Objective:To explore the application effect of group cooperative learning combined with flipped classroom in standardized training of emergency surgery resident physicians.Methods:95 resident physicians undergoing eme...Objective:To explore the application effect of group cooperative learning combined with flipped classroom in standardized training of emergency surgery resident physicians.Methods:95 resident physicians undergoing emergency surgery standardized training in our hospital were randomly divided into an experimental group(46,group cooperative learning+flipped classroom)and a control group(49,traditional teaching).The training period was 2 months.Results:The graduation assessment scores of the experimental group were higher than those of the control group(P<0.05).The improvement in critical thinking ability and self-learning ability was better than that of the control group(P<0.05).The response rate of“yes”to all items of course teaching satisfaction was significantly higher than that of the control group(P<0.05).The satisfaction scores for training teachers were higher than those of the control group(P<0.05).Conclusion:The teaching model of group cooperative learning combined with flipped classroom,through reconstructing the learning process and activating interactive participation,can significantly improve the clinical operation and theoretical foundation of emergency surgery standardized training students.It also cultivates core professional qualities such as critical thinking,self-learning,and team collaboration,providing a feasible paradigm for integrated medical education in this discipline.展开更多
This study mainly discussed the effects of three tasks of translating authentic business report on L2 vocabulary learning.160 students were chosen from different majors by a pre-task proficiency test.The findings reve...This study mainly discussed the effects of three tasks of translating authentic business report on L2 vocabulary learning.160 students were chosen from different majors by a pre-task proficiency test.The findings revealed that task 3 was the optimum task in vo-cabulary gain and direct vocabulary learning had a more facilitated power than incidental vocabulary learning in this translation task forthe learners with the lowest level of vocabulary.This study also suggested that the caution of need and evaluation needed to be adjustedand paid for the learners with the lowest vocabulary level.展开更多
Group work learning is one of the hot topics in English learning and teaching today. This discourse will probe the meaning and the advantages of group work learning, as well as its implementation. Also, the discourse ...Group work learning is one of the hot topics in English learning and teaching today. This discourse will probe the meaning and the advantages of group work learning, as well as its implementation. Also, the discourse discusses the proper time for group work learning. In addition to that, problems of group work learning are enclosed.展开更多
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 conception...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.展开更多
The joint spatial division and multiplexing(JSDM)is a two-phase precoding scheme for massive multiple-input-multiple-output(MIMO)system under frequency division duplex(FDD)mode to reduce the amount of channel state in...The joint spatial division and multiplexing(JSDM)is a two-phase precoding scheme for massive multiple-input-multiple-output(MIMO)system under frequency division duplex(FDD)mode to reduce the amount of channel state information(CSI)feedback.To apply this scheme,users need to be partitioned into groups so that users in the same group have similar channel covariance eigenvectors while users in different groups have almost orthogonal eigenvectors.In this paper,taking the clustered user model into account,we consider the user grouping of JSDM for the downlink massive MIMO system with uniform planar antenna array(UPA)at base station(BS).A deep learning based user grouping algorithm is proposed to improve the efficiency of the user grouping process.The proposed grouping algorithm transfers the statistical CSI of all users into a picture,and utilizes the deep learning enabled objective detection model you look only once(YOLO)to divide the users into different groups rapidly.Simulation results show that the proposed user grouping scheme can achieve higher sum rate with less time delay.展开更多
In a task-based communicative classroom, group activities are effective ways to devdop students' 4 basic language skills. However, not all group activities can reach the expected results. English teachers should p...In a task-based communicative classroom, group activities are effective ways to devdop students' 4 basic language skills. However, not all group activities can reach the expected results. English teachers should pay attention to some aspects in organizing a classroom group activity.展开更多
Among the various extra-curriculum activities,which are indispensable for English teaching,Extra-curriculum Activity(ECA) group studies are of great value in English learning.
