PBL(Project-Based Learning)理念融入高职劳动教育的价值意蕴与实践路径

劳动是人维持自我生存和自我发展的唯一手段,劳动教育是推进立德树人根本任务及提升人才培养质量的重要载体。然而教材体系不够成熟、课程体系不够健全、评价体系不够完善是当前高职劳动教育存在的突出问题。将PBL(Pro-ject-Based Learning)项目式教学理念融入劳动教育,契合高职人才培养目标,有助于提高师生参与的积极性,促进劳动教育效果提升。对此,在推进PBL教学理念融入劳动教育教学过程中,加强顶层设计明确目标,聚焦职教特色融通资源,项目驱动理实教育一体化,量身定制项目化测评体系,切实培养好学生的劳动意识、劳动思维、劳动能力。

Project-based Learning in Calculus on The Use of Maple Software Technology

In this article, authors describe how to use the project-based learning （PBL） pedagogy to enhance students＇ Calculus learning based on the first author＇s experimental teaching experience. The ＂2014 BMCC Polar Art Calendar＂ project was completed by Calculus students at Borough of Manhattan Community College （BMCC） during the fall 2013 semester. Students were requested to apply graphs of polar equations to create computer-generated images with a variety of flower patterns by using the Maple technology in a math lab. At the end of this project, students were requested to submit and present their written reports to express their mathematical thinking. Authors also explain in details how to create projects compatible with textbook knowledge learning objectives, how to prepare scaffolding materials for students to use, how to utilize a math lab and to work with lab technicians in Maple Software, and how to design a rubric for project evaluations. Students＇ artwork created in the Polar Art Calendar are presented. Students＇ positive outcomes have proven a success of this project design as well as its execution as an example of PBL. Benefits to students and challenges to teachers on the use of PBL approach have been discussed at the end of this article.

To Cultivate Outstanding Software Engineer Based on Project-based Learning

This thesis sketches the connotation of project-based learning and introduces the basis on which project-based learning is practiced and applied in school of software as well as the plan of further practicing project-based learning.At the same time,this thesis also discusses application of project-based learning in "education and cultivation plan of excellent engineer".

Promotion of Project-Based Learning Aimed at Professional Skills

Project-based learning theory is applied in Software English course. The aim is to develop students＇ professional skill. A professional skill scale of software engineer is introduced with Software English communicative skill （reading skill, writing skill, listening skill, and communicative skill）, engineering thinking habit （skill of design the plan and problem-solving skill）, engineering thinking skill, knowledge of software engineering, and Emotion Quotient to deal with problems （Team spirit and communicative skill and self-assessment skill）. For software majors, students learn about five stages and job responsibilities in software making process.

Using Project-Based Learning （PBL） in Management Accounting Course： Lesson and Reflection From Entrepreneurship Education

One of the main issues in entrepreneurship education is integrating core course to entrepreneurship course. Many educators fail to integrate core course and entrepreneurial education. The purpose of this study is to investigate whether the integration of management accounting course in entrepreneurship course will accomplish the objective of entrepreneurship education. This study is derived from the experience of entrepreneurial project course at Management Department of Ciputra University, Surabaya. This study uses an explorative approach to investigate the implementation of project-based learning （PBL） in management accounting course. The result shows variation of output. There are about 91% business can be implemented by business groups and about 33% target achieved. Several management accounting techniques are used in this study, such as balanced score card （BSC）, activity-based costing （ABC）, activity-based management （ABM）, cost-volume-profit （CVP） analysis, and tactical decision making. This study found that by using management accounting techniques, student can run the business well especially when they have to make decision. This study strengthens the role of management accounting in business practices, and furthermore, this study proves that management accounting is beneficial for entrepreneurs who are still in the start-up business stage.

