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。

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。

基于E-learning的《建筑材料》课程混合式教学模式新构建

随着E-learning在高校中的教与学中的应用及研究不断发展,以E-learning为基础的高校混合式学习模式受到了广泛的关注。就其平台的发展而论,目前许多专用的网络教学平台会出现不同程度的弊端,文章以腾讯QQ,土豆、优酷等视频网站,博客,知网等免费网络服务支持的教学行为特征为基础,结合《建筑材料》专业基础课实例,提出几种新的混合式学习模式,并对模式进行优缺点比较,以其对后续研究有所帮助。

The Brief on English Learning Situation in China

English as the world’s lingua franca,beyond all doubt,is the first choice of all the language learners world wide.The whole English learning movement has produced a profound influence on Chinese culture.This paper is an attempt to outline the role English plays in China and its further development,and based on this,suggests the oncoming paradigm shift of English learning in China.

Machine Learning for 5G and Beyond:From ModelBased to Data-Driven Mobile Wireless Networks

During the past few decades,mobile wireless communications have experienced four generations of technological revolution,namely from 1 G to 4 G,and the deployment of the latest 5 G networks is expected to take place in 2019.One fundamental question is how we can push forward the development of mobile wireless communications while it has become an extremely complex and sophisticated system.We believe that the answer lies in the huge volumes of data produced by the network itself,and machine learning may become a key to exploit such information.In this paper,we elaborate why the conventional model-based paradigm,which has been widely proved useful in pre-5 G networks,can be less efficient or even less practical in the future 5 G and beyond mobile networks.Then,we explain how the data-driven paradigm,using state-of-the-art machine learning techniques,can become a promising solution.At last,we provide a typical use case of the data-driven paradigm,i.e.,proactive load balancing,in which online learning is utilized to adjust cell configurations in advance to avoid burst congestion caused by rapid traffic changes.

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.

Knowledge Learning With Crowdsourcing:A Brief Review and Systematic Perspective

Big data have the characteristics of enormous volume,high velocity,diversity,value-sparsity,and uncertainty,which lead the knowledge learning from them full of challenges.With the emergence of crowdsourcing,versatile information can be obtained on-demand so that the wisdom of crowds is easily involved to facilitate the knowledge learning process.During the past thirteen years,researchers in the AI community made great efforts to remove the obstacles in the field of learning from crowds.This concentrated survey paper comprehensively reviews the technical progress in crowdsourcing learning from a systematic perspective that includes three dimensions of data,models,and learning processes.In addition to reviewing existing important work,the paper places a particular emphasis on providing some promising blueprints on each dimension as well as discussing the lessons learned from our past research work,which will light up the way for new researchers and encourage them to pursue new contributions.

On the effectiveness of formal instruction in foreign language teaching and learning

Sometimes foreign language teachers find formal instructions unrewarding. This paper intends to find answers to the problems found in the English teaching classroom at university level in China by reviewing the SLA theories on formal instruction and some pedagogical implications drawn from the findings are discussed.

DAO to HANOI via DeSci: AI Paradigm Shifts from AlphaGo to ChatGPT

From AlphaGo to ChatGPT,the field of AI has launched a series of remarkable achievements in recent years.Analyzing,comparing,and summarizing these achievements at the paradigm level is important for future AI innovation,but has not received sufficient attention.In this paper,we give an overview and perspective on machine learning paradigms.First,we propose a paradigm taxonomy with three levels and seven dimensions from a knowledge perspective.Accordingly,we give an overview on three basic and twelve extended learning paradigms,such as Ensemble Learning,Transfer Learning,etc.,with figures in unified style.We further analyze three advanced paradigms,i.e.,AlphaGo,AlphaFold and ChatGPT.Second,to enable more efficient and effective scientific discovery,we propose to build a new ecosystem that drives AI paradigm shifts through the decentralized science(DeSci)movement based on decentralized autonomous organization(DAO).To this end,we design the Hanoi framework,which integrates human factors,parallel intelligence based on a combination of artificial systems and the natural world,and the DAO to inspire AI innovations.

