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| 1 | PBL教学模式探索显示文摘 | 黄亚玲 郑孝清 金润铭 兰黎 周东风 | 2005 | 医学与社会2005，18，6： | 154 |
| 2 | PBL教学模式改革的思考显示文摘 | 崔舜 陶晓南 吴汉妮 侯晓华 | 2005 | 医学与社会2005，18，6： | 81 |
| 3 | 从Blending Learning看教育技术理论的新发展(上)显示文摘本文介绍了 Blending L earning(或 Blended L earning)的新含义 ,指出这一新含义的提出和被广泛认同 ,表明国际教育技术界的教育思想观念正在经历又一场深刻的变革 ,也是教育技术理论进一步发展的标志。作者还从对建构主义理论的反思、对信息技术教育应用认识的深化 。 | 何克抗 | 2004 | 电化教育研究2004，25，3： | 2833 |
| 4 | 从Blending Learning看教育技术理论的新发展(下)显示文摘本文介绍了 Blending L earning(或 Blended L earning)的新含义 ,指出这一新含义的提出和被广泛认同 ,表明国际教育技术界的教育思想观念正在经历又一场深刻的变革 ,也是教育技术理论进一步发展的标志。作者还从对建构主义理论的反思、对信息技术教育应用认识的深化 。 | 何克抗 | 2004 | 电化教育研究2004，25，4： | 421 |
| 5 | JiTT——Blending Learning理念下的信息化教学模式显示文摘近年来,适时教学(JiTT)作为一种能有效促进学生自主学习的教学模式,在欧美国家本科教学中受到推崇。JiTT既包含传统的面对面教学,又包含了学生自主网络探究的环节,充分体现了Blending Learning的新理念。文章通过对JiTT内涵、国外相关成功案例进行剖析,强调了成功实践JiTT的关键因素,并得出了JiTT较之传统本科授课的优势所在,以及对目前我国本科教学的启示。以期为我国当前开展的'质量工程'提供借鉴。 | 马萌 何克抗 | 2008 | 中国教育信息化(高教职教)2008，，11： | 69 |
| 6 | 基于Blending Learning的微课设计研究显示文摘通过分析比较Blending Learning概念和微课的概念、特征,提出了基于Blending Learning的微课新概念。依据Blending Learning教学过程设计模式,阐述了微课教学设计组成和原则,微课网络平台设计的基本形式、构成要素及活动特征。根据Blending Learning中师生角色、教学过程和视频录制等进行了微课分类,着重分析了讲授类和演示类微课的设计,以及微课视频录制技术应用,并且运用多媒体学习理论阐明了微课件的设计原则。 | 韩中保 韩扣兰 | 2014 | 现代教育技术2014，24，1： | 53 |
| 7 | 6G Visions:Mobile Ultra-Broadband,Super Internet-of-Things,and Artificial Intelligence显示文摘With a ten-year horizon from concept to reality, it is time now to start thinking about what will the sixth-generation(6G) mobile communications be on the eve of the fifth-generation(5G) deployment. To pave the way for the development of 6G and beyond, we provide 6G visions in this paper. We first introduce the state-of-the-art technologies in 5G and indicate the necessity to study 6G. By taking the current and emerging development of wireless communications into consideration, we envision 6G to include three major aspects, namely, mobile ultra-broadband, super Internet-of-Things(IoT), and artificial intelligence(AI). Then, we review key technologies to realize each aspect. In particular, teraherz(THz) communications can be used to support mobile ultra-broadband, symbiotic radio and satellite-assisted communications can be used to achieve super IoT, and machine learning techniques are promising candidates for AI. For each technology, we provide the basic principle, key challenges, and state-of-the-art approaches and solutions. | Lin Zhang Ying-Chang Liang Dusit Niyato | 2019 | China Communications2019，16，8： | 52 |
| 8 | Deep forest显示文摘Current deep-learning models are mostly built upon neural networks, i.e. multiple layers of parameterized differentiable non-linear modules that can be trained by backpropagation. In this paper, we explore the possibility of building deep models based on non-differentiable modules such as decision trees. After a discussion about the mystery behind deep neural networks, particularly by contrasting them with shallow neural networks and traditional machine-learning techniques such as decision trees and boosting machines,we conjecture that the success of deep neural networks owes much to three characteristics, i.e.layer-by-layer processing, in-model feature transformation and sufficient model complexity. On one hand,our conjecture may offer inspiration for theoretical understanding of deep learning; on the other hand, to verify the conjecture, we propose an approach that generates deep forest holding these characteristics. This is a decision-tree ensemble approach, with fewer hyper-parameters than deep neural networks, and its model complexity can be automatically determined in a data-dependent way. Experiments show that its performance is quite robust to hyper-parameter settings, such that in most cases, even across different data from different domains, it is able to achieve excellent performance by using the same default setting. This study opens the door to deep learning based on non-differentiable modules without gradient-based adjustment, and exhibits the possibility of constructing deep models without backpropagation. | Zhi-Hua Zhou Ji Feng | 2019 | National Science Review2019，6，1： | 52 |
