A Digital Teaching Paradigm: Natural Language Processing Integrates to Teaching Chinese as The Second Language
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Abstract
This research aims to study the integration of Natural Language Processing (NLP) tools in teaching Chinese as a second language (TCSL), a domain of increasing importance due to the global demand for Chinese language proficiency. The primary objective was to assess the effectiveness of NLP tools in improving essential language skills such as vocabulary, grammar, pronunciation, and character recognition. Additionally, the study explored learner engagement and the differential impact of these tools across various demographic factors, including age, language background, and learning style. A mixed-methods approach was adopted, incorporating both quantitative and qualitative research. The quantitative analysis involved 100 students at different proficiency levels who completed pre-and post-tests to gauge improvements in language proficiency. Qualitative data were obtained through interviews with 10 Chinese language teachers, providing insights into user experiences and challenges. Case studies further illustrated the practical application of NLP tools in real-world settings. The findings demonstrated significant improvements in all language skills, with NLP-supported learners outperforming those relying on traditional methods. Positive learner engagement was observed, though challenges, such as understanding cultural nuances (e.g., the Chinese idiom "吃亏" (chī kuī), which means "suffer a disadvantage"), and technical issues were noted. The study concluded that NLP tools are a valuable addition to TCSL, offering enhanced learning outcomes and flexibility for diverse learner needs while identifying areas for Improvement, particularly in addressing cultural content and technical refinement. This research contributes to language learning, NLP, and educational technology, advancing technology integration in language education.
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Alqahtani, T., Badreldin, H. A., Alrashed, M., Alshaya, A. I., Alghamdi, S. S., bin Saleh, K., Alowais, S. A., Alshaya, O. A., Rahman, I., Al Yami, M. S., & Albekairy, A. M. (2023). The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Research in Social and Administrative Pharmacy, 19(8), 1236-1242. https://doi.org/10.1016/j.sapharm.2023.05.016
Cleveland, M. (2022). Globalization and global consumer culture: The fragmentation, fortification, substitution and transmutation of social identities. In I. Katzarska-Miller & S. Reysen (Eds.), Globalized identities: The impact of globalization on self and identity (pp. 71-105). Springer International Publishing. https://doi.org/10.1007/978-3-031-04644-5_4
Colpitts, B. D. F., Smith, M. D., & McCurrach, D. P. (2021). Enhancing the digital capacity of EFL programs in the age of COVID-19: The ecological perspective in Japanese higher education. Interactive Technology and Smart Education, 18(2), 158-174. https://doi.org/10.1108/ITSE-08-2020-0123
Deng, J. (2022). Beyond self-orientalism: Asian masculine landscapes in Chinese and Thai martial arts tourism. Tourist Studies, 23(1), 8-24. https://doi.org/10.1177/14687976221143243
Girju, R. (2023). Understanding lived experience: Bridging artificial intelligence and natural language processing with humanities and social sciences. IOP Conference Series: Materials Science and Engineering, Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the role of digital technologies in education: A review. Sustainable Operations and Computers, 3, 275-285. https://doi.org/10.1016/j.susoc.2022.05.004
Hsiang, T. P., Graham, S., Liu, X., & Zhou, Z. (2023). Teachers’ beliefs and practices in textbook selection and use when teaching Chinese as a second language. Reading and Writing, 36(7), 1651-1684. https://doi.org/10.1007/s11145-022-10336-9
Huang, W., Hew, K. F., & Fryer, L. K. (2022). Chatbots for language learning—Are they really useful? A systematic review of chatbot‐supported language learning. Journal of computer assisted learning, 38(1), 237-257. https://doi.org/10.1111/jcal.12610
Jiang, S., & Wang, D. (2022). A qualitative exploration for the language industry in Chongqing. Insight-Information, 4(2), 26-29. https://doi.org/10.18282/ii.v4i2.534
Jing-Schmidt, Z. (2019). Corpus and computational methods for usage-based Chinese language learning: Toward a professional multilingualism. In X. Lu & B. Chen (Eds.), Computational and corpus approaches to Chinese language learning (pp.13-31). Springer Nature Singapore. https://doi.org/10.1007/978-981-13-3570-9_2
Karakas, A. (2023). Breaking down barriers with artificial intelligence (AI): Cross-cultural communication in foreign language education. In Transforming the Language Teaching Experience in the Age of AI (pp. 215-233). IGI Global. https://doi.org/10.4018/978-1-6684-9893-4.ch012
Khurana, D., Koli, A., Khatter, K., & Singh, S. (2023). Natural language processing: State of the art, current trends and challenges. Multimedia Tools and Applications, 82(3), 3713-3744. https://doi.org/10.1007/s11042-022-13428-4
Lan, S. (2022). Between privileges and precariousness: Remaking whiteness in China's teaching English as a second language industry. American Anthropologist, 124(1), 118-129. https://doi.org/10.1111/aman.13657
Li, L., Geissinger, J., Ingram, W. A., & Fox, E. A. (2020). Teaching natural language processing through big data text summarization with problem-based learning. Data and Information Management, 4(1), 18-43. https://doi.org/10.2478/dim-2020-0003
Liu, Y. (2023). A cognition perspective revisit on teaching of pictophonetic characters: A case study on the new HSK level 3 common verbs. Journal of Chinese Writing Systems, 7(2), 128-146. https://doi.org/10.1177/25138502231173723
Neto, A. F., Bezerra, B. L., & Toselli, A. H. (2020). Towards the natural language processing as spelling correction for offline handwritten text recognition systems. Applied Sciences, 10(21), 7711.
Ovcharuk, O., Ivaniuk, I., Soroko, N., Gritsenchuk, O., & Kravchyna, O. (2020). The use of digital learning tools in the teachers’ professional activities to ensure sustainable development and democratization of education in European countries. https://doi.org/10.1051/e3sconf/202016610019
Shaik, T., Tao, X., Li, Y., Dann, C., McDonald, J., Redmond, P., & Galligan, L. (2022). A review of the trends and challenges in adopting natural language processing methods for education feedback analysis. IEEE Access, 10, 56720-56739. https://doi.org/10.1109/ACCESS.2022.3177752
Tedre, M., Toivonen, T., Kahila, J., Vartiainen, H., Valtonen, T., Jormanainen, I., & Pears, A. (2021). Teaching machine learning in K–12 classroom: Pedagogical and technological trajectories for artificial intelligence education. IEEE Access, 9, 110558-110572. https://doi.org/10.1109/ACCESS.2021.3097962
Xie, H., Chu, H.-C., Hwang, G.-J., & Wang, C.-C. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education, 140, 103599. https://doi.org/10.1016/j.compedu.2019.103599
Yang, W., & Li, H. (2022). Curriculum hybridization and cultural glocalization: A scoping review of international research on early childhood curriculum in China and Singapore. ECNU Review of Education, 5(2), 299-327. https://doi.org/10.1177/20965311221092036
Zhou, W., & Li, X. (2022). Investigation of a Chinese character writing app: Learners' perspectives. Knowledge Management & E-Learning, 14(1), 15-29. https://doi.org/10.34105/j.kmel.2022.14.002