Accurate and reliable photovoltaic(PV)modeling is crucial for the performance evaluation,control,and optimization of PV systems.However,existing methods for PV parameter identification often suffer from limitations in...Accurate and reliable photovoltaic(PV)modeling is crucial for the performance evaluation,control,and optimization of PV systems.However,existing methods for PV parameter identification often suffer from limitations in accuracy and efficiency.To address these challenges,we propose an adaptive multi-learning cooperation search algorithm(AMLCSA)for efficient identification of unknown parameters in PV models.AMLCSA is a novel algorithm inspired by teamwork behaviors in modern enterprises.It enhances the original cooperation search algorithm in two key aspects:(i)an adaptive multi-learning strategy that dynamically adjusts search ranges using adaptive weights,allowing better individuals to focus on local exploitation while guiding poorer individuals toward global exploration;and(ii)a chaotic grouping reflection strategy that introduces chaotic sequences to enhance population diversity and improve search performance.The effectiveness of AMLCSA is demonstrated on single-diode,double-diode,and three PV-module models.Simulation results show that AMLCSA offers significant advantages in convergence,accuracy,and stability compared to existing state-of-the-art algorithms.展开更多
A group activity recognition algorithm is proposed to improve the recognition accuracy in video surveillance by using complex wavelet domain based Cayley-Klein metric learning.Non-sampled dual-tree complex wavelet pac...A group activity recognition algorithm is proposed to improve the recognition accuracy in video surveillance by using complex wavelet domain based Cayley-Klein metric learning.Non-sampled dual-tree complex wavelet packet transform(NS-DTCWPT)is used to decompose the human images in videos into multi-scale and multi-resolution.An improved local binary pattern(ILBP)and an inner-distance shape context(IDSC)combined with bag-of-words model is adopted to extract the decomposed high and low frequency coefficient features.The extracted coefficient features of the training samples are used to optimize Cayley-Klein metric matrix by solving a nonlinear optimization problem.The group activities in videos are recognized by using the method of feature extraction and Cayley-Klein metric learning.Experimental results on behave video set,group activity video set,and self-built video set show that the proposed algorithm has higher recognition accuracy than the existing algorithms.展开更多
Accurate and efficient methods for identifying and tracking each animal in a group are needed to study complex behaviors and social interactions.Traditional tracking methods(e.g.,marking each animal with dye or surgic...Accurate and efficient methods for identifying and tracking each animal in a group are needed to study complex behaviors and social interactions.Traditional tracking methods(e.g.,marking each animal with dye or surgically implanting microchips)can be invasive and may have an impact on the social behavior being measured.To overcome these shortcomings,video-based methods for tracking unmarked animals,such as fruit flies and zebrafish,have been developed.However,tracking individual mice in a group remains a challenging problem because of their flexible body and complicated interaction patterns.In this study,we report the development of a multi-object tracker for mice that uses the Faster region-based convolutional neural network(R-CNN)deep learning algorithm with geometric transformations in combination with multi-camera/multi-image fusion technology.The system successfully tracked every individual in groups of unmarked mice and was applied to investigate chasing behavior.The proposed system constitutes a step forward in the noninvasive tracking of individual mice engaged in social behavior.展开更多
American Sign Language(ASL)images can be used as a communication tool by determining numbers and letters using the shape of the fingers.Particularly,ASL can have an key role in communication for hearing-impaired perso...American Sign Language(ASL)images can be used as a communication tool by determining numbers and letters using the shape of the fingers.Particularly,ASL can have an key role in communication for hearing-impaired persons and conveying information to other persons,because sign language is their only channel of expression.Representative ASL recognition methods primarily adopt images,sensors,and pose-based recognition techniques,and employ various gestures together with hand-shapes.This study briefly reviews these attempts at ASL recognition and provides an improved ASL classification model that attempts to develop a deep learning method with meta-layers.In the proposed model,the collected ASL images were clustered based on similarities in shape,and clustered group classification was first performed,followed by reclassification within the group.The experiments were conducted with various groups using different learning layers to improve the accuracy of individual image recognition.After selecting the optimized group,we proposed a meta-layered learning model with the highest recognition rate using a deep learning method of image processing.The proposed model exhibited an improved performance compared with the general classification model.展开更多
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 partic...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.展开更多
