Research on the Lifting Path of Data Literacy Ability of Applied University Teachers under the Perspective of Organizational Learning

With the arrival of the big data era, the modern higher education model has undergone radical changes, and higher requirements have been put forward for the data literacy of college teachers. The paper first analyzes the connotation of teacher data literacy, and then combs through the status quo and dilemmas of teachers’ data literacy ability in applied universities. The paper proposes to enhance the data literacy ability of teachers from the perspective of organizational learning. Through building a digital culture, building a data-driven teaching environment, and constructing an interdisciplinary learning community to further promote the application of the theory and practice of datafication inside and outside the organization, and ultimately improve the quality of teaching.

Effects of Perceived Teacher Support on Student Behavioral Engagement in the Blended Learning Environment:Learning Experience as a Mediator

Blended learning(BL)has been widely adopted to improve students’academic achievements in higher education.However,its success relies mainly on student engagement,which plays an essential role in active learning and provides a rich understanding of students’experiences.The study utilized three self-designed scales-the Teacher Support Scale,Student Engagement Scale,and Student Learning Experience Scale-to gauge and examine the impact and relationship between perceived teacher support,student behavioral engagement,and the intermediary role of learning experiences.A cohort of 899 college students undertaking the obligatory College English course through BL modes across five Chinese universities actively participated by completing a comprehensive questionnaire.The results showed significant correlations between perceived teacher support,learning experience,and behavioral engagement.Perceived teacher support significantly predicted students’behavioral engagement,with socio-affective support exerting the most substantial predictive effects.All predictive effects were partially mediated by learning experience(learning mode,online resources,overall LMS-based learning,interaction with their instructor and peers,and learning outcome).The influence of perceived teacher support on behavioral engagement differed between students who reported the most positive(vs.negative)learning experiences.Suggestions for further research are offered for consideration.

The Appeals,Dilemmas,and Pathways of Enabling Rural Teachers’Professional Development Through Emerging Technologies

With the rapid advancements in technology,especially in digitalization and intelligence,numerous modern technologies have poured into rural schools,effectively improving their informatization conditions.Nevertheless,these technologies remain detached from rural teachers,failing to significantly enhance the quality of education and teaching in rural areas.Rural education is a crucial aspect of ensuring balanced development in education.The question of how to enhance rural teachers’technological application abilities and fully leverage the positive role of technology in rural education and teaching has become a significant topic of current research on rural education issues.To better address this question,this study conducted a thorough examination of the specific appeals of rural teachers in the process of technology enablement.It was discovered that rural teachers generally face dilemmas such as insufficient technological application abilities,difficulties in obtaining quality teaching resources,and the lack of continuous technical support and update mechanisms.Based on these findings,specific pathways such as strengthening rural teacher training,optimizing the allocation of educational resources,and establishing mechanisms for continuous technical support and updates are proposed to aid in the high-quality development of rural education.

Reading Loss in Arabic Language During COVID-19 in the UAE and Proposed Solutions:The Perspectives of Primary-Grade Arabic Language Teachers

The COVID-19 pandemic caused significant disruptions in the field of education worldwide,including in the United Arab Emirates.Teachers and students had to adapt to remote learning and virtual classrooms,leading to various challenges in maintaining educational standards.The sudden transition to remote teaching could have a negative impact on students’reading abilities,especially in the Arabic language.To gain insight into the unique challenges encountered by Arabic language teachers in the UAE,a survey was conducted to explore their assessment of teaching quality,student-teacher interaction,and learning outcomes amidst the COVID-19 pandemic.The results of the survey revealed a significant decline of student reading abilities and identified several major issues in online Arabic language teaching.These issues included limited interaction between students and teachers,challenges in monitoring students’class participation and performance,and challenges in effectively assessing students’reading skills.The results also demonstrated some other challenges faced by Arabic language teachers,including a lack of preparedness,a lack of subscription to relevant platforms,and a lack of resources for online learning.Several solutions to these challenges are proposed,including reevaluating the balance between depth and breadth in the curriculum,integrating language skills into the curriculum more effectively,providing more comprehensive teacher professional development,implementing student grouping strategies,utilizing retired and expert teachers in specific content areas,allocating time for interventions,and improving support from both teachers and parents to ensure the quality of online learning.