| 9 | 国内近十年混合式学习研究趋势分析——基于2005——2015教育技术领域学位论文显示文摘以十年来国内教育技术领域硕博学位论文中以混合式学习为主题的论文作为分析对象,参照国际学者提出的分析框架,从研究背景、研究方法运用及研究主题变化等维度开展混合式学习研究现状及国际对比分析。主要结论认为:国内混合式学习学位论文研究主要集中于高等教育领域,并集中在课程层面。国内与国际学位论文在混合研究方法运用方面存在较大差异。国内更注重对教学设计、学习效果分析、混合式与面对面学习对比以及混合式学习中的技术应用等主题的研究。国内外在混合式学习使用意向及交互主题的研究数量及维度方面存在较大差异。 | 马志强 孔丽丽 曾宁 | 2015 | 现代远距离教育2015，，6： | 45 |
| 10 | Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring显示文摘Computer vision techniques, in conjunction with acquisition through remote cameras and unmanned aerial vehicles (UAVs), offer promising non-contact solutions to civil infrastructure condition assessment. The ultimate goal of such a system is to automatically and robustly convert the image or video data into actionable information. This paper provides an overview of recent advances in computer vision techniques as they apply to the problem of civil infrastructure condition assessment. In particular, relevant research in the fields of computer vision, machine learning, and structural engineering is presented. The work reviewed is classified into two types: inspection applications and monitoring applications. The inspection applications reviewed include identifying context such as structural components, characterizing local and global visible damage, and detecting changes from a reference image. The monitoring applications discussed include static measurement of strain and displacement, as well as dynamic measurement of displacement for modal analysis. Subsequently, some of the key challenges that persist toward the goal of automated vision-based civil infrastructure and monitoring are presented. The paper concludes with ongoing work aimed at addressing some of these stated challenges. | Billie F. Spencer Jr. Vedhus Hoskere Yasutaka Narazaki | 2019 | Engineering2019，5，2： | 44 |
| 11 | The State of the Art of Data Science and Engineering in Structural Health Monitoring显示文摘Structural health monitoring (SHM) is a multi-discipline field that involves the automatic sensing of structural loads and response by means of a large number of sensors and instruments, followed by a diagnosis of the structural health based on the collected data. Because an SHM system implemented into a structure automatically senses, evaluates, and warns about structural conditions in real time, massive data are a significant feature of SHM. The techniques related to massive data are referred to as data science and engineering, and include acquisition techniques, transition techniques, management techniques, and processing and mining algorithms for massive data. This paper provides a brief review of the state of the art of data science and engineering in SHM as investigated by these authors, and covers the compressive sampling-based data-acquisition algorithm, the anomaly data diagnosis approach using a deep learning algorithm, crack identification approaches using computer vision techniques, and condition assessment approaches for bridges using machine learning algorithms. Future trends are discussed in the conclusion. | Yuequan Bao Zhicheng Chen Shiyin Wei Yang Xu Zhiyi Tang Hui Li | 2019 | Engineering2019，5，2： | 47 |
