A ubiquitous agent for unrestricted vocabulary learning in noisy digital environments

Authors:
David Wible;Chin-Hwa Kuo;Meng-Chang Chen;Nai-Lung Tsao;Chong-Fu Hong
Affiliations:
Department of English, Tamkang University, Taipei County, Taiwan;Computer and Network Lab, Tamkang University, Taipei County, Taiwan;Institute of Information Science, Academia Sinica, Nankang Taipei, Taiwan;Institute of Information Science, Academia Sinica, Nankang Taipei, Taiwan;Computer and Network Lab, Tamkang University, Taipei County, Taiwan
Venue:
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Year:
2006

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Abstract

One of the most persistently difficult aspects of vocabulary for foreign language learners is collocation. This paper describes a browser-based agent that assists learners in acquiring collocations in context during their unrestricted Web browsing. The agent overcomes the limitations imposed by learner models in traditional ITS. Its capacity to function in noisy unscripted contexts derives from a well-understood theory of lexical knowledge that attributes a word's identity to its contextual features. Collocations constitute a central feature type, and we extract these features computationally from a 20-million-word portion of BNC. These we are able to detect and highlight in real time for learners in the noisy Web environments they freely browse. Our learner model, derived by semi-automatic techniques from our 3-million word corpus of learner English, maps detected collocations onto corresponding collocation errors produced by this learner population, alerting learners to the non-substitutability of words within the target collocations. A notebook offers a push function for individualized repeated exposure to examples of these collocations in context.