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91.
卓越教师培养是时代的求。卓越教师的理想规格是专业精神朴实高尚、专业知识融会贯通、专业能力卓著出色。卓越教师的培养需进行有意识、有计划的设计,这个设计包括:培养模式设计、课程结构设计和实践能力训练设计等。 相似文献
92.
李昌官 《课程.教材.教法》2011,(8)
以分数为目标、对教学内容理解表面化和教学设计无基本章法是造成课堂教学平庸、低效的主要原因,因此在关注优秀课的表现形式与评价标准的同时,更应关注优秀课成长的基础、过程与方法。超越应试是优秀课的基本要求与历史使命;理解数学是教学设计与优秀课成长的基础,而好的教学设计框架则是优秀课成长的过程与方法。 相似文献
93.
以1999年-2009年1 082篇全国优秀博士学位论文为研究对象,对优秀博士论文的区域分布、授予单位、学科领域的分布形态进行了计量分析。结果显示,优秀博士论文在区域、学位授予单位、学科几个层面的分布都显示出不均衡的状态,而这种不均衡背后有着较为深远的影响因素在起作用。 相似文献
94.
黄滔 《四川教育学院学报》2011,(10):92-95
对学生演讲稿的写作过程及成稿文本研究和分析表明,学生演讲中存在清晰的隐含作者。由于演讲是一种公共传播行为,学生演讲中的隐含作者与媒体叙事中的隐含作者较为相似,表现为更优秀、更强大的“第二自我”和文本所体现的、同时也是环境和社会意识形态所要求的价值观和道德观这两个层次。真实作者(学生)与隐含作者的互动、互融过程,就是学生进步、提高的过程。在演讲教学中正确运用隐含作者的作用,将对学生成长产生深远的积极作用。 相似文献
95.
受社会文化心理的影响,欠发达地区仍然存在着教育理念落后、片面追求升学率、忽视师生的生命存在等现象,亟需具有教育智慧的卓越教师来改变这一现状。为此,有必要依据生命哲学理论,结合社会文化心理来探索“卓越教师”教育智慧的培养:以激发本土文化情怀作为卓越教师情感智慧培养的内在动力;以培养创新精神作为卓越教师实践智慧提升的关键;以激发教学灵感作为卓越教师理性智慧培养的重要手段;以提高人文素养作为卓越教师道德智慧培养的重要内涵。 相似文献
96.
The aim of this study was to investigate videos as potential triggers of behavior. Therefore, we applied the theories of triggers and media richness to learn about the triggering efficiency of mobile marketing videos on participants’ behavioral intentions. The experiment involved three distinct test groups, each comprising 41 student participants. From the perspective of media richness theory, we observed that the different kinds of videos had quite similar effects in terms of triggering behavioral changes. However, the mechanisms explaining why triggers were present differed for each video. Further, the results reveal that the consumer's position in the information search process was the most significant reason for the triggering of any kind of effect. In addition, the instructionally designed videos were able to exert an affective triggering effect: the more participants liked the video, the more it affected their participation intention and recall scores. This study extends the media richness research by demonstrating that the effects of media richness can vary within technically similar videos, as they form different logical connections among non-verbal visual cues related to a video's storyline. 相似文献
97.
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
98.
99.
Human collaborative relationship inference is a meaningful task for online social networks and is called link prediction in network science. Real-world networks contain multiple types of interacting components and can be modeled naturally as heterogeneous information networks (HINs). The current link prediction algorithms in HINs fail to effectively extract training samples from snapshots of HINs; moreover, they underutilise the differences between nodes and between meta-paths. Therefore, we propose a meta-circuit machine (MCM) that can learn and fuse node and meta-path features efficiently, and we use these features to inference the collaborative relationships in question-and-answer and bibliographic networks. We first utilise meta-circuit random walks to obtain training samples in which the basic idea is to perform biased meta-path random walks on the input and target network successively and then connect them. Then, a meta-circuit recurrent neural network (mcRNN) is designed for link prediction, which represents each node and meta-path by a dense vector and leverages an RNN to fuse the features of node sequences. Experiments on two real-world networks demonstrate the effectiveness of our framework. This study promotes the investigation of potential evolutionary mechanisms for collaborative relationships and offers practical guidance for designing more effective recommendation systems for online social networks. 相似文献
100.
Jamal Abdul Nasir Iraklis Varlamis Samreen Ishfaq 《Information processing & management》2019,56(5):1605-1617
Searching for relevant material that satisfies the information need of a user, within a large document collection is a critical activity for web search engines. Query Expansion techniques are widely used by search engines for the disambiguation of user’s information need and for improving the information retrieval (IR) performance. Knowledge-based, corpus-based and relevance feedback, are the main QE techniques, that employ different approaches for expanding the user query with synonyms of the search terms (word synonymy) in order to bring more relevant documents and for filtering documents that contain search terms but with a different meaning (also known as word polysemy problem) than the user intended. This work, surveys existing query expansion techniques, highlights their strengths and limitations and introduces a new method that combines the power of knowledge-based or corpus-based techniques with that of relevance feedback. Experimental evaluation on three information retrieval benchmark datasets shows that the application of knowledge or corpus-based query expansion techniques on the results of the relevance feedback step improves the information retrieval performance, with knowledge-based techniques providing significantly better results than their simple relevance feedback alternatives in all sets. 相似文献