The Role of Secondary School Teacher Training in Their Teaching Practices-A Secondary Publication

This work aims to analyze the effect exerted by the initial and continuous training of secondary school teachers in France on their teaching practices.For this,we carried out a secondary analysis of data from the TALIS survey(Teaching and Learning International Survey),conducted in 2013 in 34 countries,including France.In particular,we focus on practices related to the teaching methods used,classroom management,how to communicate,and the evaluative practices of teachers.In support of binary logistic regression models,we showed that the variables related to initial training play a weak role,and even exert a negative effect on certain pedagogic practices,leading to a reflection on the renovation of this training.On the other hand,our analyses highlighted the greater weight played by certain continuing education actions.

Recommendation Method for Contrastive Enhancement of Neighborhood Information

Knowledge graph can assist in improving recommendation performance and is widely applied in various person-alized recommendation domains.However,existing knowledge-aware recommendation methods face challenges such as weak user-item interaction supervisory signals and noise in the knowledge graph.To tackle these issues,this paper proposes a neighbor information contrast-enhanced recommendation method by adding subtle noise to construct contrast views and employing contrastive learning to strengthen supervisory signals and reduce knowledge noise.Specifically,first,this paper adopts heterogeneous propagation and knowledge-aware attention networks to obtain multi-order neighbor embedding of users and items,mining the high-order neighbor informa-tion of users and items.Next,in the neighbor information,this paper introduces weak noise following a uniform distribution to construct neighbor contrast views,effectively reducing the time overhead of view construction.This paper then performs contrastive learning between neighbor views to promote the uniformity of view information,adjusting the neighbor structure,and achieving the goal of reducing the knowledge noise in the knowledge graph.Finally,this paper introduces multi-task learning to mitigate the problem of weak supervisory signals.To validate the effectiveness of our method,experiments are conducted on theMovieLens-1M,MovieLens-20M,Book-Crossing,and Last-FM datasets.The results showthat compared to the best baselines,our method shows significant improvements in AUC and F1.

Multi-layer perceptron-based data-driven multiscale modelling of granular materials with a novel Frobenius norm-based internal variable

One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural networks(RNNs)have been extensively applied to capture history-dependent constitutive responses of granular materials,but these multiple-step-based neural networks are neither sufficiently efficient nor aligned with the standard finite element method(FEM).Single-step-based neural networks like the multi-layer perceptron(MLP)are an alternative to bypass the above issues but have to introduce some internal variables to encode complex loading histories.In this work,one novel Frobenius norm-based internal variable,together with the Fourier layer and residual architectureenhanced MLP model,is crafted to replicate the history-dependent constitutive features of representative volume element(RVE)for granular materials.The obtained ML models are then seamlessly embedded into the FEM to solve the BVP of a biaxial compression case and a rigid strip footing case.The obtained solutions are comparable to results from the FEM-DEM multiscale modelling but achieve significantly improved efficiency.The results demonstrate the applicability of the proposed internal variable in enabling MLP to capture highly nonlinear constitutive responses of granular materials.

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

Salivary metabolites are promising noninvasive biomarkers of druginduced liver injury