Energy Sources Driven Electricity Production: A Global Tactical and Strategical Paradigm

Energy forecasting for electricity productivity is the process of applying statistics with possible Quantum or Classical Computing with help from new innovative techniques offered by artificial intelligence to make predictions about consumption levels.This kind of computation presents corresponding utility costs in both the tactical and strategical or short term and long term.Energy forecasting models take into account historical data,trends,weather inputs,tariff structures,and occupancy schedules in the urban city due to population growth,etc.to make predictions.Additionally,energy forecasting as future paradigm is driven by electricity production demand and it is a cost-effective technique to predict future energy needs,which is a paradigm to achieve demand and supply chain equilibrium based on available energy both renewable and non-renewable sources.

教师专业学习社群研究:范式、旨趣与论述

教师专业学习社群是当前教师专业发展与教师学习研究领域的一个主要议题。历经三十余年的演进,建立和发展教师专业学习社群以促进教师学习、提升学校效能已成为各主要国家教育政策与学校改革的共识。与之相应,研究者对不同情境中的教师专业学习社群也已开展了广泛且深入的探究,然而已有研究都未能系统检视教师专业学习社群研究与实践所持的价值立场、求知兴趣与研究意图。借鉴哈贝马斯所界定的认知旨趣,我们归纳了已有教师专业学习社群研究所遵循的三种研究范式(技术性、实践性与批判性研究范式)及其衍生的六种研究论述(重构论述、效能论述、社群论述、文化论述、批判论述与转变论述),总结了不同研究范式下研究者对教师专业学习社群所持的基本主张、研究重点与发展概况。基于本文所做的范式性分析,我们讨论了研究者应该如何看待三种研究范式之间的关系,并展望了不同范式中教师专业学习社群研究未来可能的发展方向。

学习科学的学习观与方法论:范式与实践

关于“人是如何学习的”这个问题,学术界经历了从关注认知和脑的“学习的科学”到关注情境的“学习科学”的变迁。这种变迁背后的逻辑与研究者所秉承的学习观、研究范式及相关实践息息相关。行为主义与第一代认知科学的发展推动了实证主义范式与实验方法的蓬勃发展;第二代认知科学强调真实情境,建构观和解释主义范式受到重视;20世纪90年代起,旨在解决真实问题的实用主义范式与设计研究成为学习科学重要的研究方法。当下,大数据推动下学习分析领域的发展催生了指向复杂性的“新经验主义”范式。以复杂系统建模为主的数据密集型研究旨在打开学习发生的“黑箱”,深入探索学习的交互关系和演化机制。文章系统回顾与比较了学习科学领域四大发展阶段中的学习观、研究范式与相关实践,旨在推动学习科学理论与实践融合创新。

基于学业质量的义务教育体育与健康课程学习评价的内涵解析、应然取向与实践框架

体育与健康课程学习评价是确保体育与健康课程改革落到实处的关键举措和重要保障。《义务教育体育与健康课程标准(2022年版)》研制了学业质量标准,为义务教育体育与健康课程学习评价改革提供了依据。在系统分析基于学业质量的体育与健康课程学习评价的内涵和应然价值取向的基础上,以评价三角理论为理论基础,构建了认知、观察、解读、运用“四位一体”的体育与健康课程学习评价实践框架,其中学业质量标准是认知框架设计的依据,学习评价指标体系是学习评价的具体内容,而基于证据推理的过程模型是体育与健康课程学习评价的基本范式。

高职院校基于“帮学课程”的范式研究

高职“帮学课程”范式是针对“以学生为中心”的教育教学。通过电力专业“帮学课程”范式的实施,对“帮助学生学习”的核心理念开展教育教学、加强产教融合一体的实践性操作、提升考核评价螺旋式指导目标的研究水平,促进教育教学帮助学生全面、个性化、自助式发展,对学生的学习形成强有力的指引,具有现实性、针对性和强化学生职业素养的价值。

引导路径下高职教育模式改革的困境和出路

虽然新《职业教育法》赋予职业教育与普通教育同等地位,但是我国高职教育始终没有摆脱“学科教育”路径依赖,存在重数量轻质量、重专业申报轻内涵建设、重知识轻能力等问题。美国社区学院引导路径改革围绕“一中心、两重点、三支撑、四目标”展开,重点是帮助学生成功并培养学生的有效学习能力。我国高职教育可以借鉴美国经验,从构建学习范式、帮助学生规划职业和学业路径、重塑聚焦能力培养的知识体系、建立学习质量评估机制等方面破解发展困境。

