This paper describes the development of an innovative web-based environment for English language learning with advanced data-driven and statistical approaches. The project uses various corpora, including a Chinese-English parallel corpus (Sinorama) and various natural language processing (NLP) tools to construct effective English learning tasks for college learners with adaptive computational scaffolding. It integrates the expertise of a group of researchers in four areas: (a) advances in NLP technologies and applications, (b) construction of a self-access reading environment, (c) exploration of English language learning through written exercises and translations, and (d) use of bilingual corpora for culture-based English learning. In this paper, the conceptualization of the system and its various reference tools (e.g., a bilingual concordancer) for English learning and pilot testing on various modules (e.g., a reading module, Text Grader, and Collocation Practice) are reported.

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As globalization and rapid development of science and technology has brought huge impact on today’s world, it is important for non-English speaking countries such as Taiwan, Republic of China, to actively participate in the international community through communication in the international language, English. In higher education, dissemination of research achievements through publication of international journals has been a common degree requirement, the practice in public researchoriented universities being the noticeable example. However, graduate students who have such needs are often unprepared to demonstrate acceptable academic writing abilities in English. To facilitate effective access to research information and to promote national academic excellence through international publications, it is essential to promote writing instruction of English for Academic Purposes (EAP). Meanwhile, Web-based environments provide a viable choice of delivery of EAP courses and the computer-mediated communication tools help formation of the student body as virtual organizations as they learn with one another.

Adaptive filtering techniques are widely used in the fields of signal processing and communication such as echo/noise cancellation and speech/image coding. Adaptive filters usually need real time ability to process signal. This paper presents a high speed and flexible VLSI architecture. This filter is the digital adaptive finite impulse response (FIR) filter based on the delayed error least mean square (DELMS) algorithm. The architecture has hardware utilization efficiency (HUE) of loo%, and we can easily scale the filter without reducing the throughput rate. The timing simulation results demonstrate the effectiveness of the architecture. We have used 0.6 p CMOS SPTM standard cells technology to implement the chip.

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