Conference article

A VIEW of Russian: Visual Input Enhancement and Adaptive Feedback

Robert Reynolds
University of Tromsø, Department of Language and Linguistics, Norway

Eduard Schaf
University of Tübingen, Department of Linguistics, Germany/University of Tromsø, Department of Language and Linguistics, Norway

Detmar Meurers
University of Tromsø, Department of Language and Linguistics, Norway

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Published in: Proceedings of the third workshop on NLP for computer-assisted language learning at SLTC 2014, Uppsala University

Linköping Electronic Conference Proceedings 107:8, p. 98–112

NEALT Proceedings Series 22:8, p. 98–112

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Published: 2014-11-11

ISBN: 978-91-7519-175-1

ISSN: 1650-3686 (print), 1650-3740 (online)


We explore the challenges and opportunities which arise in developing automatic visual input enhancement activities for Russian with a focus on target selection and adaptive feedback. Russian, a language with a rich fusional morphology, has many syntactically relevant forms that are not transparent to the language learner, which makes it a good candidate for visual input enhancement (VIE). VIE essentially supports incidental focus on form by increasing the salience of language forms to support noticing by the learner. The freely available VIEW system (Meurers et al., 2010) was designed to automatically generate VIE activities from any web content. We extend VIEW to Russian and discuss connected research issues regarding target selection, ambiguity management, prompt generation, and distractor generation. We show that the same information and techniques used for target selection can often be repurposed for adaptive feedback. Authentic Text ICALL (ATICALL) systems incorporating only native-language NLP without the NLP analysis specific to learner language that is characteristic of Intelligent, Language Tutoring Systems (ILTS), thus can support some forms of adaptive feedback. ATICALL and ILTS represent a spectrum of possibilities rather than two categorically distinct enterprises.


CALL; ICALL; ATICALL; input enhancement; noticing; consciousness raising; adaptive feedback; scaffolding; part-of-speech tagging; finite-state technology; Constraint Grammar; Russian; stress; aspect; participles; case


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