“Learning by Doing”教学模式的探索

"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。

Learning-by-doing教学模式在安全系统工程教学中的应用

安全系统工程课程是安全工程专业的专业基础课,其教学效果的好坏对后续课程的学习以及日后所从事的工作至关重要。因而该课程教学方法的运用选择值得深思。笔者分析了安全工程专业中安全系统工程课程的特点和教学中存在的问题,引用learning-by-doing的教学模式并在教学中加以应用,提出"通过授课得到答案——学会一个解,通过案例讨论得到方法——学会一个方法,通过实践模拟学会学习——学会找到个方法,通过总结学会融会贯通"的安全系统工程教学模式,收到了较好的教学效果。

“Flash动画设计”课程“Learning by doing”教学法探索

本文针对"Flash动画设计"的课程特点引入"Learningbydoing"教学理念,提出"简单实例掌握基本技能→案例讨论学会分析方法→模拟实践学会找到方法→总结经验融会贯通同时实现创新提高"的教学模式,在教学实践中取得了良好的效果。

基于Learning-by-doing的计算机系统结构课程改革

在计算机系统结构的课程教学中,引入由卡内基.梅隆大学提出的"Learning-by-doing"这一适用于工程教学的行之有效的先进教学理念是新教学模式的一种积极探索。文章围绕基于"Learning-by-doing"教学法的计算机系统结构课程改革实施的前期准备、遇到的问题,具体解决方案等环节进行了讨论和分析。

具有不完全技术外部性的随机Learning-by-Doing模型及解法

提出适用于随机Learning-by-Doing模型的"附加效用"值函数解法,并用此方法求解具有不完全技术外部性的随机learning-by-doing模型,得到了均衡时的经济增长路径、消费—资本比和值函数,讨论了技术外部性对私人资本回报率、消费倾向、均值经济增长率和个体福利的影响.

“嵌入式系统程序设计实习”教学改革——探索“Learning by Doing”教学模式

"Learning by Doing"是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。本文阐述了在"嵌入式系统程序设计实习"课程中实施"Learning by Doing"的具体方法以及一些经验得失。

基于Learning-by-doing的不确定经济增长与财政政策研究

本文研究基于learning-by-doing的随机增长模型,得到了均衡时的经济增长路径、债券回报率、资产组合份额和消费-资本比,分析财政政策对长期经济增长、资产组合选择、个体消费倾向和个体福利的影响,探讨最优的财政政策。

“干中学(Learning by Doing)”——浅议课堂教学方法改革

提出了“干中学”的新型课堂教学观.阐释了学生通过“干中学”能主动参与课堂教学,成为教学的真正主体.从传统课堂教学和新型课堂教学的比较,说明新型的师生关系是双向的、交互的,教师作为课堂教学的组织者和协调者,在教学中只能起“主导”作用.

一个多资本的Learning-by-doing模型

考虑一个多资本投入的一般Learning by doing模型 .该模型技术进步的增长率由经济发展过程内生地决定 .当技术对资本为递减规模回报时 ,该经济仅有唯一正的均衡点 .运用单调动力系统理论论证了此模型的稳定性问题 。

Learning by doing方法在移动平台应用开发课中的应用

"嵌入式移动平台应用开发"课程是电子信息科学与技术专业的专业课,以培养学生的嵌入式软件开发能力为目的。将Learning by doing教学模式应用到嵌入式移动平台应用开发课程中,通过改革授课方式、教学内容组织以及考核方式,使学生在做中理解所学的知识,融会贯通,实操能力和编程动手能力得到提高。通过实践,取得了良好的教学效果,培养了学生的创新精神和解决实际问题的能力。

