English Writing Autonomy and Literacy among Chinese College Students

The purpose of this study is to examine the current situation of English writing autonomy and literacy among Chinese college students,to analyze their performance,scale,and distribution as well as influencing factors,and to explore the correlation between English writing autonomy and literacy.This study will provide an in-depth analysis of the current situation of Chinese college students’English writing autonomy and literacy,reveal the scale,distribution,and influencing factors,as well as put forward strategies to enhance them,so as to provide a reference for the reform and development of college English education.

The Role and features of Student Peer Feedback in Interpreting Training——Preliminary Findings of a Survey of both trainers and students

The project delves into the preliminary findings of a survey of both trainers and students on the practice of using student peer feedback in interpreting practice.It first explains the theoretical foundation which justifies the use of peer feedback in interpreting practice,the research methodology and data collection.Then it brings forth specific findings concerning the implementation of peer feedback in the interpreting class followed by discussions of the role and features of student peer feedback as a means to help students ready for the booth.Analysis of the results shows that peer feedback in interpreting practice keeps students on-task,attentive and help them spot their own problems.Trainers and students themselves point to similar features of student peer feedback as focusing on comprehension of the original,word choice and numbers.The preliminary findings of the survey demonstrate the roles and features of student peer feedback in interpreting practice and point to the possible way of enhancing student’s learning curve through more effective peer feedback.

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.

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%.

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.

On the Development of Chinese College Students'English Listening Autonomy

The article discusses issues related to listening autonomy including the definition of listening and autonomy.The foster of listening involve the collaborated effort of the listening teacher and student.Meanwhile three specific ways to improve listen ing autonomy are presented-keeping a listening diary,extensive listening and forming a listening online community.

The Cultivation of Students' Autonomy in CALL Context

since the middle 1950s,CALL(computer assisted language learning) has become an advanced medium for language learning,which makes the language teaching and learning more convenient.The main characteristics of CALL are opening,initiative,vigorous and collaborative.In language teaching and learning assisted by computer,students are the center.However,the internet is complicated and multiple.It's hard for some students to concentrate on the language learning in CALL environment.In that case,the enhancement of the students 'autonomy is very important.The cultivation of students 'autonomy in CALL environment is crucial for students as well as teachers.Simultaneously,teachers'role in CALL context is also significant.This essay aims at discussing how to cultivate the students'autonomy in CALL context.

Prevalence of asthenopia and its risk factors in Chinese college students

·AIM: To determine the prevalence of asthenopia and identify any associated risk factors in the college students in Xi’an, China. · METHODS: From April to September 2012, 1 500 students from five universities in Xi’an were selected according to a multi -stage stratified cluster sampling method. Data on demographic features, lifestyle or dietary habits, health status, living environment conditions, sleep and mental status, and asthenopia symptoms were collected through a self -administered validated questionnaire. Univariate logistic regression and multivariate logistic regression analysis modified by the factor analysis were performed to evaluate risk factors for asthenopia. ·RESULTS: Fifty-seven percent of the college students complained of asthenopia. Statistically significant risk factors for asthenopia in the univariate analysis included 13 variables. Multivariate analysis revealed a significant relationship between the use of computer and asthenopia (OR 1.21, 95% CI: 1.09 to 1.35). Good sleep and mental status (OR 0.86, 95% CI: 0.76 to 0.97), good living environment conditions (OR 0.67, 95% CI: 0.60 to 0.76), and high intake of green leafy vegetables (OR 0.89, 95% CI: 0.80 to 0.98) were found to be strong predictors of decreasing the occurrence of asthenopia complaints. ·CONCLUSION: Asthenopia symptom appeares to be common among college students; and it is strongly associated with computer use, psychosocial state, environment conditions and dietary habits, suggesting that additional studies are warranted to verify these risk factors and establish prevention guidelines, especially for college students. ·