Project-based Learning （PBL） for Enhancing Chinese College Students＇ English Performance

Project-based learning （PBL） as an instructional approach focuses on student-centeredness through contextualizing learning by presenting students with real world issues and practices that aim at achieving enduring learning effects. As a challenge to traditional lecture-based instruction, PBL may generate positive backwash assessment effects via documenting the process of developing authentic language skills. These skills-oriented outcomes are achieved through proper curricula in appropriate learning situations via various dimensions. However, problems may occur such as management, pressure from administration, evaluation doing their selected projects. teacher/student attitude, class size and proper group process and criteria, and students＇ likely frustrations in

Alternative Assessment in an EFL Project-based Learning Context: Perceptions and Correlations

This study aims to explore Chinese university EFL learners'perceptions toward alternative assessment in a context of a project-based learning digital storytelling presentation in Speaking Course.It also seeks to compare the relationship between alternative assessment and teacher assessment.The findings showed that a strong correlation between alternative assessment and teacher assessment occurred.Alternative assessment activities are viewed by students as"authentic"assessments,as they mimic how the student will be using their knowledge in the future.Alternative assessment as a form of formative assessment can be a powerful day-to-day tool for teachers and students.Alternative assessment is an enabler of process of learning.The study suggests that alternative assessment can encourage learners to become more fully responsible for their learning and can result in more and better learning.Alternative assessment can thus be used as a golden key to the"deaf and dumb"phenomenon for Chinese university EFL learners.

PBL(Project-based Learning)教学模式在高职英语自主学习课程中的应用研究

本文采用行动研究的方法,以武汉职业技术学院的英语听说过级自主学习课程为例,探讨如何在自主学习课程中应用PBL的教学模式改进自主学习课程的考核方式,提高学生的自主学习能力和意识。文章首先分析了自主学习课程在实施过程中遇到的问题,详细描述了PBL教学模式的总体设计构想和具体的实施步骤,并对实施的结果进行了讨论。经过统计和问卷调查结果发现,应用PBL模式后,实验班的学生的学习兴趣有了很大的提高,有利于培养他们的自主学习能力和合作精神。同时,试验前后的听力测试表明,实验班听力成绩较对照班也有了显著的提高。可见,PBL模式的应用能在一定程度上改进目前在自主学习课程中所遇到的一些问题。

基于PBL(Project-based Learning)的商务英语混合式教学模式研究

商务英语课程是融英语语言知识、商务知识及商务实践为一体的课程,其知识性和实践性并重的特点决定了传统的以教师讲授为主的教学模式无法满足培养社会需要的商务英语人才的需求,教学模式亟待改革。项目学习法是学生在实践中构建知识的一种教学模式,而线上线下资源有机融合的混合式教学模式能够拓展课堂容量,使学习不受时空地域的限制,二者相结合是商务英语教学改革的重要方向,教学评价体系的改革是确保新教学模式有效实施的重要保证。

Development of Fundamental Competencies for Working Persons Through Project-Based Learning

Universities teach mainly specialized subjects and the liberal arts. Society expects university students to gain certain basic skills important when working for a company. These skills can be divided broadly into action, thinking, and teamwork. The purpose of this paper is to propose a method of project-based education for developing fundamental competencies for working persons. Many studies have been reported on educational methods with project management techniques, but few have considered project-based education aiming at improving fundamental competencies for working persons. If these competencies can be developed through project-based education, it will be possible to develop not only teamwork skills, but also a wide range of skills involving action as well as thinking. The traditional Japanese university curriculum comprises specialized subjects and the liberal arts. The author proposes the addition of project-based education to develop basic skills needed in the workforce. This research proposes an education model for basic competency training and examines the educational outcomes by studying results of a cooking tool project assigned to university students. The model includes Teoriya Resheniya Izobretatelskikh Zadatch （TRIZ, a Russian acronym for the theory of inventive problem solving）, a World Cafe, and the SECI process （a process of knowledge creation comprised of socialization, externalization, combination, and internalization in knowledge management） in the hope that this model will be conducive to implementing effective project-based learning. This research concludes that it is possible to develop the basic skills needed by university students in society through project-based learning under a basic skills education model.