The Gleason grade group(GG)is an important basis for assessing the malignancy of prostate can-cer,but it requires invasive biopsy to obtain pathology.To noninvasively evaluate GG,an automatic prediction method is prop...The Gleason grade group(GG)is an important basis for assessing the malignancy of prostate can-cer,but it requires invasive biopsy to obtain pathology.To noninvasively evaluate GG,an automatic prediction method is proposed based on multi-scale convolutional neural network of the ensemble attention module trained with curriculum learning.First,a lesion-attention map based on the image of the region of interest is proposed in combination with the bottleneck attention module to make the network more focus on the lesion area.Second,the feature pyramid network is combined to make the network better learn the multi-scale information of the lesion area.Finally,in the network training,a curriculum based on the consistency gap between the visual evaluation and the pathological grade is proposed,which further improves the prediction performance of the network.Ex-perimental results show that the proposed method is better than the traditional network model in predicting GG performance.The quadratic weighted Kappa is 0.4711 and the positive predictive value for predicting clinically significant cancer is 0.9369.展开更多
This paper examines dependencies of voice and video contents on human perception of group (or inter-destination) synchronization error in remote learning by Quality of Experience (QoE) assessment. In our assessment, w...This paper examines dependencies of voice and video contents on human perception of group (or inter-destination) synchronization error in remote learning by Quality of Experience (QoE) assessment. In our assessment, we use two videos and three voices (two voices for one video and one voice for the other video). We also investigate influences of silence periods in the voices and temporal relations between the voices and videos (called the tightly-coupled and loosely-coupled contents here). The voices are spoken by a teacher according to the videos. Each subject as a student assesses the group synchronization quality by watching each lecture video and the corresponding explanation voice, and then the subject answers whether he/she perceives the group synchronization error or not. As a result, assessment results illustrate that silence periods mitigate the perception rate of the error, and we can also find that we can more easily perceive the error for tightly-coupled contents than loosely-coupled ones.展开更多
The Learning Communities are an educational project.Its aim is to achieve quality education for children,youth and adults.The method to complete this goal is educational activities as interactive groups.In this paper,...The Learning Communities are an educational project.Its aim is to achieve quality education for children,youth and adults.The method to complete this goal is educational activities as interactive groups.In this paper,we present the solidarity of the students in interactive groups.For this reason,we use the critical communicative method.The research techniques are communicative observation and document analysis.The data has been obtained a learning community in Spain.The results of the analysis show that in interactive groups,the students help each other.Likewise,they cheer up and wait for each other.展开更多
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 ...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].展开更多
基金Key Research and Development Program of Shaanxi Province,“Exploring the Changes and Mechanisms of Cerebral Microcirculation in Patients with Cerebral Small Vessel Disease and Mild Cognitive Impairment Based on OCTA Assessment”(Project No.:2023-YBSF-569)Shaanxi Provincial People’s Hospital Science and Technology Development Incubation Fund,“Early Identification of Cognitive Impairment in Cerebral Small Vessel Disease Based on Eye Tracking Technology”(Project No.:2023YJY-72)Key Research and Development Program of Shaanxi Province,“Study on the Correlation between Deep Medullary Veins and Cognitive Dysfunction in Cerebral Small Vessel Disease”(Project No.:2023-YBSF-033)。
文摘Objective:To explore the application effect of group cooperative learning combined with flipped classroom in standardized training of emergency surgery resident physicians.Methods:95 resident physicians undergoing emergency surgery standardized training in our hospital were randomly divided into an experimental group(46,group cooperative learning+flipped classroom)and a control group(49,traditional teaching).The training period was 2 months.Results:The graduation assessment scores of the experimental group were higher than those of the control group(P<0.05).The improvement in critical thinking ability and self-learning ability was better than that of the control group(P<0.05).The response rate of“yes”to all items of course teaching satisfaction was significantly higher than that of the control group(P<0.05).The satisfaction scores for training teachers were higher than those of the control group(P<0.05).Conclusion:The teaching model of group cooperative learning combined with flipped classroom,through reconstructing the learning process and activating interactive participation,can significantly improve the clinical operation and theoretical foundation of emergency surgery standardized training students.It also cultivates core professional qualities such as critical thinking,self-learning,and team collaboration,providing a feasible paradigm for integrated medical education in this discipline.
文摘This study mainly discussed the effects of three tasks of translating authentic business report on L2 vocabulary learning.160 students were chosen from different majors by a pre-task proficiency test.The findings revealed that task 3 was the optimum task in vo-cabulary gain and direct vocabulary learning had a more facilitated power than incidental vocabulary learning in this translation task forthe learners with the lowest level of vocabulary.This study also suggested that the caution of need and evaluation needed to be adjustedand paid for the learners with the lowest vocabulary level.
文摘Group work learning is one of the hot topics in English learning and teaching today. This discourse will probe the meaning and the advantages of group work learning, as well as its implementation. Also, the discourse discusses the proper time for group work learning. In addition to that, problems of group work learning are enclosed.
