提高学生听力理解的实证研究     

陈菊 《集美大学学报(教育科学版》2010,11(4):106-108

如何有效提高学生的英语听力理解水平一直是英语教学中的一道难题。基于实证研究观察＂听前了解相关主题并进行讨论法（TP法）＂对学生英语听力理解的影响,结果表明：两平行班学生听力课后的英语学习情况相似,但后测英语听力水平在统计学意义上有显著差异,实验组学生的听力理解表现明显比对照组学生好。TP法能有效提高学生的英语听力理解表现。  相似文献   

试析话题标记“我说”     

李丽群 《河西学院学报》2010,26(3):96-99

话题标记"我说"是一个口语性比较强的语言单位,其出现的会话环境大致可以分为五种类型:宣告型、建议型、不满型、疑惑型和释因型。"我说"的语用功能主要体现在篇章功能和人际功能两方面。  相似文献   

人物专题学习网站的建设     

陈桂成  罗勇为 《现代教育技术》2006,16(4):39-41

通过《邓演达思想研究》学习网站的设计案例和教学实践,分析人物专题学习网站的定位、系统结构和功能设计,为学习者提供一个完整的网络学习环境和特定的人物专题知识展示,实现基于专题网站的研究性学习模式。  相似文献   

国内主题图应用研究综述   总被引：1，自引：0，他引：1  

胡明玲  王建涛 《新世纪图书馆》2010,(2):33-35

论文在介绍国内主题图在专题知识组织、专题信息资源组织、主题图的自动构建和技术标准等方面的应用研究状况的基础上,分析了国内主题图应用的发展条件,并提出了发展的三个方向。  相似文献   

抛锚式教学模式在高职英语毕业论文选题中的应用     

王薇 《宁波广播电视大学学报》2013,(4):103-105

建立在建构主义学习理论下的抛锚式教学模式,强调以学生为中心,培养学生在真实情境中解决问题的能力。本文通过对近几年高职学生英语毕业论文选题的现状及原因进行分析,并探索抛锚式教学模式在高职学生英语毕业论文选题中的应用。  相似文献   

Time gap analysis by the topic model-based temporal technique     

《Journal of Informetrics》2014,8(3):776-790

This study proposes a temporal analysis method to utilize heterogeneous resources such as papers, patents, and web news articles in an integrated manner. We analyzed the time gap phenomena between three resources and two academic areas by conducting text mining-based content analysis. To this end, a topic modeling technique, Latent Dirichlet Allocation (LDA) was used to estimate the optimal time gaps among three resources (papers, patents, and web news articles) in two research domains. The contributions of this study are summarized as follows: firstly, we propose a new temporal analysis method to understand the content characteristics and trends of heterogeneous multiple resources in an integrated manner. We applied it to measure the exact time intervals between academic areas by understanding the time gap phenomena. The results of temporal analysis showed that the resources of the medical field had more up-to-date property than those of the computer field, and thus prompter disclosure to the public. Secondly, we adopted a power-law exponent measurement and content analysis to evaluate the proposed method. With the proposed method, we demonstrate how to analyze heterogeneous resources more precisely and comprehensively.  相似文献   

Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis     

Kai Hu  Qing Luo  Kunlun Qi  Siluo Yang  Jin Mao  Xiaokang Fu  Jie Zheng  Huayi Wu  Ya Guo  Qibing Zhu 《Information processing & management》2019,56(4):1185-1203

Topic evolution has been described by many approaches from a macro level to a detail level, by extracting topic dynamics from text in literature and other media types. However, why the evolution happens is less studied. In this paper, we focus on whether and how the keyword semantics can invoke or affect the topic evolution. We assume that the semantic relatedness among the keywords can affect topic popularity during literature surveying and citing process, thus invoking evolution. However, the assumption is needed to be confirmed in an approach that fully considers the semantic interactions among topics. Traditional topic evolution analyses in scientometric domains cannot provide such support because of using limited semantic meanings. To address this problem, we apply the Google Word2Vec, a deep learning language model, to enhance the keywords with more complete semantic information. We further develop the semantic space as an urban geographic space. We analyze the topic evolution geographically using the measures of spatial autocorrelation, as if keywords are the changing lands in an evolving city. The keyword citations (keyword citation counts one when the paper containing this keyword obtains a citation) are used as an indicator of keyword popularity. Using the bibliographical datasets of the geographical natural hazard field, experimental results demonstrate that in some local areas, the popularity of keywords is affecting that of the surrounding keywords. However, there are no significant impacts on the evolution of all keywords. The spatial autocorrelation analysis identifies the interaction patterns (including High-High leading, High-Low suppressing) among the keywords in local areas. This approach can be regarded as an analyzing framework borrowed from geospatial modeling. Moreover, the prediction results in local areas are demonstrated to be more accurate if considering the spatial autocorrelations.  相似文献   

Coupled matrix factorization and topic modeling for aspect mining     

Ding Xiao  Yugang Ji  Yitong Li  Fuzhen Zhuang  Chuan Shi 《Information processing & management》2018,54(6):861-873

Aspect mining, which aims to extract ad hoc aspects from online reviews and predict rating or opinion on each aspect, can satisfy the personalized needs for evaluation of specific aspect on product quality. Recently, with the increase of related research, how to effectively integrate rating and review information has become the key issue for addressing this problem. Considering that matrix factorization is an effective tool for rating prediction and topic modeling is widely used for review processing, it is a natural idea to combine matrix factorization and topic modeling for aspect mining (or called aspect rating prediction). However, this idea faces several challenges on how to address suitable sharing factors, scale mismatch, and dependency relation of rating and review information. In this paper, we propose a novel model to effectively integrate Matrix factorization and Topic modeling for Aspect rating prediction (MaToAsp). To overcome the above challenges and ensure the performance, MaToAsp employs items as the sharing factors to combine matrix factorization and topic modeling, and introduces an interpretive preference probability to eliminate scale mismatch. In the hybrid model, we establish a dependency relation from ratings to sentiment terms in phrases. The experiments on two real datasets including Chinese Dianping and English Tripadvisor prove that MaToAsp not only obtains reasonable aspect identification but also achieves the best aspect rating prediction performance, compared to recent representative baselines.  相似文献   

搜索引擎中分类技术研究     

万小容  马帅  刘利军 《宁波广播电视大学学报》2008,6(2):116-118

本文提出了一种基于主题采集的Web文档自动分类算法,该算法对朴素贝叶斯分类模型进行了改进。利用该算法,我们实现了一个基于主题信息采集的网页分类系统。文中着重对该系统的页面解析、中文分词和文本分类模块进行了论述,并对改进后的贝叶斯分类方法进行了评估。实验结果表明,该算法对网页分类有较高的准确性。  相似文献   

宋玉《高唐》《神女》二赋之主旨新论     

刘刚 《鞍山师范学院学报》2004,6(3):32-36

关于宋玉<高唐><神女>二赋的主旨,众说纷纭,莫衷一是.我们通过文本内容的分析、祭高唐神女之礼俗分析和<高唐><神女>以事为谏之思想分析,得出了新的结论.我们认为:<高唐><神女>二赋以楚襄王祭高禖为创作素材,借襄王欲幸巫山神女之事为说,劝谏襄王不要把复兴楚国的希望完全寄托在敬天事神的"天命"政治之上,而要面对现实,重视人事,推行"民本"政治,以求楚之王室与国家"延年益寿千万岁".否则即便是巫山神女也不会福佑楚王和楚国.作品反映了宋玉轻天命而重人事的进步思想.  相似文献