Analyzing Innovative Features in Higher Education in Israel

Though starting late, Israeli higher education has made fruitful achievements, owing much to the innovative thinking ideology in education. This innovative spirit is reflected in the traditional concept of Jewish education, high investments from government and society, the problem-driven features of the education process, and a comprehensive focus on the industrial transformation of research products. In terms of cultivating innovative talents, sound scientific and technological plans, effective guiding mechanisms, unique research learning modes, organic integration of innovation and entrepreneurship education, and innovative measures of military education contribute to an excellent environment and a platform for cultivating students' scientific research and entrepreneurial abilities. The rich content of entrepreneurship and professional education, and the driving effect of the national military service system reflect the innovative features of higher education in Israel and offer positive reference value for the reform and development of innovation and entrepreneurship education in China.

教育心理学研究的特点和趋势——基于2011-2015年《Journal of Educational Psychology》刊载文章的分析

以SSCI数据库中美国《Journal of Educational Psychology》从2011到2015年的384篇文献为基础,可分析总结出教育心理学的研究呈现以下特点及趋势:(1)突显"学习者中心"特点;(2)以中小学生为主要研究对象;(3)研究设计纵深化,研究方法实证化,注重对数据进行深层次分析;(4)更关注解决教育实践中的问题而非建构理论。只有结合具体研究才能深入剖析了教育心理学服务于教育实践的研究特色。

New Characteristics of College Students'Ideological Dynamics and Innovation of Ideological and Political Education Methods based on Psychological Education

Under the influence of social environment,the ideological dynamics of college students have tremendously changed.Among them,the biggest change is that the psychological reception mechanism is very different from the level of self-cognition.Since the report of the 17^(th) National Congress of the Commmist Party of China,the state has formed an agenda to strengthen the ideological and political education in colleges and universities,among which,it is important to reinforce humanistic care and psychological counseling.Mental health education is inextricably linked to the ideological and political education in colleges and universities.Therefore,this article begins in the perspective of psychological education,analyzes the ideological dynamic characteristics of college students,summarizes effective principles and important measures of ideological and political education innovation methods in order to maximize the constructive role of ideological and political education.

Construction and features of somatology in 9-year Qihuang TCM experimental class

Nine-year Qihuang experimental class,which is characterized by nine-year direct Ph.D.,was first opened in 2011,which is an exploration of the way of cultivating high-end characteristic talents of Chinese medicine specialty in our University.Under the background of“quality revolution”in higher education,our University has put forward the cultivation idea of“traditional Chinese medicine+”and“+traditional Chinese medicine”according to the orientation of running a school.Based on this,we have taken the lead in the reform of the curriculum integration of normal body science based on form,tissue and function.From the thought to the practice of teaching reform,we have carried on the thorough exploration and the beneficial attempt to train the Chinese medicine high-end talented persons.Furthermore we have accumulated certain experience.

A Feature Learning-Based Model for Analyzing Students’ Performance in Supportive Learning

Supportive learning plays a substantial role in providing a quality edu-cation system.The evaluation of students’performance impacts their deeper insight into the subject knowledge.Specifically,it is essential to maintain the baseline foundation for building a broader understanding of their careers.This research concentrates on establishing the students’knowledge relationship even in reduced samples.Here,Synthetic Minority Oversampling TEchnique(SMOTE)technique is used for pre-processing the missing value in the provided input dataset to enhance the prediction accuracy.When the initial processing is not done substantially,it leads to misleading prediction accuracy.This research concentrates on modelling an efficient classifier model to predict students’perfor-mance.Generally,the online available student dataset comprises a lesser amount of sample,and k-fold cross-validation is performed to balance the dataset.Then,the relationship among the students’performance(features)is measured using the auto-encoder.The stacked Long Short Term Memory(s-LSTM)is used to learn the previous feedback connection.The stacked model handles the provided data and the data sequence for understanding the long-term dependencies.The simula-tion is done in the MATLAB 2020a environment,and the proposed model shows a better trade-off than the existing approaches.Some evaluation metrics like pre-diction accuracy,sensitivity,specificity,AUROC,F1-score and recall are evalu-ated using the proposed model.The performance of the s?LSTM model is compared with existing approaches.The proposed model gives 89% accuracy,83% precision,86%recall,and 87%F-score.The proposed model outperforms the existing systems in terms of the earlier metrics.

