Task Effects on Linguistic Complexity and Accuracy: A Large‐Scale Learner Corpus Analysis Employing Natural Language Processing TechniquesAlexopoulou, T., Michel, M., Murakami, A. & Detmar, M., Jun-2017, In : Language Learning. 67, Suppl. 1, p. 180-208 29 p.
Research output: Contribution to journal › Article › Academic › peer-review
Large-scale learner corpora collected from online language learning platforms, such as the EF-Cambridge Open Language Database (EFCAMDAT), provide opportunities to analyze learner data at an unprecedented scale. However, interpreting the learner language in such corpora requires a precise understanding of tasks: Howdoes the prompt and input of a task and its functional requirements influence task-based linguistic performance?This question is vital for making large-scale task-based corpora fruitful for second language acquisition research. We explore the issue through an analysis of selected tasks in EFCAMDAT and the complexity and accuracy of the language they elicit.
|Number of pages||29|
|Issue number||Suppl. 1|
|Publication status||Published - Jun-2017|
- s learner corpus, task complexity, complexity, accuracy, fluency (CAF), NLP, TBLT, 2ND-LANGUAGE WRITING RESEARCH, SYNTACTIC COMPLEXITY, FOREIGN-LANGUAGE, ACQUISITION, PERFORMANCE, FLUENCY, DISCOURSE, ENGLISH, FRENCH