Agricultural information dissemination using ICTs: a review and analysis of information dissemination models in China

Open Access funded by China Agricultural UniversityOver the last three decades, China’s agriculture sector has been transformed from the traditional to modern practice through the effective deployment of Information and Communication Technologies (ICTs). Information processing and dissemination have played a critical role in this transformation process. Many studies in relation to agriculture information services have been conducted in China, but few of them have attempted to provide a comprehensive review and analysis of different information dissemination models and their applications. This paper aims to review and identify the ICT based information dissemination models in China and to share the knowledge and experience in applying emerging ICTs in disseminating agriculture information to farmers and farm communities to improve productivity and economic, social and environmental sustainability. The paper reviews and analyzes the development stages of China’s agricultural information dissemination systems and different mechanisms for agricultural information service development and operations. Seven ICT-based information dissemination models are identified and discussed. Success cases are presented. The findings provide a useful direction for researchers and practitioners in developing future ICT based information dissemination systems. It is hoped that this paper will also help other developing countries to learn from China’s experience and best practice in their endeavor of applying emerging ICTs in agriculture information dissemination and knowledge transfer

Knowledge building and vocabulary growth over two years, Grades 3 and 4

High-level literacy and productive knowledge work are central to educational reforms. In the research reported in this article, students were engaged in sustained, collaborative knowledge building in science and social studies. The vocabulary growth of 22 students over Grades 3 and 4 was traced, based on their entries to Knowledge Forum—a knowledge building environment used as an integral part of classroom work. It is the communal space where ideas, reference material, results of experiments, and other inquiry work are entered and continually improved. Analysis of lexical frequency profiles indicated significant growth in productive written vocabulary, including academic words. In a Grade 4 inquiry, students incorporated almost all the domain-specific terms at and below their current grade level, and most of those expected for upper grade levels (5-8) based on the curriculum guidelines. Domain-specific and academic words were correlated with depth of understanding. High correlations between student engagement in knowledge building and vocabulary growth suggest that productive vocabulary can be developed through sustained knowledge building in subject areas

Developing Deep Understanding and Literacy while Addressing a Gender-Based Literacy Gap

Online discourse from a class of 22 students (11 boys and 11 girls) was analysed to assess advances in conceptual understanding and literacy. The students worked over a two-­‐year period (Grades 3-­‐4), during which they contributed notes to an online Knowledge Building environment—Knowledge Forum®. Contributions revealed that both boys and girls produced a substantial amount of text and graphics, and that their written texts incorporated an increasing proportion of less-­‐frequent, advanced words, including academic vocabulary and domain-­‐specific words from grade levels higher than their own. Brief accounts of classroom discourse indicate how deep understanding and vocabulary growth mutually support each other in online and offline exchanges. The gender differences that were observed show boys doing slightly better than girls, suggesting that Knowledge Building has the potential to help boys overcome weaknesses in literacy

Human and Machine Speaker Recognition Based on Short Trivial Events

Trivial events are ubiquitous in human to human conversations, e.g., cough, laugh and sniff. Compared to regular speech, these trivial events are usually short and unclear, thus generally regarded as not speaker discriminative and so are largely ignored by present speaker recognition research. However, these trivial events are highly valuable in some particular circumstances such as forensic examination, as they are less subjected to intentional change, so can be used to discover the genuine speaker from disguised speech. In this paper, we collect a trivial event speech database that involves 75 speakers and 6 types of events, and report preliminary speaker recognition results on this database, by both human listeners and machines. Particularly, the deep feature learning technique recently proposed by our group is utilized to analyze and recognize the trivial events, which leads to acceptable equal error rates (EERs) despite the extremely short durations (0.2-0.5 seconds) of these events. Comparing different types of events, 'hmm' seems more speaker discriminative.Comment: ICASSP 201