基金Na tureScienceFoundationof JiangsuProvinceunder Grant No .BK2005027 and the211 FoundationofSoochow University
文摘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.
基金This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFE0121500in part by the National Natural Science Foundation of China under Grants 61971126 and 61831013.
文摘The joint spatial division and multiplexing(JSDM)is a two-phase precoding scheme for massive multiple-input-multiple-output(MIMO)system under frequency division duplex(FDD)mode to reduce the amount of channel state information(CSI)feedback.To apply this scheme,users need to be partitioned into groups so that users in the same group have similar channel covariance eigenvectors while users in different groups have almost orthogonal eigenvectors.In this paper,taking the clustered user model into account,we consider the user grouping of JSDM for the downlink massive MIMO system with uniform planar antenna array(UPA)at base station(BS).A deep learning based user grouping algorithm is proposed to improve the efficiency of the user grouping process.The proposed grouping algorithm transfers the statistical CSI of all users into a picture,and utilizes the deep learning enabled objective detection model you look only once(YOLO)to divide the users into different groups rapidly.Simulation results show that the proposed user grouping scheme can achieve higher sum rate with less time delay.
文摘In a task-based communicative classroom, group activities are effective ways to devdop students' 4 basic language skills. However, not all group activities can reach the expected results. English teachers should pay attention to some aspects in organizing a classroom group activity.
文摘Among the various extra-curriculum activities,which are indispensable for English teaching,Extra-curriculum Activity(ECA) group studies are of great value in English learning.
基金supported by the National Natural Science Foundation of China(Grant Nos.62303197,62273214)the Natural Science Foundation of Shandong Province(ZR2024MFO18).
文摘Accurate and reliable photovoltaic(PV)modeling is crucial for the performance evaluation,control,and optimization of PV systems.However,existing methods for PV parameter identification often suffer from limitations in accuracy and efficiency.To address these challenges,we propose an adaptive multi-learning cooperation search algorithm(AMLCSA)for efficient identification of unknown parameters in PV models.AMLCSA is a novel algorithm inspired by teamwork behaviors in modern enterprises.It enhances the original cooperation search algorithm in two key aspects:(i)an adaptive multi-learning strategy that dynamically adjusts search ranges using adaptive weights,allowing better individuals to focus on local exploitation while guiding poorer individuals toward global exploration;and(ii)a chaotic grouping reflection strategy that introduces chaotic sequences to enhance population diversity and improve search performance.The effectiveness of AMLCSA is demonstrated on single-diode,double-diode,and three PV-module models.Simulation results show that AMLCSA offers significant advantages in convergence,accuracy,and stability compared to existing state-of-the-art algorithms.
基金Supported by the National Natural Science Foundation of China(61672032,61401001)the Natural Science Foundation of Anhui Province(1408085MF121)the Opening Foundation of Anhui Key Laboratory of Polarization Imaging Detection Technology(2016-KFKT-003)
文摘A group activity recognition algorithm is proposed to improve the recognition accuracy in video surveillance by using complex wavelet domain based Cayley-Klein metric learning.Non-sampled dual-tree complex wavelet packet transform(NS-DTCWPT)is used to decompose the human images in videos into multi-scale and multi-resolution.An improved local binary pattern(ILBP)and an inner-distance shape context(IDSC)combined with bag-of-words model is adopted to extract the decomposed high and low frequency coefficient features.The extracted coefficient features of the training samples are used to optimize Cayley-Klein metric matrix by solving a nonlinear optimization problem.The group activities in videos are recognized by using the method of feature extraction and Cayley-Klein metric learning.Experimental results on behave video set,group activity video set,and self-built video set show that the proposed algorithm has higher recognition accuracy than the existing algorithms.
基金supported by grants from the National Key R&D Program of China(2017YFA0105201)the National Natural Science Foundation of China(81925011,92149304,31900698,32170954,and 32100763+2 种基金the Key-Area Research and Development Program of Guangdong Province(2019B030335001)The Youth Beijing Scholars Program(015),Support Project of High-level Teachers in Beijing Municipal Universities(CIT&TCD20190334)Beijing Advanced Innovation Center for Big Data-based Precision Medicine,Capital Medical University,Beijing,China(PXM2021_014226_000026).