Building Features in Schools That Influence Academic Performance

School decision makers are faced with a great many decisions when considering a school renovation or new school building. All stakeholders want a building that is safe and provides an optimal learning environment. However, it is often difficult to know which building features will have the greatest effect on student learning. Because of a limited understanding of the relationship between individual building features and student learning, researchers at the University of Oklahoma hope to explore how building components influence student and teacher performance. This paper explores the importance of school building features that can be designed and changed during a renovation project. The hope is to one day determine which features have the greatest impact on student test scores. The research team believes that although it is difficult to find the exact relationship between each building features and student outcomes with one study, if multiple users repeat the same or similar studies, hopefully we will one day know the effect of these building features. In order to develop feature building users survey and physical assessment tools, it was necessary for investigators to develop a list of important building features and their associated definitions in layman terms. This was accomplished through utilization and conducting of a CAB （community advisory board） and subject matter expert materials. In addition, previous research relating to different school building features and their associations with student performance were reviewed. To define and narrow the list the researchers, community educational, and building professionals rated based on their professional experience, how directly related each feature is to student performance. The building feature list serves as a starting point to determine which features should be analyzed in a later phase of the project. It is hoped that resulting tools based on the work of this project can be used by school decision-makers and researchers to access building features that have been identified through research as being important for student and teacher performance.

On Developing Students' Communicative Competence in English Classes of Higher Vocational Colleges

This article introduces the theory of communicative competence and explores some possible teaching strategies for developing the students' communicative competence according to the students' features in higher vocational colleges.

坚守与超越:中职语文统编教材适用性的价值归因与表现

中职语文统编教材的体例、选文和具体内容在不断的守正与创新矛盾转化中渐趋完善成型。该套教材呼应了时代发展对人才培养提出的新要求,强化了体现语文学科本质的内容设计,突出了职业教育类型特色在语文教材中的个性体现,创新了以学生学习为主体的内容组织方式,助推了教师教学行为的根本转变,体现了教材建设的科学性、实用性、创新性。从教材的不变与变、坚守与超越中明确其内在的价值属性,是用对教材,用好教材,用活教材的前提和基础。

在反思中成长:质性研究方法的教与学

本文收录了作者在教授“质性研究方法”课程第6-8次课后写的反思笔记,包括如下主题:回应学生发言需要教师的实践智慧,师生相互理解是良好教学的保证,教师是否开放课堂涉及教学自主等伦理问题。这些主题涉及课堂教学中教师们通常会遭遇的困境,具有深入探讨的理论潜力和实践价值。通过分享作者的体验和反思,本文希望邀请读者对这些问题进行更加深入的思考和对话。

父母自主支持对大学生生涯适应力的影响:主动性人格和个人成长主动性的作用

为了探讨父母自主支持对大学生生涯适应力的影响及其作用机制,采用父母自主支持量表、主动性人格量表、个人成长主动性量表、生涯适应力量表对961名大学生进行问卷调查。结果发现,父母自主支持不仅可以直接影响大学生生涯适应力,还可以通过主动性人格的单独中介作用、个人成长主动性的单独中介作用、主动性人格与个人成长主动性的链式中介作用间接影响大学生生涯适应力。

基于教师-学生模型的点云目标检测算法

针对现有点云目标检测算法对室外自动驾驶场景中遮挡物体检测精度低的问题,提出一种基于知识蒸馏和注意力增强的点云目标检测算法。以CIA-SSD模型为基础,设计了一种基于教师-学生模型的密集特征生成模块,提出基于交并比匹配策略的密集数据生成方法,将稀疏特征转换为密集特征。密集特征生成模块位于学生模型中,学生模型在教师模型生成的软目标监督训练下,推断出完整的密集目标特征,实现目标特征的补全;在教师模型中设计空间注意力和通道注意力机制,增强密集目标点云,提升特征图的质量。在KITTI数据集上的验证实验结果表明:与SE-SSD模型和CIA-SSD模型相比,本文提出的算法保持了单阶段目标检测速度的优势,同时明显提升了检测精度。

公费师范生教师职业认同对生活满意度的影响:有调节的中介模型

目的:探究公费师范生教师职业认同对生活满意度的影响,以及压力知觉在其中的中介作用和父母自主支持的调节作用。方法:采用师范生职业认同感量表、压力知觉量表、父母自主支持量表和生活满意度量表对某本科师范高校779名公费师范生进行调查。结果:(1)教师职业认同对生活满意度具有显著的正向预测作用(β=0.347,P<0.001);(2)压力知觉在教师职业认同和生活满意度的关系中起部分中介作用,中介效应占总效应的24.79%;(3)父母自主支持在教师职业认同与生活满意度(β=0.013,P<0.01)、教师职业认同与压力知觉(β=-0.006,P<0.05)之间起调节作用,父母自主支持水平越低,教师职业认同对生活满意度和压力知觉影响越大。结论:公费师范生教师职业认同不仅可以直接影响生活满意度,还可以通过压力知觉间接影响生活满意度,且父母自主支持调节教师职业认同对生活满意度的直接预测作用及压力知觉在二者关系间中介作用的前半段路径。

