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.

UAV Frequency-based Crowdsensing Using Grouping Multi-agent Deep Reinforcement Learning

Mobile CrowdSensing(MCS)is a promising sensing paradigm that recruits users to cooperatively perform sensing tasks.Recently,unmanned aerial vehicles(UAVs)as the powerful sensing devices are used to replace user participation and carry out some special tasks,such as epidemic monitoring and earthquakes rescue.In this paper,we focus on scheduling UAVs to sense the task Point-of-Interests(PoIs)with different frequency coverage requirements.To accomplish the sensing task,the scheduling strategy needs to consider the coverage requirement,geographic fairness and energy charging simultaneously.We consider the complex interaction among UAVs and propose a grouping multi-agent deep reinforcement learning approach(G-MADDPG)to schedule UAVs distributively.G-MADDPG groups all UAVs into some teams by a distance-based clustering algorithm(DCA),then it regards each team as an agent.In this way,G-MADDPG solves the problem that the training time of traditional MADDPG is too long to converge when the number of UAVs is large,and the trade-off between training time and result accuracy could be controlled flexibly by adjusting the number of teams.Extensive simulation results show that our scheduling strategy has better performance compared with three baselines and is flexible in balancing training time and result accuracy.

Undergraduate Medical Students＇ Perception about Learning in Small Group at University of Sharjah

Learning in small groups is a key instructional strategy in medicine and more so in the problem based curriculum （PBL）. It is perceived that working in small groups enhances acquisition, processing and retention of the ever increasing medical knowledge. Learning in small group will help the students to be better learner and improve their personal, social and cognitive skills. The objective of this study is to describe undergraduate medical students＇ perception toward small group learning in a PBL curriculum. A cross-sectional descriptive study was conducted among the undergraduate medical students in the phase 2 of their MBBS program at University of Sharjah. A total of 277 undergraduate medical students participated in the study. The mean age of the study population was 20 years and 61% were female students. The most rewarding experiences as perceived by medical students were exposure to different views （71%）, making friends （57%） improving their grades （52%） and underwent personal development （46%）. The main disadvantages of small group learning were waste of time （55%）, side talks （16%）, and other distractions （14%）. Majority of students had a positive perception towards small group learning and agreed that it enhances their collaborative learning and team work skills. Small group learning was perceived as a key instructional method in the PBL curriculum [3] and it enhances their grades, learning outcomes, personal development and critical thinking abilities [4].

Geometry Algorisms of Dynkin Diagrams in Lie Group Machine Learning

This paper uses the geometric method to describe Lie group machine learning(LML)based on the theoretical framework of LML,which gives the geometric algorithms of Dynkin diagrams in LML.It includes the basic conceptions of Dynkin diagrams in LML,the classification theorems of Dynkin diagrams in LML,the classification algorithm of Dynkin diagrams in LML and the verification of the classification algorithm with experimental results.

Experimental Study of The Group Cooperative Learning in Oral English Class of English Majors

College students have better English foundations for them to develop English speaking abilities.However, most of them are not good at it compared with reading and listening.Therefore, teachers need to find out some methods to develop students' ability of Group Cooperative Learning to know their errors in time and make sure that they can benefit from it so that students have the enthusiasm to improve their oral English.The thesis is mainly about all empirical study of Group Cooperative Leaning in college English oral course.The major experiment was done with some English majors at North University of China.Though many subjects supported Cooperative Learning and believed that it did effectively reduce some anxiety in students' English oral learning and enhanced their interests, the improvement of their English oral ability is far from satisfaction.This thesis employs questionnaire survey and contrastive analysis methods.This research paper means to investigate the Group Cooperative Learning of oral English learning for college students.The thesis includes five parts.The first part illustrates the purpose, significance of the research and the definition of Group Cooperative Learning. The second part shows the research background from abroad and home, and the factors of poor oral English.The third part presents the research design of the author.The fourth part states the results and discussions about the research.The conclusion part summarizes the major findings of research and also explains the limitations of the author's research and further study about Group Cooperative Learning in oral class for English Majors.