文摘Accurate and efficient methods for identifying and tracking each animal in a group are needed to study complex behaviors and social interactions.Traditional tracking methods(e.g.,marking each animal with dye or surgically implanting microchips)can be invasive and may have an impact on the social behavior being measured.To overcome these shortcomings,video-based methods for tracking unmarked animals,such as fruit flies and zebrafish,have been developed.However,tracking individual mice in a group remains a challenging problem because of their flexible body and complicated interaction patterns.In this study,we report the development of a multi-object tracker for mice that uses the Faster region-based convolutional neural network(R-CNN)deep learning algorithm with geometric transformations in combination with multi-camera/multi-image fusion technology.The system successfully tracked every individual in groups of unmarked mice and was applied to investigate chasing behavior.The proposed system constitutes a step forward in the noninvasive tracking of individual mice engaged in social behavior.
基金This research was supported by the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT and Future Planning(NRF-2019R1A2C1084308).
文摘American Sign Language(ASL)images can be used as a communication tool by determining numbers and letters using the shape of the fingers.Particularly,ASL can have an key role in communication for hearing-impaired persons and conveying information to other persons,because sign language is their only channel of expression.Representative ASL recognition methods primarily adopt images,sensors,and pose-based recognition techniques,and employ various gestures together with hand-shapes.This study briefly reviews these attempts at ASL recognition and provides an improved ASL classification model that attempts to develop a deep learning method with meta-layers.In the proposed model,the collected ASL images were clustered based on similarities in shape,and clustered group classification was first performed,followed by reclassification within the group.The experiments were conducted with various groups using different learning layers to improve the accuracy of individual image recognition.After selecting the optimized group,we proposed a meta-layered learning model with the highest recognition rate using a deep learning method of image processing.The proposed model exhibited an improved performance compared with the general classification model.
基金supported by the Innovation Capacity Construction Project of Jilin Development and Reform Commission(2020C017-2)Science and Technology Development Plan Project of Jilin Province(20210201082GX)。
文摘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.
基金Foundation item:the Suzhou Municipal Health and Family Planning Commission's Key Diseases Diagnosis and Treatment Program(No.LCzX202001)the Science and Technology Development Project ofSuzhou(Nos.SS2019012andSKY2021031)+1 种基金the Youth Innovation Promotion Association CAS(No.2021324)the Medical Research Project of Jiangsu Provincial Health and Family Planning Commission(No.M2020068)。
文摘The Gleason grade group(GG)is an important basis for assessing the malignancy of prostate can-cer,but it requires invasive biopsy to obtain pathology.To noninvasively evaluate GG,an automatic prediction method is proposed based on multi-scale convolutional neural network of the ensemble attention module trained with curriculum learning.First,a lesion-attention map based on the image of the region of interest is proposed in combination with the bottleneck attention module to make the network more focus on the lesion area.Second,the feature pyramid network is combined to make the network better learn the multi-scale information of the lesion area.Finally,in the network training,a curriculum based on the consistency gap between the visual evaluation and the pathological grade is proposed,which further improves the prediction performance of the network.Ex-perimental results show that the proposed method is better than the traditional network model in predicting GG performance.The quadratic weighted Kappa is 0.4711 and the positive predictive value for predicting clinically significant cancer is 0.9369.
文摘This paper examines dependencies of voice and video contents on human perception of group (or inter-destination) synchronization error in remote learning by Quality of Experience (QoE) assessment. In our assessment, we use two videos and three voices (two voices for one video and one voice for the other video). We also investigate influences of silence periods in the voices and temporal relations between the voices and videos (called the tightly-coupled and loosely-coupled contents here). The voices are spoken by a teacher according to the videos. Each subject as a student assesses the group synchronization quality by watching each lecture video and the corresponding explanation voice, and then the subject answers whether he/she perceives the group synchronization error or not. As a result, assessment results illustrate that silence periods mitigate the perception rate of the error, and we can also find that we can more easily perceive the error for tightly-coupled contents than loosely-coupled ones.
文摘The Learning Communities are an educational project.Its aim is to achieve quality education for children,youth and adults.The method to complete this goal is educational activities as interactive groups.In this paper,we present the solidarity of the students in interactive groups.For this reason,we use the critical communicative method.The research techniques are communicative observation and document analysis.The data has been obtained a learning community in Spain.The results of the analysis show that in interactive groups,the students help each other.Likewise,they cheer up and wait for each other.
文摘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].
