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.

TikTok Shop在东南亚五国的发展策略研究——基于SWOT-AHP模型的分析

伴随全球电商渗透率持续提升,我国的一批优秀企业纷纷开通跨境电商平台,以更好地助力品牌出海。字节跳动旗下的全球最大的短视频网站之一Tiktok就是其中的典型代表,其在东南亚五国开通了Titok Shop,为我国企业开拓东南亚市场搭建优质的跨境电商平台。首先总结了TikTok Shop在东南亚五国开展业务的现状,采用SWO T-AHP方法对TikTok Shop在东南亚五国的发展进行深入剖析,认为其战略发展方向应以SO战略为主;提出TikTok Shop应完善跨境电商物流体系、深耕本土化运营、完善品牌化建设、加强基础设施建设、培养跨境电商复合型人才等发展策略,以期为我国企业搭建优质的跨境电商平台,助力我国品牌出海提供有价值的参考。

MDNN:Predicting Student Engagement via Gaze Direction and Facial Expression in Collaborative Learning

Prediction of students’engagement in aCollaborative Learning setting is essential to improve the quality of learning.Collaborative learning is a strategy of learning through groups or teams.When cooperative learning behavior occurs,each student in the group should participate in teaching activities.Researchers showed that students who are actively involved in a class gain more.Gaze behavior and facial expression are important nonverbal indicators to reveal engagement in collaborative learning environments.Previous studies require the wearing of sensor devices or eye tracker devices,which have cost barriers and technical interference for daily teaching practice.In this paper,student engagement is automatically analyzed based on computer vision.We tackle the problem of engagement in collaborative learning using a multi-modal deep neural network(MDNN).We combined facial expression and gaze direction as two individual components of MDNN to predict engagement levels in collaborative learning environments.Our multi-modal solution was evaluated in a real collaborative environment.The results show that the model can accurately predict students’performance in the collaborative learning environment.

A Study of Multimodal Intelligent Adaptive Learning System and Its Pattern of Promoting Learners’Online Learning Engagement

As the field of artificial intelligence continues to evolve,so too does the application of multimodal learning analysis and intelligent adaptive learning systems.This trend has the potential to promote the equalization of educational resources,the intellectualization of educational methods,and the modernization of educational reform,among other benefits.This study proposes a construction framework for an intelligent adaptive learning system that is supported by multimodal data.It provides a detailed explanation of the system’s working principles and patterns,which aim to enhance learners’online engagement in behavior,emotion,and cognition.The study seeks to address the issue of intelligent adaptive learning systems diagnosing learners’learning behavior based solely on learning achievement,to improve learners’online engagement,enable them to master more required knowledge,and ultimately achieve better learning outcomes.

Analysis of Students’ Preferences and Engagement with Mobile Games: A Study of Game Assets and Colour Impact

Mobile games are gaining in popularity among students, although the factors driving engagement remain poorly understood. This study, conducted among 378 students studying for a Bachelor’s degree in Computer and Digital Sciences at the Université Virtuelle de Côte d’Ivoire, explores the influence of game assets and colours on player engagement. Using an online questionnaire, the study identified students’ gaming preferences and their perception of colours. The results show that the key elements most favoured by students are graphics, sound design, the game system, narrative, interactivity and accessibility. What’s more, colour plays a decisive role in their preferences and commitment to educational mobile games.

A Study of the Effects of Employee Engagement on Job Satisfaction:Mediating Effect of Labor Relations Climate

With the rapid advancement of new technologies and the transformation of the economic model on a global scale,enterprises are facing unprecedented changes in the external environment and more diversified demands from employees.Therefore,it is essential for enterprises to bolster employee engagement by enhancing employees’job satisfaction,building a stable and harmonious internal climate,and adapting to the dynamic,complex external environment.This study analyzes the positive effects of employee engagement on job satisfaction,explores the mediating effect of labor relations climate in the relationship between the two,and proposes corresponding practical countermeasures and suggestions.