The Characteristics of Preschool Children’s Collaborative Problem Solving:A Discourse Analysis in Project-Based Learning

Considered a crucial skill in the 21st century,collaborative problem solving(CPS)has been an essential development task for preschool children.This study analyzes preschool children’s discourse in the project-based learning(PBL)process and presents the following findings.Firstly,in the collaborative dimension,the frequency of children’s discourse on establishing and maintaining shared understanding(U)and taking appropriate action to solve the problem(A)is relatively high,while that on establishing and maintaining team organization(O)is relatively low.Secondly,in the problem solving dimension,the frequency of children’s discourse on planning and executing(P&E)is the highest,while that on monitoring and reflecting(M&R)is the lowest.Thirdly,in terms of turn taking patterns,self-selection accounts for a significantly higher proportion than allocation and continuation.Overall,preschool children’s CPS is characterized by loose collaboration and multilinear problem solving.They are usually keener to strive for opportunities to express their views but lack attention to others’speeches.At the same time,they can constantly come up with new problem solving plans and actions but rarely reflect on their feasibility and actual effects.In addition to children’s collaborative role,teachers’intervention can also impact the CPS processes.Therefore,teachers are recommended to provide children with opportunities for CPS and strengthen monitoring,guidance,and support in children’s CPS processes to facilitate better child engagement in CPS.

基于改进Q-Learning的移动机器人路径规划算法

随着移动机器人在生产生活中的深入应用,其路径规划能力也需要向快速性和环境适应性兼备发展。为解决现有移动机器人使用强化学习方法进行路径规划时存在的探索前期容易陷入局部最优、反复搜索同一区域,探索后期收敛率低、收敛速度慢的问题,本研究提出一种改进的Q-Learning算法。该算法改进Q矩阵赋值方法,使迭代前期探索过程具有指向性,并降低碰撞的情况;改进Q矩阵迭代方法,使Q矩阵更新具有前瞻性,避免在一个小区域中反复探索;改进随机探索策略,在迭代前期全面利用环境信息,后期向目标点靠近。在不同栅格地图仿真验证结果表明,本文算法在Q-Learning算法的基础上,通过上述改进降低探索过程中的路径长度、减少抖动并提高收敛的速度,具有更高的计算效率。

基于Q-Learning的航空器滑行路径规划研究

针对传统算法规划航空器滑行路径准确度低、不能根据整体场面运行情况进行路径规划的问题,提出一种基于Q-Learning的路径规划方法。通过对机场飞行区网络结构模型和强化学习的仿真环境分析,设置了状态空间和动作空间,并根据路径的合规性和合理性设定了奖励函数,将路径合理性评价值设置为滑行路径长度与飞行区平均滑行时间乘积的倒数。最后,分析了动作选择策略参数对路径规划模型的影响。结果表明,与A*算法和Floyd算法相比,基于Q-Learning的路径规划在滑行距离最短的同时,避开了相对繁忙的区域,路径合理性评价值高。

Machine learning applications in stroke medicine:advancements,challenges,and future prospectives

Stroke is a leading cause of disability and mortality worldwide,necessitating the development of advanced technologies to improve its diagnosis,treatment,and patient outcomes.In recent years,machine learning techniques have emerged as promising tools in stroke medicine,enabling efficient analysis of large-scale datasets and facilitating personalized and precision medicine approaches.This abstract provides a comprehensive overview of machine learning’s applications,challenges,and future directions in stroke medicine.Recently introduced machine learning algorithms have been extensively employed in all the fields of stroke medicine.Machine learning models have demonstrated remarkable accuracy in imaging analysis,diagnosing stroke subtypes,risk stratifications,guiding medical treatment,and predicting patient prognosis.Despite the tremendous potential of machine learning in stroke medicine,several challenges must be addressed.These include the need for standardized and interoperable data collection,robust model validation and generalization,and the ethical considerations surrounding privacy and bias.In addition,integrating machine learning models into clinical workflows and establishing regulatory frameworks are critical for ensuring their widespread adoption and impact in routine stroke care.Machine learning promises to revolutionize stroke medicine by enabling precise diagnosis,tailored treatment selection,and improved prognostication.Continued research and collaboration among clinicians,researchers,and technologists are essential for overcoming challenges and realizing the full potential of machine learning in stroke care,ultimately leading to enhanced patient outcomes and quality of life.This review aims to summarize all the current implications of machine learning in stroke diagnosis,treatment,and prognostic evaluation.At the same time,another purpose of this paper is to explore all the future perspectives these techniques can provide in combating this disabling disease.

