David Wible

A ubiquitous agent for unrestricted vocabulary learning in noisy digital environments

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 words 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.

Toward Automating a Personalized Concordancer for Data-Driven Learning: A Lexical Difficulty Filter for Language Learners

One of the most widely acknowledged barriers to the effectiveness of corpus and concordancing resources in the hands of language learners and educators is the lack of control over the examples retrieved. The purpose of this paper is to describe a novel tool, called the Lexical Difficulty Filter (LDF), which we have developed to increase this sort of control, specifically to filter concordance examples according to a flexible threshold of lexical difficulty. We also suggest refinements and extensions to the LDF for future research. What we present here constitutes one part of a larger effort that we are engaging in to provide precision and flexibility for language teachers and learners in their use of concordancing tools and large corpora.

Assessment of an Online Learning Community from Technology Acceptance Model in Education

Online learning communities are gradually altering the traditional learning style of people because of the pervasiveness of the Internet. The environment of the online learning community has been formed gradually as more and more people join Web sites and learn from each other. A total of 436 senior high school students in Taiwan participated in this research. To test the hypotheses of this research, we use structural equation modeling (SEM) method for validation. All hypotheses we proposed were supported. Finally, we list several implications of this research results as guidelines for developing an online learning community for future study.

On the design of Web-based interactive multimedia contents for English learning

We present a Web-based authoring tool to create interactive multimedia contents for English learning. Target users of this authoring tool are English teachers and content designer, therefore, ease of use and simplicity are the fundamental issues. Furthermore, the authoring tool and multimedia database are integrated in the context of intelligent Web-based interactive language learning (IWiLL) system. Special language tools to access corpus, manipulate multimedia elements, and create collaborative learning sessions are designed in the system. These resources and tools on hand yield the potential to create rich and deep interactive multimedia contents.

Feature expansion for word sense disambiguation

One of the most serious obstacles in research on word sense disambiguation (WSD) is sparseness of training data. We describe and motivate a method of feature expansion as a means of resolving the data sparseness problem in supervised corpus-based WSD. The expanded features are extracted from an existing corpus and WordNet automatically. We use our method to supplement the feature expansion approach of [Leacock and Chodorow 1998]. In the experiments, the addition of our method more than doubled the precision improvement over baseline that was obtained by using Leacock and Chodorows approach alone.

The design of a Lexical Difficulty Filter for language learning on the Internet

The authors describe the design and implementation of a tool called the Lexical Difficulty Filter (LDF) intended to help learners or teachers in selecting sentences appropriate to the level of students for English vocabulary learning. The LDF serves as a bridge between learner and information resource bank. It selects suitable sentences from the output of standard English corpus concordancers. The core technology in the design of LDF is a fuzzy expert system. The results obtained are very encouraging in this pioneering work. We achieve 94.33% accuracy rate in imitating the judgments of a human expert in determining the degree of difficulty of a sentence for a given learner level.