高校课程思政育人效果如何?——基于大学生满意度、自主性与政治素养的实证分析

高质量推进课程思政建设是近年来高校本科课程教学创新的重要工作,了解高校课程思政的育人情况对于提升课程思政育人实效具有重要意义.对5所高校538名大学生的问卷调查数据进行分析,结果发现:部分大学生认为教师实施课程思政的频率不高,大学生对课程思政教学的满意度参差不齐;大学生对教学的满意度不仅直接影响大学生政治素养提升,还通过大学生学习自主性间接影响其政治素养提升.因此,学校要加强课程思政实施的监督,形成全方位的课程思政评价体系;教师要坚持“以学生为中心”,强化课程思政教学能力,提升课程思政教学满意度;大学生应注重课程思政学习自主性培养,深化课程思政的价值感知,由此发挥学校、教师、学生三方力量,共同推进高校课程思政的高质量发展.

学生自治视域下一站式学生社区建设的价值意蕴、现实困境与建设路径

学生自治视角下的高校学生社区建设在推动学生全面发展方面具有重要意义。研究通过深入探讨高校一站式学生社区建设的现实困境,提出一系列在微观、中观和宏观层面的建设路径,以推动高校一站式学生社区建设的全面发展。在微观层面,强调激发学生内在动力和潜能的重要性;在中观层面,提倡构建师生和谐的生态系统;在宏观层面,建议完善以学生为中心的培养体系。研究旨在为高校教育改革和构建更加活跃、有创新性的一站式学生社区建设提供新的思路和实践方案。

大学生双元自主性与学校适应的关系

为给大学生心理与行为的研究及干预提供参考,通过方便抽样方法选取山东省某大学1 673名大学生,采用青少年双元自主性量表、中国大学生适应量表进行调查,分析大学生双元自主性与学校适应的关系。大学生个体化自主、关系自主与学校适应总分都高于理论中值。大学生个体化自主、关系自主得分与学校适应总分呈正相关。个体化自主在性别、年级和是否独生上差异显著;女生的择业适应得分高于男生;学校适应总分在年级与生源地上差异显著;多重线性回归分析显示,进入回归分析模型且对学校适应总分影响作用最大的因素为个体化自主,其次为关系自主、性别等。大学生个体化自主、关系自主发展和学校适应整体水平良好。大学生个体化、关系自主是学校适应水平的影响因素。现阶段应加强对大学生个体化与关系自主能力的培养,并及时采取心理干预措施调整其学校适应不良的状况。

教师自主支持与高中生选科成熟度的关系:职业生涯探索的中介及自主决定动机的调节作用

以845名高中生为被试,采用问卷法考察职业生涯探索在教师自主支持与高中生选科成熟度关系中的中介作用以及自主决定动机在这一中介过程中的调节作用。结果表明:(1)教师自主支持显著正向预测选科成熟度;(2)职业生涯探索在教师自主支持与选科成熟度之间起部分中介作用;(3)自主选择动机在教师自主支持通过职业生涯探索影响选科成熟度的中介模型前半段未起调节作用;控制选择动机在教师自主支持通过职业生涯探索影响选科成熟度的中介模型前半段起负向调节作用。

教师自主支持与小学生的科学学习无畏:学习兴趣和创造力效能感的中介作用

学习无畏(intellectual risk-taking)是指学生在学习中勇于尝试、不怕失败,这种品质对学生的科学学习和创造力发展非常重要。本研究基于自我决定理论和自我效能理论,采用自我报告式调查问卷,对南方某省方便取样的四所学校的757名四、五、六年级小学生进行调查,探究教师自主支持与学生科学学习无畏的关系,以及科学学习兴趣和创造力自我效能感的中介作用。结果表明:(1)教师自主支持行为对学生的学习无畏具有显著的正向预测作用。学生感受到的教师自主支持越多,越可能在科学学习中勇于尝试、不怕失败;(2)在教师自主支持与学生科学学习无畏之间的关系中,学生的科学学习兴趣和创造力效能感起完全中介作用。即教师自主支持行为可能通过提高学生对科学学习的兴趣和创造力自我效能感,间接提高学生的学习无畏水平。