BACKGROUND Drug-induced liver injury(DILI)is one of the most common adverse events of medication use,and its incidence is increasing.However,early detection of DILI is a crucial challenge due to a lack of biomarkers and noninvasive tests.AIM To identify salivary metabolic biomarkers of DILI for the future development of noninvasive diagnostic tools.METHODS Saliva samples from 31 DILI patients and 35 healthy controls(HCs)were subjected to untargeted metabolomics using ultrahigh-pressure liquid chromatography coupled with tandem mass spectrometry.Subsequent analyses,including partial least squares-discriminant analysis modeling,t tests and weighted metabolite coexpression network analysis(WMCNA),were conducted to identify key differentially expressed metabolites(DEMs)and metabolite sets.Furthermore we utilized least absolute shrinkage and selection operato and random fores analyses for biomarker prediction.The use of each metabolite and metabolite set to detect DILI was evaluated with area under the receiver operating characteristic curves.RESULTS We found 247 differentially expressed salivary metabolites between the DILI group and the HC group.Using WMCNA,we identified a set of 8 DEMs closely related to liver injury for further prediction testing.Interestingly,the distinct separation of DILI patients and HCs was achieved with five metabolites,namely,12-hydroxydodecanoic acid,3-hydroxydecanoic acid,tetradecanedioic acid,hypoxanthine,and inosine(area under the curve:0.733-1).CONCLUSION Salivary metabolomics revealed previously unreported metabolic alterations and diagnostic biomarkers in the saliva of DILI patients.Our study may provide a potentially feasible and noninvasive diagnostic method for DILI,but further validation is needed.

Computer vision-aided DEM study on the compaction characteristics of graded subgrade filler considering realistic coarse particle shapes

The compaction quality of subgrade filler strongly affects subgrade settlement.The main objective of this research is to analyze the macro-and micro-mechanical compaction characteristics of subgrade filler based on the real shape of coarse particles.First,an improved Viola-Jones algorithm is employed to establish a digitalized 2D particle database for coarse particle shape evaluation and discrete modeling purposes of subgrade filler.Shape indexes of 2D subgrade filler are then computed and statistically analyzed.Finally,numerical simulations are performed to quantitatively investigate the effects of the aspect ratio(AR)and interparticle friction coefficient(μ)on the macro-and micro-mechanical compaction characteristics of subgrade filler based on the discrete element method(DEM).The results show that with the increasing AR,the coarse particles are narrower,leading to the increasing movement of fine particles during compaction,which indicates that it is difficult for slender coarse particles to inhibit the migration of fine particles.Moreover,the average displacement of particles is strongly influenced by the AR,indicating that their occlusion under power relies on particle shapes.The dis-placement and velocity of fine particles are much greater than those of the coarse particles,which shows that compaction is primarily a migration of fine particles.Under the cyclic load,the interparticle friction coefficientμhas little effect on the internal structure of the sample;under the quasi-static loads,however,the increase inμwill lead to a significant increase in the porosity of the sample.This study could not only provide a novel approach to investigate the compaction mechanism but also establish a new theoretical basis for the evaluation of intelligent subgrade compaction.

The Effectiveness of Group Cooperative Learning Method in Badminton Teaching in Colleges and Universities

In college badminton teaching,teachers utilize the group cooperative learning method,which not only helps to improve students’badminton skill level but also cultivates their teamwork spirit,communication skills,and self-management ability unconsciously.In view of this,this paper mainly describes the significance of applying the group cooperative learning method in college badminton teaching,analyzes the current problems in college badminton teaching,and aims to discover effective development strategies for group cooperative learning method in college badminton teaching in order to improve the effectiveness of college badminton teaching.

Design of N-11-Azaartemisinins Potentially Active against Plasmodium falciparum by Combined Molecular Electrostatic Potential, Ligand-Receptor Interaction and Models Built with Supervised Machine Learning Methods

N-11-azaartemisinins potentially active against Plasmodium falciparum are designed by combining molecular electrostatic potential (MEP), ligand-receptor interaction, and models built with supervised machine learning methods (PCA, HCA, KNN, SIMCA, and SDA). The optimization of molecular structures was performed using the B3LYP/6-31G* approach. MEP maps and ligand-receptor interactions were used to investigate key structural features required for biological activities and likely interactions between N-11-azaartemisinins and heme, respectively. The supervised machine learning methods allowed the separation of the investigated compounds into two classes: cha and cla, with the properties εLUMO+1 (one level above lowest unoccupied molecular orbital energy), d(C6-C5) (distance between C6 and C5 atoms in ligands), and TSA (total surface area) responsible for the classification. The insights extracted from the investigation developed and the chemical intuition enabled the design of sixteen new N-11-azaartemisinins (prediction set), moreover, models built with supervised machine learning methods were applied to this prediction set. The result of this application showed twelve new promising N-11-azaartemisinins for synthesis and biological evaluation.

