TAL Journal: Special issue NLP for Learning and Teaching

2016 Volume 57 Number 3

Direction: Georges Antoniadis & Piet Desmet

Foreign Language Learning and Teaching is one of the fields where the introduction of information and communication technologies (ICT) has proved particularly fruitful. It is thus no wonder that Computer-Assisted Language Learning (CALL) has been among the first, from the 1960’s, to integrate insights and techniques from Natural Language Processing (NLP) to create intelligent computer-assisted learning environments. Since then, various other fields and disciplines have also incorporated NLP into electronic learning environments to support self-directed learning, blended learning or classroom teaching. NLP has overall contributed to the improvement of learning environments, and to the development of research in the related fields. It has allowed for the improvement of integrated systems, not to say the widening of issues in the related fields.

Today, online learning tools, Massive Open Online Courses (MOOCs), Small Private Online Courses, Computer-Assisted Pronunciation Teaching (CAPT) systems, Computer-Assisted Instruction systems for mathematics, sign language learning applications, or Intelligent Tutoring Systems (ITS), among many others, are heavy “consumers” of NLP, or about to become it.

Integrating NLP into these systems enables to consider, process and reproduce for learning purposes aspects of the content of linguistic data, to create more advanced educational resources, but also to make the communication with the learner more relevant in a teaching context.

The aspects of NLP most frequently involved are analysis of learners’ responses, feedback provision, automated generation of exercises, and the monitoring of learning progress. Other aspects related to learning and teaching also involve NLP, such as plagiarism detection, writing support, use of learner corpora or parallel corpora to detect and resolve errors, or adaptive learning systems integrating ontologies for the associated domains.

The contribution of NLP to these systems is generally regarded as positive. It must be recognized, however, that only a handful of such applications have made it to the general public as a commercial software. In most cases, the systems never left the laboratory and have a limited range of use, sometimes only as a proof of concept. Is this due, as many believe, to the high production cost of NLP resources? Is it because of the current quality of NLP results? Is it a consequence of the integration strategy of NLP into these applications?

The goal of this issue of Traitement Automatique des Langues dedicated to “NLP for learning and teaching” is to summarize the contribution of NLP to instructional systems, both at a theoretical level (opportunities, limitations, integration methods) and at the level of learning systems – or parts of systems – production.

Authors are invited to submit papers on all the aspects of the implementation of NLP into Computer-Assisted Instruction (CAI) systems for a given discipline, as well as useful tools for this task, in particular regarding, but not limited to, the following issues and tasks:

  • Contribution of (written or spoken) NLP to CAI systems.
  • Needs and requirements of NLP techniques and methods for instructional systems design.
  • Instructional design methodology for NLP-based CAI systems.
  • Presentation of systems and learning tools involving NLP.
  • Collection and use of language corpora for pedagogical purposes using NLP.
  • Use of learner corpora and error annotation using NLP.
  • Automated evaluation of learner writing and short answers using NLP.
  • (Semi-)automated diagnostic assessment and remedial help.
  • Design and setting up of activities involving NLP.
  • Language resources for NLP-based instruction and learning.
  • Automated selection of text resources based on pedagogical criteria.
  • Development, presentation and use of linguistic and metalinguistic information for pedagogical purposes.
  • Learner modelling based on his linguistic output.
  • Approaches and methods for plagiarism detection.

Position papers and state of the art papers are also welcome.





Submission deadline : october 28,  2016

Notification to authors after the first review: february 17, 2017

Notification to authors after the second review: april 28, 2017

Publishing: september, 2017


TAL (Traitement Automatique des Langues / Natural Language Processing) is an international journal published by ATALA (French Association for Natural Language Processing, http://www.atala.org) since 1959 with the support of CNRS (National Centre for Scientific Research). It has moved to an electronic mode of publication, with printing on demand.

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