Progressive Transfer Learning-based Deep Q Network for DDOS Defence in WSN

In The Wireless Multimedia Sensor Network(WNSMs)have achieved popularity among diverse communities as a result of technological breakthroughs in sensor and current gadgets.By utilising portable technologies,it achieves solid and significant results in wireless communication,media transfer,and digital transmission.Sensor nodes have been used in agriculture and industry to detect characteristics such as temperature,moisture content,and other environmental conditions in recent decades.WNSMs have also made apps easier to use by giving devices self-governing access to send and process data connected with appro-priate audio and video information.Many video sensor network studies focus on lowering power consumption and increasing transmission capacity,but the main demand is data reliability.Because of the obstacles in the sensor nodes,WMSN is subjected to a variety of attacks,including Denial of Service(DoS)attacks.Deep Convolutional Neural Network is designed with the stateaction relationship mapping which is used to identify the DDOS Attackers present in the Wireless Sensor Networks for Smart Agriculture.The Proposed work it performs the data collection about the traffic conditions and identifies the deviation between the network conditions such as packet loss due to network congestion and the presence of attackers in the network.It reduces the attacker detection delay and improves the detection accuracy.In order to protect the network against DoS assaults,an improved machine learning technique must be offered.An efficient Deep Neural Network approach is provided for detecting DoS in WMSN.The required parameters are selected using an adaptive particle swarm optimization technique.The ratio of packet transmission,energy consumption,latency,network length,and throughput will be used to evaluate the approach’s efficiency.

Under "Integration of Doing, Learning and Teaching", Research on the Project-Based Teaching Innovation of "Landscape Planning and Design"

Based on the research on the project course theory of "integration of theory and practice" in higher vocational education and the analysis of practical teaching in colleges and universities at home and abroad, combined with literature research, case analysis, system theory and other research methods, the project-based teaching goal, model, content and means of "integration of doing, learning and teaching" in higher vocational education is explored, and the project-based teaching model of "Landscape Planning and Design" is discussed combined with the application of information-based teaching methods. So as to provide references for carrying out the project-based teaching in similar courses in higher vocational colleges and really achieve docking the actual post requirements with the course to provide the basis for achieving the purpose of cultivating skilled talents in higher vocational education.

Advances in machine learning-and artificial intelligence-assisted material design of steels

With the rapid development of artificial intelligence technology and increasing material data,machine learning-and artificial intelligence-assisted design of high-performance steel materials is becoming a mainstream paradigm in materials science.Machine learning methods,based on an interdisciplinary discipline between computer science,statistics and material science,are good at discovering correlations between numerous data points.Compared with the traditional physical modeling method in material science,the main advantage of machine learning is that it overcomes the complex physical mechanisms of the material itself and provides a new perspective for the research and development of novel materials.This review starts with data preprocessing and the introduction of different machine learning models,including algorithm selection and model evaluation.Then,some successful cases of applying machine learning methods in the field of steel research are reviewed based on the main theme of optimizing composition,structure,processing,and performance.The application of machine learning methods to the performance-oriented inverse design of material composition and detection of steel defects is also reviewed.Finally,the applicability and limitations of machine learning in the material field are summarized,and future directions and prospects are discussed.

Machine Learning Approaches to Detect DoS and Their Effect on WSNs Lifetime

Energy and security remain the main two challenges in Wireless Sensor Networks(WSNs).Therefore,protecting these WSN networks from Denial of Service(DoS)and Distributed DoS(DDoS)is one of the WSN networks security tasks.Traditional packet deep scan systems that rely on open field inspection in transport layer security packets and the open field encryption trend are making machine learning-based systems the only viable choice for these types of attacks.This paper contributes to the evaluation of the use machine learning algorithms in WSN nodes traffic and their effect on WSN network life time.We examined the performance metrics of different machine learning classification categories such asK-Nearest Neighbour(KNN),Logistic Regression(LR),Support Vector Machine(SVM),Gboost,Decision Tree(DT),Na飗e Bayes,Long Short Term Memory(LSTM),and Multi-Layer Perceptron(MLP)on aWSN-dataset in different sizes.The test results proved that the statistical and logical classification categories performed the best on numeric statistical datasets,and the Gboost algorithm showed the best performance compared to different algorithms on average of all performance metrics.The performance metrics used in these validations were accuracy,F1-score,False Positive Ratio(FPR),False Negative Ratio(FNR),and the training execution time.Moreover,the test results showed the Gboost algorithm got 99.6%,98.8%,0.4%0.13%in accuracy,F1-score,FPR,and FNR,respectively.At training execution time,it obtained 1.41 s for the average of all training time execution datasets.In addition,this paper demonstrated that for the numeric statistical data type,the best results are in the size of the dataset ranging from3000 to 6000 records and the percentage between categories is not less than 50%for each category with the other categories.Furthermore,this paper investigated the effect of Gboost on the WSN lifetime,which resulted in a 32%reduction compared to other Gboost-free scenarios.

