About this Research Topic
With the rapid advances in computational linguistics, text mining and analysis, approaches have been increasingly used in the design of educational applications as well as in empirical research in second language acquisition and technology-assisted language learning and testing, yielding fruitful results in various domains of language teaching and learning. Meanwhile, the COVID-19 pandemic has brought about an unprecedented expansion of remote learning, making educational applications powered by natural language processing technology more relevant in second language teaching, learning, and assessment contexts than ever before. In light of these developments, our Research Topic highlights interdisciplinary research at the interface of computational linguistics and second language acquisition that contributes to bridging the latest technological advances with new needs of second language teachers and learners.
Although many achievements have been witnessed in this interdisciplinary research field, there are still several important questions that need to be addressed or discussed, including but not limited to:
- How can recent advances in deep learning in the fields of computational linguistics (CL) and natural language processing (NLP) be employed to benefit second language teaching, learning, and assessment?
- Individual variation and variability have been increasingly highlighted in current second language acquisition theories. How can CL/NLP methods be used to enable the design of innovative language learning resources and applications to facilitate personalized and adaptive language learning or testing?
- The global pandemic has posed new challenges for second language teaching, learning, and assessment. How can CL/NLP methods be systematically employed to help address such challenges?
This Research Topic invites original research articles and systematic review articles on (but not limited to) the following themes:
- Corpora for second language learning, teaching, or testing
- Linguistic analysis tools at multiple levels (e.g., lexical, phrasal, phraseological, syntactic, and discourse)
- Automated scoring of writing or speaking responses
- Grammatical error detection and correction
- Text adaptation for learning, teaching and/or testing purposes
- Data mining of educational texts
- Intelligent tutoring systems
- Innovative applications for second language learners, teachers and/or test developers
- Systems that detect and adapt to learners’ cognitive or emotional states
- Combination of computational models and neuropsychological data for second language learning
Although many achievements have been witnessed in this interdisciplinary research field, there are still several important questions that need to be addressed or discussed, including but not limited to:
- How can recent advances in deep learning in the fields of computational linguistics (CL) and natural language processing (NLP) be employed to benefit second language teaching, learning, and assessment?
- Individual variation and variability have been increasingly highlighted in current second language acquisition theories. How can CL/NLP methods be used to enable the design of innovative language learning resources and applications to facilitate personalized and adaptive language learning or testing?
- The global pandemic has posed new challenges for second language teaching, learning, and assessment. How can CL/NLP methods be systematically employed to help address such challenges?
This Research Topic invites original research articles and systematic review articles on (but not limited to) the following themes:
- Corpora for second language learning, teaching, or testing
- Linguistic analysis tools at multiple levels (e.g., lexical, phrasal, phraseological, syntactic, and discourse)
- Automated scoring of writing or speaking responses
- Grammatical error detection and correction
- Text adaptation for learning, teaching and/or testing purposes
- Data mining of educational texts
- Intelligent tutoring systems
- Innovative applications for second language learners, teachers and/or test developers
- Systems that detect and adapt to learners’ cognitive or emotional states
- Combination of computational models and neuropsychological data for second language learning
Keywords: computational linguistics, natural language processing, corpus, computer assisted language learning, educational applications, second language acquisition
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