Survey on Lie Group Machine Learning

Lie group machine learning is recognized as the theoretical basis of brain intelligence,brain learning,higher machine learning,and higher artificial intelligence.Sample sets of Lie group matrices are widely available in practical applications.Lie group learning is a vibrant field of increasing importance and extraordinary potential and thus needs to be developed further.This study aims to provide a comprehensive survey on recent advances in Lie group machine learning.We introduce Lie group machine learning techniques in three major categories:supervised Lie group machine learning,semisupervised Lie group machine learning,and unsupervised Lie group machine learning.In addition,we introduce the special application of Lie group machine learning in image processing.This work covers the following techniques:Lie group machine learning model,Lie group subspace orbit generation learning,symplectic group learning,quantum group learning,Lie group fiber bundle learning,Lie group cover learning,Lie group deep structure learning,Lie group semisupervised learning,Lie group kernel learning,tensor learning,frame bundle connection learning,spectral estimation learning,Finsler geometric learning,homology boundary learning,category representation learning,and neuromorphic synergy learning.Overall,this survey aims to provide an insightful overview of state-of-the-art development in the field of Lie group machine learning.It will enable researchers to comprehensively understand the state of the field,identify the most appropriate tools for particular applications,and identify directions for future research.

FLIGHT:Federated Learning with IRS for Grouped Heterogeneous Training

In recent years,federated learning(FL)has played an important role in private data-sensitive scenarios to perform learning tasks collectively without data exchange.However,due to the centralized model aggregation for heterogeneous devices in FL,the last updated model after local training delays the convergence,which increases the economic cost and dampens clients’motivations for participating in FL.In addition,with the rapid development and application of intelligent reflecting surface(IRS)in the next-generation wireless communication,IRS has proven to be one effective way to enhance the communication quality.In this paper,we propose a framework of federated learning with IRS for grouped heterogeneous training(FLIGHT)to reduce the latency caused by the heterogeneous communication and computation of the clients.Specifically,we formulate a cost function and a greedy-based grouping strategy,which divides the clients into several groups to accelerate the convergence of the FL model.The simulation results verify the effectiveness of FLIGHT for accelerating the convergence of FL with heterogeneous clients.Besides the exemplified linear regression(LR)model and convolutional neural network(CNN),FLIGHT is also applicable to other learning models.

MONITORING OF METACOGNITIVE STRATEGY TRAINING

The goal of education is to cultivate learner autonomy. Studies in metacognition suggest that the ideal way to accomplish this is through metacognitive strategy training. Research also indicates that effective training programs depend on monitoring. To cultivate self-regulation or learner autonomy, it is necessary to exert external monitoring at the beginning, and gradually reduce external supervision as the students become more autonomous. The present study argues for cooperative group learning and regular evaluation as the main monitoring strategies. A semester long study indicates that cooperative group learning, besides being valuable in cultivating social relationship, and fostering cooperation and learner autonomy, can also serve as a good monitoring strategy in metacognitive strategy training. Regular evaluation, when it is progress- oriented and learner-centered, and conducted at proper times, will also provide valuable information for students to check, evaluate and adjust their learning. Self-questioning sheets and learner portfolios are also useful techniques in monitoring students' learning.

Fresh thing for freshman: Exploring a blended method on teaching the history of geographic thought

It is necessary for undergraduates majoring in geography to learn the history of geographic thought. Although there are different cultural and educational backgrounds between China and the West, teaching methods such as text teaching, students＇ presentations and group learning are suitable for most of teachers and students even from different countries and regions. The blended method is helpful to popularize history of geographic thought and improve the level of teaching and learning. Owing to lack of the class on the history of geographic thought in countries like China, the authors try to explore a blended method for the first-year geography undergraduates and to assess the effects of this teaching based on some questionnaires. The students have different benefits and responses to this class. A special group consisting of one teacher and several undergraduates does the research and coauthors the paper through making questionnaire, interviewing and analyzing materials from 67 freshmen majoring in human geography and geography science（teacher-training） in China. For the undergraduates especially from the countries like China, it is well worth making the history of geographic thought become a necessary and interesting class.