Abstract
While there have been a considerable number of corpus-based studies informing the content of teaching materials, direct explorations of corpora by teachers to adapt source texts (i.e., data-driven adapting) for classroom teaching remain a largely unexplored area. This chapter examines how teachers adapt new texts in a more comprehensible manner for L2 Chinese learners using an online system, Chi-Editor. Chi-Editor was developed to automatically assess text complexity and tag Chinese words and sentences for text simplification purposes. The evaluation of a text in terms of its level of difficulty and annotation of difficult words and long sentences in the text are produced based on the data mining of linguistic features from a corpus of roughly 350 widely-used textbooks, selected from an anthology of L2 Chinese teaching materials and packages produced by over 1000 publishers around the world. To investigate both the process and outcome of data-driven adapting using Chi-Editor, a case study was conducted, involving a team of teachers working on the adaptation of texts. Results are discussed in terms of the effectiveness of the data-driven adapting practices by teachers in a classroom setting. Overall, the results contribute strong evidence that teachers can learn and benefit from data-driven adapting and support the notion that corpus data, including linguistic features, can be employed to facilitate the text simplification process. Implications are also given for integrating the data-driven adapting process into regular teacher-prepared L2 Chinese materials for classroom teaching.
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Acknowledgements
This research was supported by the Fundamental Research Funds for the Central Universities (16wkpy09). We are indebted to Dr. Xiaofei Lu, Dr. Baichuan Li, and Dr. Le Wang for their suggestions and feedback on earlier drafts of the chapter.
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Bo, W., Chen, J., Guo, K., Jin, T. (2019). Data-Driven Adapting for Fine-Tuning Chinese Teaching Materials: Using Corpora as Benchmarks. In: Lu, X., Chen, B. (eds) Computational and Corpus Approaches to Chinese Language Learning. Chinese Language Learning Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-13-3570-9_6
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