Influencing program satisfaction for undergraduate students in STEM areas: The impact of interdisciplinary features, disciplinary connectedness, faculty support, and examination difficulty

Ensuring program satisfaction for undergraduate students in the areas of science,technology,engineering and mathematics(STEM)matters in student retention and education quality improvement.This study explores how four rarely examined variables support from faculty members,interdisciplinary features of STEM program courses,disciplinary connectedness of STEM program core courses,and examination difficulty impact Chinese STEM undergraduates’program satisfaction.With data from 619 Chinese STEM undergraduates,structural equation modeling shows that course satisfaction partially mediates the impact of support from faculty members on program satisfaction,while fully mediating that of interdisciplinary features of STEM program courses and disciplinary connectedness of STEM program core courses on program satisfaction.Examination difficulty exerts no significant impact on program satisfaction neither directly nor indirectly.Support from faculty members impact course satisfaction significantly stronger for junior and senior students than for freshmen and sophomores,while interdisciplinary features of STEM program courses impact course satisfaction stronger for freshmen and sophomores than for juniors and seniors.The study ends with practical implications for the higher education reform in relevant areas.

Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

Educational Data Mining(EDM)is an emergent discipline that concen-trates on the design of self-learning and adaptive approaches.Higher education institutions have started to utilize analytical tools to improve students’grades and retention.Prediction of students’performance is a difficult process owing to the massive quantity of educational data.Therefore,Artificial Intelligence(AI)techniques can be used for educational data mining in a big data environ-ment.At the same time,in EDM,the feature selection process becomes necessary in creation of feature subsets.Since the feature selection performance affects the predictive performance of any model,it is important to elaborately investigate the outcome of students’performance model related to the feature selection techni-ques.With this motivation,this paper presents a new Metaheuristic Optimiza-tion-based Feature Subset Selection with an Optimal Deep Learning model(MOFSS-ODL)for predicting students’performance.In addition,the proposed model uses an isolation forest-based outlier detection approach to eliminate the existence of outliers.Besides,the Chaotic Monarch Butterfly Optimization Algo-rithm(CBOA)is used for the selection of highly related features with low com-plexity and high performance.Then,a sailfish optimizer with stacked sparse autoencoder(SFO-SSAE)approach is utilized for the classification of educational data.The MOFSS-ODL model is tested against a benchmark student’s perfor-mance data set from the UCI repository.A wide-ranging simulation analysis por-trayed the improved predictive performance of the MOFSS-ODL technique over recent approaches in terms of different measures.Compared to other methods,experimental results prove that the proposed(MOFSS-ODL)classification model does a great job of predicting students’academic progress,with an accuracy of 96.49%.

职业教育国家规划教材内容特征分析——以财经商贸大类为例

内容是教材的灵魂。采用内容分析法,从教材设计理念、教材结构、教材设计、纸数融合四个维度,对财经商贸大类“十四五”职业教育国家规划教材内容特征进行分析。研究发现:认可能力本位教材设计理念,与标准对接不足;“章—节”式目录结构仍占较大比重,教材内容主题分歧明显;标题式项目、理实叠加现象突出;数字资源以二维码融入微课视频为主,存在不一致、使用率不高问题。据此,提出提升高职专业教学标准适应性、内容描述详尽度和时效性,重视专业核心课程教材的技术理论知识开发,以实践性问题为纽带整合理论与实践知识,结合课程内容特性与学习者需求开发教材配套的数字化资源等优化对策。