元宇宙赋能职业教育教与学范式转型目标、内容与实施路径

元宇宙作为新一代网络信息技术的典型代表,为教育领域的深刻变革带来了机遇和挑战。通过剖析元宇宙赋能职业教育教与学范式转型的建构特征,为教育元宇宙建设提供理论指导和现实依据,助推教育元宇宙建设实践。基于国内外教育元宇宙最新研究成果及实践探索,以具身认知理论、沉浸理论、建构主义等教育理论为依据,分析元宇宙的特征与知识结构,深入探讨虚拟身份、沉浸式体验、开放性、知识互动等元宇宙特性与实现沉浸式、个性化、全场景交互式学习的重要关系,以洞察元宇宙赋能职业教育教与学范式转型的目标、内容与实施路径。元宇宙视角下教与学范式有别于传统教学模式,有三个重要目标:实现个性化教学、实现大规模超域协同学习、实现沉浸式学习;元宇宙对教学过程、教学组织形式、教师角色等将产生颠覆性影响。通过构建个性化学习过程结构、构建教育元宇宙模型、构建课堂视场结构和组成结构、建立动态智能学习评推系统,明晰了元宇宙赋能职业教育教与学范式转型的实施路径。

重树经学典范:曹元弼“郑注配经”的思想要义

百年来中国人文学术与哲学思想的发展,在研究内容、态度、方法上均不断变化,在接受的同时亦自我调整。20世纪60、70年代“范式转移”的研究观念论开始大行其道,亦渗透进近40年来中国学术各类之中,与“新变”一词语义互参,成为当下学术思路不断求新求变的集体风尚,形成集中一偏的思想困局。相对于集大成式的学术高度的追求,无疑是背道而驰。以客观态度审视学术研究主体性的范式观念,本来早存在于中国经学的传统之中,透过范式的“会通”构成强大的学术人格魅力,以顶天立地的文化精神为时代缔构正面的建设力量,从而体现集体幸福的愿望,形成“立”的精神动力,与“范式转移”所导致的“破”,实在是两副照面。因此特别彰显民国时代以来,在学制“废经”而学术全面西化的时代情景中,于学制外所存在坚持不懈的终身治经之士如曹元弼,其全力重建经学的典范价值,坚持树立中国文化的恒常属性,重建中国文化的高尚情操。正视曹元弼“范式重建”的意义,无疑能增强社会上有所“立”的正面价值。

医学教育“评学融合”新理念:从理论到实践

教育评价事关教育发展方向,有什么样的评价指挥棒,就有什么样的办学导向。随着教育评价理论的发展和教育技术赋能水平的提升,全球教育评价改革的新趋势不断涌现。文章系统梳理4个教育评价时期的演进历程和实践经验,厘清教育评价范式的转型动因和发展方向,结合我国教育评价改革的探索经验和新时代教育评价改革的新方向、新要求,从人工智能技术助力评学融合新模式建设、教育评价赋能学生全面发展、评价方式和主体多元化、关注增值评价发展等方面进行了总结,为我国医学教育考试评价改革创新提供借鉴和参考。

模糊认知图学习算法及应用综述

模糊认知图(Fuzzy cognitive map, FCM)是建立在认知图和模糊集理论上的一类代表性的软计算理论,兼具神经网络和模糊决策两者的优势,已成功地应用于复杂系统建模和时间序列分析等众多领域.学习权重矩阵是基于模糊认知图建模的首要任务,是模糊认知图研究领域的焦点.针对这一核心问题,首先,全面综述模糊认知图的基本理论框架,系统地总结近年来模糊认知图的拓展模型.其次,归纳、总结和分析模糊认知图学习算法的最新研究进展,对学习算法进行重新定义和划分,深度阐述各类学习算法的时间复杂度和优缺点.然后,对比分析各类学习算法在不同科学领域的应用特点以及现有的模糊认知图建模软件工具.最后,讨论学习算法未来潜在的研究方向和发展趋势.