching. Oxford University Press.

13. Willis, J. (1996). A framework for task-based learning. Longman.

14. Skehan, P. (1998). A cognitive approach to language learning. Oxford University Press.

한국어 교육과정 및 교수법 연구

15. 국립국어원. (2017). 한국어 표준 교육과정. 국립국어원.

16. Lee, S. H. (2022). Verb conjugation errors by learners of Korean. Korean Linguistics, 25(3), 287-315.

과제 중심 교수법 및 PPP 모델 비교 연구

17. Samuda, V., & Bygate, M. (2008). Tasks in second language learning. Palgrave Macmillan.

18. Robinson, P. (2011). Second language task complexity, the cognition hypothesis, second language task-based learning, and performance. In P. Robinson (Ed.), Second language task complexity: Researching the cognition hypothesis of language learning and performance (pp. 3-37). John Benjamins.

19. Bruner, J. (1986). Actual minds, possible worlds. Harvard University Press.

20. Foster, P., & Skehan, P. (1996). The influence of planning and task type on second language performance. Studies in Second Language Acquisition, 18(3), 299-323.

21. Barkhuizen, G. (2011). Narrative knowledging in TESOL. TESOL Quarterly, 45(3), 391-414.

AI 및 기술 활용 언어 교육 연구

22. Chapelle, C. A., & Sauro, S. (Eds.). (2017). The handbook of technology and second language teaching and learning. Wiley-Blackwell.

23. Heift, T., & Schulze, M. (2007). Errors and intelligence in computer-assisted language learning: Parsers and pedagogues. Routledge.

24. Chen, X., Zou, D., Cheng, G., & Xie, H. (2022). Detecting and predicting learner engagement in mobile-assisted language learning: A machine learning approach. Computers & Education, 178, 204-219.

25. Lan, Y. J. (2020). Immersive technology-enhanced learning: A perspective of embodied cognition. British Journal of Educational Technology, 51(4), 1387-1404.

26. Wang, Y. F., & Petrina, S. (2013). Using learning analytics to understand the design of an intelligent language tutor – Chatbot Lucy. International Journal of Advanced Computer Science and Applications, 4(11), 124-131.

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Published

2025-10-19