国家级创新创业教育实践基地建设特征探索——基于首批国家级双创基地建设内容的文本分析

国家级创新创业教育实践基地作为高校实施创新创业教育的实践载体,在创新创业教育体系中发挥着重要作用。该文以100所国家级创新创业教育实践基地资料为研究对象,使用内容分析法探究不同类别高校基地的建设内容特点。结果显示综合类、理工类、职业类、特色类高校基地在建设内容特点上存在差异,并结合具体案例总结出依托优势平台、校企合作、结合特色产业三种基地建设模式,以期为高校创新创业教育提供新思路。

中国特色工程教育体系的演进历程、内涵特征及未来进路

文本回顾了欧洲、美国和中国的工程教育制度演化过程,发现虽然不同国家和地区的工程教育体系各具特色,但总体遵循从技术范式、科学范式到工程范式的变迁特征,且各国工程教育体系的发展与所在国家的工业化和现代化进程密切相关,表明工程教育是现代化转型的重要驱动力之一。面向中国式现代化的战略使命,亟须构建具有中国特色、世界水平的工程教育体系。中国特色工程教育体系是与我国具体国情相适应、服务中国式现代化的工程教育体系,具有深度服务国家重大战略需求、推动工程科学与工程实践有机平衡、促进知识创新与产业实践同步发展、贯通多阶段与融合多类型教育体系以及加速融合数字化与智能化技术等关键内涵特征。基于此,文本从构建工程教育战略整体规划,推动工程教育规模结构质量动态调整,科教产融合驱动教育教学改革等三方面提出加快构建中国特色工程教育体系的未来进路。

“三全育人”行业特色型高校创新实践平台建设探索

培养具有解决复杂系统工程问题能力的创新型工程人才是我国行业特色型高校的根本任务。基于此,行业特色型高校应准确识己,主动思变,以“三全育人”理念为指导,紧扣核心抓手,协同多元主体,从建设思路和具体措施上构建“理论与实践融合,创新与实战并举”的创新实践平台,推动实验室教学高质量内涵式发展。针对行业特色型高校,探索研究递进式多层协同育人创新实践平台的构建非常重要。

以色列STEM教育体系与特征探微

以色列的社会和经济发展与科技创新紧密相关,而其STEM教育体系是科技创新的支撑。以色列构建起由多元主体参与的STEM教育体系,包括政府部门统筹协调STEM教育发展、国防部在以色列STEM教育体系中扮演关键角色、中小学开设丰富多彩的STEM课程、高等教育机构为国家培养大批工程师和科学技术人才、企业和社会组织积极投身STEM教育,并以多样化的项目为载体助推STEM教育发展。以色列国家安全和经济产业发展需要决定其高度重视STEM教育,特殊国情为其STEM教育发展带来阻碍和助推两方面作用。为STEM教育营造有利的政策环境,多元主体协同参与对以色列STEM教育发展具有重要推动作用。

问题导向学习法指导下的英语专业课程思政教育——以“科技英语口译”课程为例

基于课程思政教育思想,认为目前高校专业课程与课程思政融合特色不够突出。以上海理工大学的“科技英语口译”课程为例,基于该课程口译材料内容多为科技知识或事件的特色,以“问题导向学习法”(PBL)为指导理论,引导学生思考口译原文讲述的现实问题及其解决方法。课程实践结果表明,该方法可以促进学生对科技词汇中英文表达的理解和记忆,提高科技口译能力和发现并解决问题的能力,对中外科技史上由现实问题触发的科技进步达到更深刻的认识,从而增强爱国热情,培养国际视野。

课程思政融入量子力学教学的研究与设计

以普通高校课程思政总体要求为研究依据,结合量子力学课程的结构和特色,深度研究和挖掘量子力学课程中蕴含的丰富的思政元素,探索在量子力学课程中开展课程思政教学的具体路径。以课程承载思政,将思政融于课程,达到激发学生的学习兴趣、锻炼学生的思辨能力、培养学生的爱国情怀和勇于追求真理的科学理性精神的目的。