| 12 | Recent advances and applications of machine learning in solidstate materials science显示文摘One of the most exciting tools that have entered the material science toolbox in recent years is machine learning.This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research.At present,we are witnessing an explosion of works that develop and apply machine learning to solid-state systems.We provide a comprehensive overview and analysis of the most recent research in this topic.As a starting point,we introduce machine learning principles,algorithms,descriptors,and databases in materials science.We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure.Then we discuss research in numerous quantitative structure–property relationships and various approaches for the replacement of first-principle methods by machine learning.We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications.Two major questions are always the interpretability of and the physical understanding gained from machine learning models.We consider therefore the different facets of interpretability and their importance in materials science.Finally,we propose solutions and future research paths for various challenges in computational materials science. | Jonathan Schmidt Mário R.G.Marques Silvana Botti Miguel A.L.Marques | 2019 | npj Computational Materials2019，，1： | 49 |
| 13 | Diagnostic accuracy of a deep learning approach to calculate FFR from coronary CT angiography显示文摘Background The computational fluid dynamics(CFD)approach has been frequently applied to compute the fractional flow reserve(FFR)using computed tomography angiography(CTA).This technique is efficient.We developed the DEEPVESSEL-FFR platform using the emerging deep learning technique to calculate the FFR value out of CTA images in five minutes.This study is to evaluate the DEEPVESSEL-FFR platform using the emerging deep learning technique to calculate the FFR value from CTA images as an efficient method.Methods A single-center,prospective study was conducted and 63 patients were enrolled for the evaluation of the diagnostic performance of DEEPVESSEL-FFR.Automatic quantification method for the three-dimensional coronary arterial geometry and the deep learning based prediction of FFR were developed to assess the ischemic risk of the stenotic coronary arteries.Diagnostic performance of the DEEPVESSEL-FFR was assessed by using wire-based FFR as reference standard.The primary evaluation factor was defined by using the area under receiver-operation characteristics curve(AUC)analysis.Results For per-patient level,taking the cut-off value<0.8 referring to the FFR measurement,DEEPVESSEL-FFR presented higher diagnostic performance in determining ischemia-related lesions with area under the curve of 0.928 compare to CTA stenotic severity 0.664.DEEPVESSEL-FFR correlated with FFR(R=0.686,P<0.001),with a mean di&ference of-0.006士0.0091(P=0.619).The secondary evaluation factors,indicating per vessel accuracy,sensitivity,specificity,positive predictive value,and negative predictive value were 87.3%,97.14%,75%,82.93%,and 95.45%,respectively.Conclusion DEEPVESSEL-FFR is a novel method that allows efficient assessment of the functional significance of coronary stenosis. | Zhi-Qiang WANG Yu-Jie ZHOU Ying-Xin ZHAO Dong-Mei SHI Yu-Yang LIU Wei LIU Xiao-Li LIU Yue-Ping LI | 2019 | Journal of Geriatric Cardiology2019，16，1： | 37 |
| 14 | 从大学英语教学透视Blended Learning显示文摘本文从大学英语教学和 Blended L earning的产生、发展等多视角透视其概念内涵 ;在计算机及网络技术已经全面且实质性地应用于我国英语教学的今天 ,正确地把握 Blended L earning的本质将有助于教育技术在英语教学改革中发挥越来越重要的作用。 | 赵丽娟 | 2004 | 电化教育研究2004，25，11： | 29 |
| 15 | 结合实际合理运用PBL教学法显示文摘PBL(problem based-learning,问题式学习)作为一种提高学生综合素质的教学法,得到中外学者的一致认同.PBL把学生置于教学环节的中心位置,强调发挥学生的主观能动性,自己去寻找解决问题的方法,并在解决问题的过程中培养医学专业素质,使学生学会学习、学会分析、学会与他人协作,加强学生把理论学习与实践工作有机结合的观念.近年来,在国内推行PBL教育的过程中,也发现了其中存在的一些问题.因此,对于PBL教学法,应该针对实际情况,有选择的、有适用范围地引入我国医学教育的教学体系中. | 刘宇 王跃民 裴建明 | 2005 | 山西医科大学学报(基础医学教育版)2005，7，2： | 30 |
| 16 | Blended Learning与多媒体英语听力实验课程教学的实证研究显示文摘在国内外教育技术学领域中,关于学习或教学模式,讨论较多的问题是如何充分发挥学生学习的自主性。然而,在英语教学中,由于过去传统的教学思想的影响,在新的教学环境中,在给予学生自主学习的同时,教师常常处于一种尴尬的境地,即感到在这种环境中无所适从,不知如何发挥其应有的作用。本文基于对Blended Learning理念的阐述,以英语听力教学为例,旨在探讨在多媒体环境下在保持学生自主学习模式的同时如何发挥教师应有的作用。 | 张海森 李艳 | 2006 | 外语电化教学2006，，3： | 28 |