Implementing explicit pronunciation instruction: The case of a nonnative English-speaking teacher (Ⅰ)

Studies in second language teacher cognition(SLTC)of pronunciation teachers have increased in the last 10 years,due mainly to the fact that the decisions teachers make about explicit instruction are critical for the development of second language(L2)pronunciation in learners.Although recent research has indicated that nonnative-speaking teachers(NNSTs)can be as effective as native-speaking teachers(NSTs)in pronunciation instruction,and that their training needs resemble those of NSTs,the way NNSTs implement L2 pronunciation instruction has not been studied extensively.This is important to understand given the number of NNsTs of English worldwide at present,and because of the potential benefits of nonnative-speaking pronunciation teaching models in general.In this study,I analysed the way an experienced NNST implemented explicit pronunciation instruction in a context of English as a foreign language(EFL)to understand both his actual teaching practices and the rationale behind such practices.Using a framework of knowledge base of language teaching,this study demonstrates how factors such as previous teaching and learning experiences,teaching context,and L2 learner characteristics shaped and guided the techniques the teacher implemented in class.These results are discussed in terms of implications for pronunciation teaching and teacher training purposes.

Development and Psychometric Assessment of the Dyslexia Awareness Instrument in Teachers

Dyslexia is a specific learning disability that is neurobiological in origin.It is characterized by difficulties with accurate and/or fluent word recognition and by poor spelling and decoding abilities.Teachers may not be very sure about the definition of dyslexia and generally struggle to tell the difference between dyslexic learners and slow learners.Developing the DyAwI may provide an important psychometric assessment tool in determining the awareness level of the teacher and being able to make this distinction.A descriptive,explorative design was used in this study.The study consisted of two main phases.In the first phase,in order to develop the instrument,a literature review and a pilot study on 20 primary school teachers were carried out,and in line with expert opinions,the content validity index was calculated.In the second stage,exploratory and confirmatory factor analyses were carried out to identify the construct validity and reliability.The study included 182 primary school teachers for the second stage.The KMO and Bartlett test values,which determine the suitability of DyAwI for factor analysis,were found to be 0.77 and 0.000,respectively.The overall Cronbach’s alpha value of DyAwI was 0.75.As a result of the assessment of its construct validity,the scale consisted of 2 factors and 14 items.The findings of the study show that the tool is reliable and sufficient.The instrument is easy to understand,and this tool can determine the dyslexia awareness levels of teachers.DyAwI could promote teachers’awareness of dyslexia and support the early identification of primary school students with dyslexia.It is believed that,thanks to the data obtained from the instrument,teachers will be able to decide on an educational assessment of a student with reading difficulties more quickly.

Exploring the Reform of"Teachers,Teaching Materials,and Teaching Methods"Based on the M1+XM Certificate

In order to promote the reform of vocational education,the state has issued the National Vocational Education Reform Implementation Plan,in which the reform of"Teachers,Teaching Materials,and Teaching Methods"as well as the"1+X"certificate have been the most prominent topics for discussion.Facing new opportunities for vocational education development,popularizing the blend of"1+x"courses and certificates,strengthening the integration of production and education,as well as enhancing professional soft skills are urgent issues to solved.This article combines the"1+X"certificate of civil engineering professional construction drawings and analyzes the necessity of promoting the"1+X"professional skill level as well as the combination of certificate and curriculum construction in deepening the reform of"Teachers,Teaching Materials,and Teaching Methods."Several suggestions have been put forward for the reform of"Teachers,Teaching Materials,and Teaching Methods"which would be helpful for the practical exploration in the reform of"three teaching methods."

