Chin-Hwa Kuo

Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community

An online learning community enables learners to access up-to-date information via the Internet anytime-anywhere because of the ubiquity of the World Wide Web (WWW). Students can also interact with one another during the learning process. Hence, researchers want to determine whether such interaction produces learning synergy in an online learning community. In this paper, we take the Technology Acceptance Model as a foundation and extend the external variables as well as the Perceived Variables as our model and propose a number of hypotheses. A total of 436 Taiwanese senior high school students participated in this research, and the online learning community focused on learning English. The research results show that all the hypotheses are supported, which indicates that the extended variables can effectively predict whether users will adopt an online learning community. Finally, we discuss the implications of our findings for the future development of online English learning communities.

Concept-Based Pages Recommendation by Using Cluster Algorithm

In this research, we used a proxy server to search for information related to the userpsilas browsed Web pages. From the records of the proxy server we constructed a profile of the userpsilas browsing habits. At the end of the userpsilas search subsystem, we will use content based concept to extract keywords to obtain the articlepsilas characteristicspsila description. From the recommendation system, the Web pages will be classified using the hierarchical grouping method, and through collaborative filtering, the recommendation Web pages will be chosen to provide further readings for students language learning.

CanFind-a semantic image indexing and retrieval system

We present CanFind, a semantic image indexing and retrieval system in this paper. To identify the target images of interest in the database in the conceptual level, the presented system makes use of keywords as the input of searching vehicle. The system consists of two subsystems, i.e., semantic indexing and query expansion. In the semantic indexing, the subsystem includes three main building blocks, namely, keyword extraction, keyword expansion, and keyword weighting. The information of WordNet is used to extend existing keywords associated with images. This design intends to overcome the drawbacks in conventional keyword-based image retrieval system. Next, the resulting word set is filtered by a filter to extract common words from the word set and set up the image indexing for the corresponding image. In the query expansion, corpus is used to help users find relative or precise results in the facing dilemma of too few or too many query results for a given query. The designed semantic image indexing and retrieval system is integrated with IWiLL, a web-based language learning platform to further illustrate the value of the designed system.

The Designing of a Web Page Recommendation System for ESL

In this paper, a webpage reading recommendation system is constructed through the concept of meta search and article summary technique. The designed system recommends webpages that are related to the current webpage, to provide the user with further reading material. Using article-searching mechanism, the ESL student can avoid using keyword-based search method, thereby greatly decreasing the time spent to look for related articles. The system provides related articles as well as information such as the difficulty of the articles, which would assist English learning, and harbor a more user friendly English learning environment. This in turn increases learning efficiency. A designed toolbar serves as the main medium of communication with the user. All the user has to do is install the toolbar on the browser to gain the assistance from the system.

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.

The Design of English Article Recommender Mechanism for Senior High School Students

For most English learners, reading English articles has always been a good way of improving the English proficiency. However, it is not easy to select interesting English articles of adequate difficulty vocabulary level. The purpose of this work is to devise a mechanism for selecting appropriate English articles for ESL learners in Taiwan. The proposed mechanism works by indicating whether a particular article, e.g., English news, is adequate in difficulty for the senior high school students in Taiwan.

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.