Improved Behavioural Engagement of Students through Low-stakes Online Tests and Immediate Dialogic Feedback

The engagement of students is a recognised challenge for teachers.Technology offers some practical student engagement tools,and this paper examines the use of low-stakes online tests and immediate dialogic feedback to improve behavioural engagement.The academic exploration of low-stakes tests and dialogic feedback has been extensive,and they are credible teaching tools.In this study,we explore the learning benefit of their combination.Postgraduate engineering students’self-reported and learning analytics data shows conclusive evidence of improved behavioural engagement.We measured a 500%increase in the Learning Management System(LMS)page views on the days when we ran the low-stakes tests(each worth 2%of the marks for the subject)and engaged in immediate dialogic feedback.To interpret these results,we draw on theories of behavioural engagement,low-stakes tests,and feedback.We conclude that the combination of low-stakes tests and immediate feedback improves student behavioural engagement.

Current Situation and Improvement Strategies of Teaching Engagement by Teachers in Higher Medical Colleges Under the Background of“New Medical Education”

The“New Medical Education”is a new requirement for the development of medical education in the new era.As a key and core element of the construction of the“New Medical Education,”the engagement in teaching work of university teachers is a key issue that determines the level of teaching development of university teachers and affects the quality of talent cultivation in universities.Using the literature review method,this study analyzed 39 relevant literatures retrieved from China National Knowledge Infrastructure(CNKI)from 1997 to 2023.The concept,connotation,current situation,influencing factors,and improvement strategies of teaching engagement by university teachers were reviewed and summarized.The results showed that the issue of teaching engagement by university teachers has not received high attention in the field of higher education,especially in higher medical education,which has not yet been reported.Although the overall number of literature is not large,some of the conclusions have important implications for the next research on the influencing factors and mechanisms of double-qualified teacher engagement in higher medical colleges.

An Elite-Class Teaching-Learning-Based Optimization for Reentrant Hybrid Flow Shop Scheduling with Bottleneck Stage

Bottleneck stage and reentrance often exist in real-life manufacturing processes;however,the previous research rarely addresses these two processing conditions in a scheduling problem.In this study,a reentrant hybrid flow shop scheduling problem(RHFSP)with a bottleneck stage is considered,and an elite-class teaching-learning-based optimization(ETLBO)algorithm is proposed to minimize maximum completion time.To produce high-quality solutions,teachers are divided into formal ones and substitute ones,and multiple classes are formed.The teacher phase is composed of teacher competition and teacher teaching.The learner phase is replaced with a reinforcement search of the elite class.Adaptive adjustment on teachers and classes is established based on class quality,which is determined by the number of elite solutions in class.Numerous experimental results demonstrate the effectiveness of new strategies,and ETLBO has a significant advantage in solving the considered RHFSP.

Correlation between psychological resilience and burnout among female employees in a shopping mall in Xi Xian new area,China:A cross-sectional survey-Retraction

In the article titled“Correlation between psychological resilience and burnout among female employees in a shopping mall in Xi Xian New Area,China:A cross-sectional survey”by Zhang Q and Liu L(J Integr Nurs 2021;3(3):117-121.doi:10.4103/jin.jin_14_21),[1]the content and results data of this article was questioned by International database(Web of Science)institution.This article was then investigated by the publisher and Journal of Integrative Nursing(JIN).

Strengthened Dominance Relation NSGA-Ⅲ Algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem

The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives.The Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling problem.Nevertheless,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and diversity.To enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence issues.Furthermore,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex computation.To validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test instances.The experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem.

Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

An Improved Harris Hawk Optimization Algorithm for Flexible Job Shop Scheduling Problem

Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production management.The Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been widely employed to solve scheduling problems.However,HHO suffers from premature convergence when solving NP-hard problems.Therefore,this paper proposes an improved HHO algorithm(GNHHO)to solve the FJSP.GNHHO introduces an elitism strategy,a chaotic mechanism,a nonlinear escaping energy update strategy,and a Gaussian random walk strategy to prevent premature convergence.A flexible job shop scheduling model is constructed,and the static and dynamic FJSP is investigated to minimize the makespan.This paper chooses a two-segment encoding mode based on the job and the machine of the FJSP.To verify the effectiveness of GNHHO,this study tests it in 23 benchmark functions,10 standard job shop scheduling problems(JSPs),and 5 standard FJSPs.Besides,this study collects data from an agricultural company and uses the GNHHO algorithm to optimize the company’s FJSP.The optimized scheduling scheme demonstrates significant improvements in makespan,with an advancement of 28.16%for static scheduling and 35.63%for dynamic scheduling.Moreover,it achieves an average increase of 21.50%in the on-time order delivery rate.The results demonstrate that the performance of the GNHHO algorithm in solving FJSP is superior to some existing algorithms.

Deep Reinforcement Learning Solves Job-shop Scheduling Problems

To solve the sparse reward problem of job-shop scheduling by deep reinforcement learning,a deep reinforcement learning framework considering sparse reward problem is proposed.The job shop scheduling problem is transformed into Markov decision process,and six state features are designed to improve the state feature representation by using two-way scheduling method,including four state features that distinguish the optimal action and two state features that are related to the learning goal.An extended variant of graph isomorphic network GIN++is used to encode disjunction graphs to improve the performance and generalization ability of the model.Through iterative greedy algorithm,random strategy is generated as the initial strategy,and the action with the maximum information gain is selected to expand it to optimize the exploration ability of Actor-Critic algorithm.Through validation of the trained policy model on multiple public test data sets and comparison with other advanced DRL methods and scheduling rules,the proposed method reduces the minimum average gap by 3.49%,5.31%and 4.16%,respectively,compared with the priority rule-based method,and 5.34%compared with the learning-based method.11.97%and 5.02%,effectively improving the accuracy of DRL to solve the approximate solution of JSSP minimum completion time.

On Fashion’s Political Engagement in Modern Society

In class society,fashion is identified as a sign of class position and social status,while in modern democratic society fashion’s political connotation and significance have greatly changed and is mainly displayed from the following three aspects:1)as a way of social protest from vulnerable social groups by being symbol of being an independent and free subject.However,the resistance or protest represented by fashion is no more than something ritual.2)as a way of identity and subjectivity construction,both individual and national;3)as embodiment of fashion leadership and a way of incorporation.Generally to say,fashion has played an important role in every aspect above.However,there is still a tension between them and we must manage carefully to keep a balance.

Parametric Analysis of the End Face Engagement Worm Gear

A novel specific type of worm drive, so-called end face engagement worm gear（EFEWD）, is originally presented to minimize or overcome the gear backlash. Different factors, including the three different types, contact curves, tooth profile, lubrication angle and the induced normal curvature are taken into account to investigate the meshing characteristics and create the profile of a novel specific type of worm drive through mathematical models and theoretical analysis. The tooth of the worm wheel is very specific with the sine-shaped tooth which is located at the alveolus of the worm and the tooth profile of a worm is generated by the meshing movement of the worm wheel with the sine-shaped tooth, but just the end face of the worm（with three different typical meshing types） is adapted to meshing, and therefore an extraordinary manufacturing methods is used to generate the profile of the end face engagement worm. The research results indicates that the bearing contacts of the generated conjugate hourglass worm gear set are in line contacts, with certain advantages of no-backlash, high precision and high operating efficiency over other gears and gear systems besides the end face engagement worm gear drive may improve bearing contact, reduce the level of transmission errors and lessen the sensitivity to errors of alignment. Also, the end face engagement worm can be easily made with superior meshing and lubrication performance compared with the conventional techniques. In particular, the meshing and lubrication performance of the end face engagement worm gear by using the end face to meshing can be increased over 10% and 7%, respectively. This investigate is expect to provide a new insight on the design of the future no-backlash worm drive for industry.