改进Q-Learning的路径规划算法研究

针对Q-Learning算法学习效率低、收敛速度慢且在动态障碍物的环境下路径规划效果不佳的问题,本文提出一种改进Q-Learning的移动机器人路径规划算法.针对该问题,算法根据概率的突变性引入探索因子来平衡探索和利用以加快学习效率;通过在更新函数中设计深度学习因子以保证算法探索概率;融合遗传算法,避免陷入局部路径最优同时按阶段探索最优迭代步长次数,以减少动态地图探索重复率;最后提取输出的最优路径关键节点采用贝塞尔曲线进行平滑处理,进一步保证路径平滑度和可行性.实验通过栅格法构建地图,对比实验结果表明,改进后的算法效率相较于传统算法在迭代次数和路径上均有较大优化,且能够较好的实现动态地图下的路径规划,进一步验证所提方法的有效性和实用性.

Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning

BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective preventive measures.AIM To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment.METHODS Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission.Relevant data from the initial 14 d of ICU stay,such as age,comorbidities,sedative dosage,vasopressor dosage,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,were gathered.The relationships between these variables and ICU-AW were examined.Utilizing iterative machine learning techniques,a multilayer perceptron neural network model was developed,and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve.RESULTS Within the ICU-AW group,age,duration of mechanical ventilation,lorazepam dosage,adrenaline dosage,and length of ICU stay were significantly higher than in the non-ICU-AW group.Additionally,sepsis,multiple organ dysfunction syndrome,hypoalbuminemia,acute heart failure,respiratory failure,acute kidney injury,anemia,stress-related gastrointestinal bleeding,shock,hypertension,coronary artery disease,malignant tumors,and rehabilitation therapy ratios were significantly higher in the ICU-AW group,demonstrating statistical significance.The most influential factors contributing to ICU-AW were identified as the length of ICU stay(100.0%)and the duration of mechanical ventilation(54.9%).The neural network model predicted ICU-AW with an area under the curve of 0.941,sensitivity of 92.2%,and specificity of 82.7%.CONCLUSION The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation.A primary preventive strategy,when feasible,involves minimizing both ICU stay and mechanical ventilation duration.

A credibility-aware swarm-federated deep learning framework in internet of vehicles

Although Federated Deep Learning(FDL)enables distributed machine learning in the Internet of Vehicles(IoV),it requires multiple clients to upload model parameters,thus still existing unavoidable communication overhead and data privacy risks.The recently proposed Swarm Learning(SL)provides a decentralized machine learning approach for unit edge computing and blockchain-based coordination.A Swarm-Federated Deep Learning framework in the IoV system(IoV-SFDL)that integrates SL into the FDL framework is proposed in this paper.The IoV-SFDL organizes vehicles to generate local SL models with adjacent vehicles based on the blockchain empowered SL,then aggregates the global FDL model among different SL groups with a credibility weights prediction algorithm.Extensive experimental results show that compared with the baseline frameworks,the proposed IoV-SFDL framework reduces the overhead of client-to-server communication by 16.72%,while the model performance improves by about 5.02%for the same training iterations.

Assessments of Data-Driven Deep Learning Models on One-Month Predictions of Pan-Arctic Sea Ice Thickness

In recent years,deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration,but relatively little research has been conducted for larger spatial and temporal scales,mainly due to the limited time coverage of observations and reanalysis data.Meanwhile,deep learning predictions of sea ice thickness(SIT)have yet to receive ample attention.In this study,two data-driven deep learning(DL)models are built based on the ConvLSTM and fully convolutional U-net(FC-Unet)algorithms and trained using CMIP6 historical simulations for transfer learning and fine-tuned using reanalysis/observations.These models enable monthly predictions of Arctic SIT without considering the complex physical processes involved.Through comprehensive assessments of prediction skills by season and region,the results suggest that using a broader set of CMIP6 data for transfer learning,as well as incorporating multiple climate variables as predictors,contribute to better prediction results,although both DL models can effectively predict the spatiotemporal features of SIT anomalies.Regarding the predicted SIT anomalies of the FC-Unet model,the spatial correlations with reanalysis reach an average level of 89%over all months,while the temporal anomaly correlation coefficients are close to unity in most cases.The models also demonstrate robust performances in predicting SIT and SIE during extreme events.The effectiveness and reliability of the proposed deep transfer learning models in predicting Arctic SIT can facilitate more accurate pan-Arctic predictions,aiding climate change research and real-time business applications.

Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.