伴生与耦合:新质生产力视域下的职业教育高质量发展

以新技术、新经济、新业态为主要内涵的新质生产力代表了生产力的跃迁,同时也对职业教育提出了更高质量发展的要求,如何与新质生产力共生长成为职业教育在新时代需要面对的重大课题。高质量职业教育与新质生产力具有伴生与耦合的现实逻辑,职业教育高质量发展应具有系统性提质、数智化驱动、嵌入式培养、跨场域协同四个方面的典型特征。为推动我国职业教育面向新质生产力实现高质量发展,我们应坚持以立德树人为根本任务、以协同创新为关键路径、以共建共治为主要抓手、以数字化转型为核心动力、以价值创造为基本导向,高要求打造职业教育新形态、高品质提升人才培养新效能、高水平构建产教融合新格局、高标准激活数智赋能新变量、高站位形成服务发展新优势等行动策略。

我国高等教育实践教学研究热点与进展分析

基于中国知网数据库1998—2022年CSSCI引文数据库,借助CiteSpace,绘制我国高等教育实践教学研究现状、热点和演进特征的科学知识图谱。研究发现,该领域研究仍具较高关注度,但研究机构之间合作密度稀疏。研究内容呈现多元学科特征,从新工科延伸到新文科、课程思政和人工智能。研究热点集中在实践教学制度、人才培养方案、课程体系、实践教学模式、评估与监督体系等方面。但现有研究多强调实践教学对人才培养的作用而缺乏对具体实现路径的系统分析,强调多维度、多层次构建实践教学体系而缺乏对影响因素权重的研究,强调从学科特征出发改革实践教学模式而缺乏对各学科教学内容的比较和分类研究,这些为进一步构建满足教育现代化征程要求和经济高质量发展的高等教育实践教学体系留下空间。

基于多行为特征嵌入记忆网络的知识追踪模型

【目的】为了充分利用交互记录中的学习和遗忘特征,提出了一种基于多行为特征嵌入记忆网络的知识追踪模型(MFKT).该模型考虑了学习过程中的学习和遗忘两种行为。【方法】首先,从交互记录中提取学习和遗忘两大特征,然后将提取到的学习特征通过标量交叉方式嵌入记忆网络,同时将遗忘特征通过向量组合的方式嵌入,用于增强其对于学生答题序列的学习能力。此外,还考虑到了不同学生回答完成后的知识增长差异,在原先记忆网络的基础上增加了一个知识增长层,用于计算学生答题得到的知识增长。【结果】通过在公开数据集上的实验表明,MFKT更加符合学生的真实学习规律,能够实现对学生知识状态更加精准的追踪。

“三全育人”背景下职业院校长跑俱乐部人才质量评估模型构建

在“三全育人”的背景下,体育、艺术类的教学质量得到了重视.鉴于目前网络上没有符合长跑俱乐部人才质量评估的模型,提出了一种基于特征加权聚类算法的长跑俱乐部人才质量评估模型.该模型使用模糊K原型聚类作为核心算法,为了提高评估准确率,引入特征加权算法进行优化.此外,研究中发现模型运行速度较慢,难以达到全局最优,因此采用爬山算法特高模型收敛速度.实验结果表明,模型在自创数据集上评估正确率93.16%,聚类纯度87.07%,平均输出时间50.03s.与其他同类模型相比,该模型在运行时间、评估正确率、聚类效果等多项性能上更优,具有先进性.

行业特色高校出版社编辑力建设及其提升策略探析

编辑力提升是出版社高质量发展的关键。处于新发展格局下的大学出版社特别是行业特色高校出版社,要坚持守正创新,坚持特色发展,加强编辑队伍建设,在时代精神领悟力、教育出版担当力、科技信息敏锐力等七个方面进行不断提升,打造适应出版特色的编辑力;要立足并扎根母体大学,充分发挥大学的人才优势、学科优势和行业特色专业所蕴含的内容资源优势,出版思想精深、内容精湛的精品出版物,打造有影响力的特色出版物品牌,讲好大学育人故事、科技创新故事,传播先进文化、科学知识、科学精神,助力大学“双一流”建设。