Optimal Design for an End Face Engagement Worm Gear with Multiple Worm-wheel Meshing

To solve the problem for lacking a special mechanical transmission that could provide multiple outputs with high transmission efficiency and good lubrication in the modem industrial, a novel worm gear, named end face engagement worm gear, with multiple worm-wheel meshing is proposed for the first time. The essential parameters for the worm gear are optimized to enhance lubrication and meshing properties. Moreover, analysis of variance（ANOVA） is applied to determine the optimum levels and to determine the influence of parameters. The ANOVA results show that the novel end face engagement worm gear with multiple worm wheels provides high lubrication（the lubrication angle is more than 89~） and meshing performance（the induce normal curvature is less than 0.0002 mm ＇）. The interaction between center distance and roller slant distance most strongly influences the lubrication angle（contributed 51.6%）, followed by the parameters of center distance（contributed 25.0%）, roller slant distance（contributed 16.4%）, tooth angle of gear, gear ratio, and roller radius. In addition, roller radius most strongly influences the induced normal curvature（contributed 39.4%）, followed by roller slant distance（contributed 15.2%）, tooth angle of the gear（contributed 9.0%）, center distance, and gear ratio. The proposed worm gear helps to enrich the no-backlash high precision worm drive and the optimal design method can provide a useful reference on performance improvement of other worm gear.

顾客忠诚Customer Engagement:定义、测量与研究展望

本文研究了CE提出的背景、定义和量表,并分析了CE与顾客忠诚、消费者涉入(Involvement)及顾客参与(Participation)等相似概念的关系。

Formation and adjustment of manned/unmanned combat aerial vehicle cooperative engagement system

Manned combat aerial vehicles （MCAVs）, and un-manned combat aerial vehicles （UCAVs） together form a cooper-ative engagement system to carry out operational mission, whichwill be a new air engagement style in the near future. On the basisof analyzing the structure of the MCAV/UCAV cooperative engage-ment system, this paper divides the unique system into three hi-erarchical levels, respectively, i.e., mission level, task-cluster leveland task level. To solve the formation and adjustment problem ofthe latter two levels, three corresponding mathematical modelsare established. To solve these models, three algorithms calledquantum artificial bee colony （QABC） algorithm, greedy strategy（GS） and two-stage greedy strategy （TSGS） are proposed. Finally,a series of simulation experiments are designed to verify the effec-tiveness and superiority of the proposed algorithms.

The mediating role of organizational commitment between calling and work engagement of nurses: A cross-sectional study

Objective: Nurse shortage is a critical problem for global healthcare services.It impacts the quality of clinical care.Work engagement is the core competence of hospitals,which indicates employee's positive attitude toward organization and work.This study aimed to explore the relationships among calling,organizational commitment,and work engagement.Methods: A cross-sectional study was designed,and 320 nurses from tertiary hospitals in China completed the questionnaires that included demographic information,calling scale,employee engagement scale,and organizational commitment scale.Pearson correlation was performed to test the correlations among calling,organizational commitment,and work engagement.Stepwise regression analyses were performed to explore the mediating role of organizational commitment.The bootstrap method was employed to confirm the mediating effect.Results: Nurses' work engagement score was at the medium degree,whereas calling and organizational commitment were in the medium to high level.The results revealed that calling,organizational commitment,work engagement,and each dimension were positively correlated with one another (r=0.145-0.922,P < 0.01).The organizational commitment plays a partially mediating effect between calling and work engagement (β =0.603 to 0.333,P < 0.01).Conclusions: The mediation effect of organizational commitment was verified,which provided a comprehensive understanding of how calling impacted work engagement.Moreover,administrators should not only promote interventions to increase work engagement but also pay attention to calling and organizational commitment so as to improve their work engagement.Taken together,increased level of work engagement is required in the current nursing field.