A high‑dimensionality‑trait‑driven learning paradigm for high dimensional credit classification

To solve the high-dimensionality issue and improve its accuracy in credit risk assessment,a high-dimensionality-trait-driven learning paradigm is proposed for feature extraction and classifier selection.The proposed paradigm consists of three main stages:categorization of high dimensional data,high-dimensionality-trait-driven feature extraction,and high-dimensionality-trait-driven classifier selection.In the first stage,according to the definition of high-dimensionality and the relationship between sample size and feature dimensions,the high-dimensionality traits of credit dataset are further categorized into two types:100<feature dimensions<sample size,and feature dimensions≥sample size.In the second stage,some typical feature extraction methods are tested regarding the two categories of high dimensionality.In the final stage,four types of classifiers are performed to evaluate credit risk considering different high-dimensionality traits.For the purpose of illustration and verification,credit classification experiments are performed on two publicly available credit risk datasets,and the results show that the proposed high-dimensionality-trait-driven learning paradigm for feature extraction and classifier selection is effective in handling high-dimensional credit classification issues and improving credit classification accuracy relative to the benchmark models listed in this study.

e-Learning新解：网络教学范式的转换

当代学习理论正在发生有意义的变化,学习越来越多的被认为是建构认知、分布式认知和情境认知的过程,而不仅仅是知识的传递;越来越关注知识协同建构的社会本质,知识协同建构受共同体的影响。Web2.0技术在e-Learning中的应用更加关注学习过程的参与性,强调通过师生、生生之间的学习活动来促进知识内容的内化,通过学习活动的序列化,来支持多种实践导向的教学模式。而普适计算技术的发展,使得学习无时不在、无处不在、按需适应成为可能。在学习理论发展和信息技术发展的双重推动下,网络教学正实现从接受认知范式到建构认知范式再到分布式情境认知范式的转换,这反映了e-Learning从技术向教育回归的基本趋势。

Learning by *ing的教学模式与实践

本文论述了Learning by Doing,Learning by Abstracting,Learning by Analogy,Learning byTeaching以及Learing by Simulating等教学模式。分析了其优点,并介绍了我们将其应用于嵌入式软件开发导论和系统结构等课程的效果。文中尤其强调了Learning by Abstracting的重要性。

e-Learning环境学习测量研究进展与趋势——基于眼动应用视角

"日益关注学习测量"已成为教育变革的重要趋势,e-Learning环境学习测量的研究正日益突显多维整体、真实境脉、实时连续的特征。该文通过眼动应用视角透析e-Learning环境学习测量研究的进展与趋势。基于信息加工论、"直接假说"和"眼脑假说",阐释眼动在信息提取、加工、整合以及意义建构中的重要作用。此外,围绕多媒体界面有效性、多媒体学习效果、数字阅读、信息加工过程和学习分析五个方面,对研究内容、研究结果和发展趋势进行梳理与分析。研究认为眼动技术有助于获取具备"大数量、全样本、实时性、微观指向"特性的学习数据,可以深入评估多媒体学习效果和阅读过程,量化注意力、认知过程和学习结果之间的关系,为拓展教育技术的研究手段和应用领域提供了方向指引。