Sora变革教育:基于教师角色与学习方式的洞见

生成式人工智能的发展又迎来重大飞跃,具有“通用物理世界模拟器”潜力的文生视频模型——Sora被推出。技术的革新势必对教育领域产生重大影响,Sora对教师角色的影响如何?又将为学习方式带来哪些变革?回答好这两个问题是推动Sora变革与赋能教育领域的关键。基于此,本研究在对Sora进行本质解析的基础上,分析Sora对教师角色产生的影响,包括Sora对教师角色挑战的冲击、Sora变革下教师何为的应对以及Sora与教师协同发展的共进;接着提出Sora对学习方式的变革,包括进行“可想即可见”的学习、虚实融合学习的升级、人机协同学习的革新、促进创造性学习实现以及丰富个性化学习形态等;最后对Sora变革教育进行审思与展望,包括Sora的教育边界确立、Sora的使用标准建立、Sora应用的算力需求以及开发教育专属Sora模型。研究以期促进Sora有效应用于教育领域,提升学习效果与质量。

An Analysis of Development as A Reflective Teacher in Higher Education

Effective teaching is every teacher's teaching goal.How to reach the aim is to reflect on the teaching.The following document describes the development on how to reflect the teaching.The main purpose of this analysis is to show how effective and important the reflection on the teaching and to show how useful the reflection which can make the teacher change the way of teaching,encouraging the students to adopt a deep approach in their learning.

基于残差U-Net和自注意力Transformer编码器的磁场预测方法

利用有限元方法对几何结构复杂的电机和变压器进行磁场分析,存在仿真时间长且无法复用的问题。因此,该文提出一种基于残差U-Net和自注意力Transformer编码器的磁场预测方法。首先建立永磁同步电机(PMSM)和非晶合金变压器(AMT)有限元模型,得到深度学习训练所需的数据集;然后将Transformer模块与U-Net模型结合,并引入短残差机制建立ResUnet-Transformer模型,通过预测图像的像素实现磁场预测;最后通过Targeted Dropout算法和动态学习率调整策略对模型进行优化,解决拟合问题并提高预测精度。计算实例证明,ResUnet-Transformer模型在PMSM和AMT数据集上测试集的平均绝对百分比误差(MAPE)均小于1%,且仅需500组样本。该文提出的磁场预测方法能减少实际工况和多工况下精细模拟和拓扑优化的时间和资源消耗,亦是虚拟传感器乃至数字孪生的关键实现方法之一。

ChatGPT热潮下大学生学习方式的转变及引导

ChatGPT在高等学校教育领域中的应用促进了大学生学习方式的重大转变,优化了学习环境,拓宽了学习视野,创新了学习方法,重塑了学习关系,但也面临着在人工智能依赖状态下大学生主体性异化、知识获取异变、师生交往疏远、学习体验减退的问题与风险。高等学校教育工作者需要从以下4个方面对大学生及其学习活动加以教育引导:坚守育人本位,树立正确的学习观;转变学习理念,走向知识创造;革新学习范式,指向深度学习;深化学习体验,转向生命交流。

基于Delphi-AHP法的高职专业课教师课程思政能力评价指标体系建构

专业课教师的课程思政能力是高职院校全面推进课程思政工作的关键。针对高职专业课教师课程思政能力结构尚不明晰,评价体系粗放等问题,采用Delphi-AHP法构建高职专业课教师课程思政能力评价指标体系。通过文献三级编码、专家咨询和研究小组讨论,拟定高职专业课教师课程思政能力评价指标体系草案,然后经过两轮德尔菲(Delphi)专家咨询,确定了包含5个一级指标、17个二级指标和58个三级指标的评价指标体系,再采用层次分析法(AHP),构建各级指标判断矩阵,计算各指标权重并检验判断矩阵的一致性,确定各指标的合理权重。研究结果在理论上为探究高职专业课教师课程思政能力内涵拓展了新的认知视角,在实践上为高职院校教师课程思政能力测评提供了可操作的依据和方向。