| 17 | 国内混合式学习的文献计量和知识图谱分析——基于CNKI 2003-2016年数据显示文摘以2003-2016年中国知网收录的混合式学习文献为样本,计量分析混合学习文献的数量分布、核心作者、研究机构、来源期刊以及高被引文献,并进一步利用知识图谱工具CiteSpace进行关键词共现分析、突变检测分析以及聚类时间域图谱分析,研究发现:国内混合学习的热点研究主题包括MOOC、教学模式、翻转课堂、教学设计、在线学习、SPOC、自主学习、学习效果等;研究前沿可以归纳为三个方面:一是MOOC及SPOC的研究与实践(2014年),二是翻转课堂的研究与实践(2013年),三是混合式课堂教学的研究与实践(2008年);研究热点的演化主要体现在启蒙阶段(2003-2006年)、稳步发展阶段(2007-2009)、实践反思阶段(2010-2014年)、突破爆发阶段(2015年后)。从总体上看,国内混合学习的理论与实践有待深化,我们建议:一是进一步增强混合学习研究的本土化,在紧跟国际研究热点和前沿的同时,结合国内特点,探索形成适合我国实际的混合学习理论与应用成果,避免一味地追赶新模式、新概念、新技术;二是进一步增强混合学习研究的创新性;三是进一步增强混合学习研究的系统性。 | 蒋红星 代洪彬 肖宗娜 | 2016 | 广西师范大学学报(哲学社会科学版)2016，52，5： | 28 |
| 18 | A strategy to apply machine learning to small datasets in materials science显示文摘There is growing interest in applying machine learning techniques in the research of materials science.However,although it is recognized that materials datasets are typically smaller and sometimes more diverse compared to other fields,the influence of availability of materials data on training machine learning models has not yet been studied,which prevents the possibility to establish accurate predictive rules using small materials datasets.Here we analyzed the fundamental interplay between the availability of materials data and the predictive capability of machine learning models.Instead of affecting the model precision directly,the effect of data size is mediated by the degree of freedom(DoF)of model,resulting in the phenomenon of association between precision and DoF.The appearance of precision–DoF association signals the issue of underfitting and is characterized by large bias of prediction,which consequently restricts the accurate prediction in unknown domains.We proposed to incorporate the crude estimation of property in the feature space to establish ML models using small sized materials data,which increases the accuracy of prediction without the cost of higher DoF.In three case studies of predicting the band gap of binary semiconductors,lattice thermal conductivity,and elastic properties of zeolites,the integration of crude estimation effectively boosted the predictive capability of machine learning models to state-of-art levels,demonstrating the generality of the proposed strategy to construct accurate machine learning models using small materials dataset. | Ying Zhang Chen Ling | 2018 | npj Computational Materials2018，，1： | 25 |
| 19 | Deep Learning in Medical Ultrasound Analysis: A Review显示文摘Ultrasound (US) has become one of the most commonly performed imaging modalities in clinical practice. It is a rapidly evolving technology with certain advantages and with unique challenges that include low imaging quality and high variability. From the perspective of image analysis, it is essential to develop advanced automatic US image analysis methods to assist in US diagnosis and/or to make such assessment more objective and accurate. Deep learning has recently emerged as the leading machine learning tool in various research fields, and especially in general imaging analysis and computer vision. Deep learning also shows huge potential for various automatic US image analysis tasks. This review first briefly introduces several popular deep learning architectures, and then summarizes and thoroughly discusses their applications in various specific tasks in US image analysis, such as classification, detection, and segmentation. Finally, the open challenges and potential trends of the future application of deep learning in medical US image analysis are discussed. | Shengfeng Liu Yi Wang Xin Yang Baiying Lei Li Liu Shawn Xiang Li Dong Ni Tianfu Wang | 2019 | Engineering2019，5，2： | 24 |
| 20 | “Learning by Doing”教学模式的探索显示文摘'Learning by Doing'是由美国卡内基·梅隆大学率先提出的一种旨在强化工程学科的学生全面实践能力和工程素养的教学模式。其目的就是让学生在'做'的过程中,深刻掌握相关的技术和技能,获得远超过课堂教学的教学效果。本文首先介绍了'LearningbyDoing'的概念及作用,然后详细讨论了在'WindowsCE嵌入式系统'课程中实施'LearningbyDoing'的具体做法以及经验得失。 | 何宗键 覃文忠 | 2005 | 计算机教育2005，，12： | 21 |
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