High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an eff...High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.展开更多
English is a very important subject at school.I want to learn it well so I work hard.I can remember most new word s.I like pair work best because I like to talk with my classmates in English.I think writing is the mos...English is a very important subject at school.I want to learn it well so I work hard.I can remember most new word s.I like pair work best because I like to talk with my classmates in English.I think writing is the most difficult part in English learning,so I will spend more time on it every day.My writing needs improving.I think reading texts aloud is a very useful way in English learning.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
The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep le...The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.展开更多
In recent years,deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields.In the diagnosis of sucker-rod pump...In recent years,deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields.In the diagnosis of sucker-rod pump working conditions,due to the lack of a large-scale dynamometer card data set,the advantages of a deep convolutional neural network are not well reflected,and its application is limited.Therefore,this paper proposes an intelligent diagnosis method of the working conditions in sucker-rod pump wells based on transfer learning,which is used to solve the problem of too few samples in a dynamometer card data set.Based on the dynamometer cards measured in oilfields,image classification and preprocessing are conducted,and a dynamometer card data set including 10 typical working conditions is created.On this basis,using a trained deep convolutional neural network learning model,model training and parameter optimization are conducted,and the learned deep dynamometer card features are transferred and applied so as to realize the intelligent diagnosis of dynamometer cards.The experimental results show that transfer learning is feasible,and the performance of the deep convolutional neural network is better than that of the shallow convolutional neural network and general fully connected neural network.The deep convolutional neural network can effectively and accurately diagnose the working conditions of sucker-rod pump wells and provide an effective method to solve the problem of few samples in dynamometer card data sets.展开更多
The current advancement in cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT)transformed the traditional healthcare system into smart healthcare.Healthcare services could be enhanced by incorp...The current advancement in cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT)transformed the traditional healthcare system into smart healthcare.Healthcare services could be enhanced by incorporating key techniques like AI and IoT.The convergence of AI and IoT provides distinct opportunities in the medical field.Fall is regarded as a primary cause of death or post-traumatic complication for the ageing population.Therefore,earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary support.Lately,the emergence of IoT,AI,smartphones,wearables,and so on making it possible to design fall detection(FD)systems for smart home care.This article introduces a new Teamwork Optimization with Deep Learning based Fall Detection for IoT Enabled Smart Healthcare Systems(TWODLFDSHS).The TWODL-FDSHS technique’s goal is to detect fall events for a smart healthcare system.Initially,the presented TWODL-FDSHS technique exploits IoT devices for the data collection process.Next,the TWODLFDSHS technique applies the TWO with Capsule Network(CapsNet)model for feature extraction.At last,a deep random vector functional link network(DRVFLN)with an Adam optimizer is exploited for fall event detection.A wide range of simulations took place to exhibit the enhanced performance of the presentedTWODL-FDSHS technique.The experimental outcomes stated the enhancements of the TWODL-FDSHS method over other models with increased accuracy of 98.30%on the URFD dataset.展开更多
Video object tracking is an important research topic of computer vision, whichfinds a wide range of applications in video surveillance, robotics, human-computerinteraction and so on. Although many moving object tracki...Video object tracking is an important research topic of computer vision, whichfinds a wide range of applications in video surveillance, robotics, human-computerinteraction and so on. Although many moving object tracking algorithms have beenproposed, there are still many difficulties in the actual tracking process, such asillumination change, occlusion, motion blurring, scale change, self-change and so on.Therefore, the development of object tracking technology is still challenging. Theemergence of deep learning theory and method provides a new opportunity for theresearch of object tracking, and it is also the main theoretical framework for the researchof moving object tracking algorithm in this paper. In this paper, the existing deeptracking-based target tracking algorithms are classified and sorted out. Based on theprevious knowledge and my own understanding, several solutions are proposed for theexisting methods. In addition, the existing deep learning target tracking method is stilldifficult to meet the requirements of real-time, how to design the network and trackingprocess to achieve speed and effect improvement, there is still a lot of research space.展开更多
The standard design technology co-optimization(DTCO)involves frequent interactions between circuit design and process manufacturing,which requires several months.To assist designers in establishing a bridge between de...The standard design technology co-optimization(DTCO)involves frequent interactions between circuit design and process manufacturing,which requires several months.To assist designers in establishing a bridge between device parameters and circuit metrics efficiently,and provide guidance for parameter optimization in the early stages of circuit design.In this paper,we propose an efficient machine learning(ML)-enhanced DTCO framework.This framework achieves the co-optimization of device parameters and circuit metrics.We select the gate metal work function(WF)as the parameter to validate the effectiveness of our framework.And the ridge regression approach is used to bypass TCAD simulation,compact model extraction and cell library characterization.We reduces time consumption by at least 92%compared to traditional DTCO framework,while ensuring that errors of delay,internal power consumption and leakage power below 4 ps,0.035mJ,and 0.4μW,respectively.By adjusting the WF,we achieved a better balance between circuit delay and power consumption.This work contributes to designers exploring a broader design space and achieving a efficient DTCO flow.展开更多
The change of working conditions not only makes the data distribution inconsistent,but also increases the diagnosis difficulty of fuzzy samples at the fault boundary.The traditional distance-based deep metric learning...The change of working conditions not only makes the data distribution inconsistent,but also increases the diagnosis difficulty of fuzzy samples at the fault boundary.The traditional distance-based deep metric learning cannot effectively classify the fuzzy samples at the fault boundary.In the traditional transfer learning models,the maximum mean discrepancy(MMD)and joint maximum mean discrepancy only increase the transferability of same-class samples,and neglect the discriminability of different-class samples across different domains.The discriminative joint probability MMD(DJP-MMD)increases the transferability of same-class samples and the discriminability of different-class samples across different domains,but it only considers the global transferability of all fault classes,ignoring the different transferability of each same fault class.Therefore,a Yu norm-based deep transfer metric learning based on weighted DJP-MMD is proposed to further improve the diagnosis accuracy of bearings under variable working conditions.The deep transfer metric learning model adopts the Yu norm-based similarity instead of the distance-based similarity to effectively classify the data samples,especially those at the fault boundary,and uses the weighted DJP-MMD to measure the data distribution discrepancy between the source and target domains to increase the transferability of each same-class samples and discriminability of different-class samples across different domains.Through the fault diagnosis analysis on bearings under variable working conditions,the diagnosis results demonstrate that the proposed deep transfer metric learning model can diagnose bearing faults with higher accuracy,stronger generalization and anti-noise capabilities compared with other fault diagnosis methods based on transfer learning.展开更多
The current research was grounded in prior interdisciplinary research that showed cognitive ability (verbal ability for translating cognitions into oral language) and multiple-working memory endophenotypes (behavioral...The current research was grounded in prior interdisciplinary research that showed cognitive ability (verbal ability for translating cognitions into oral language) and multiple-working memory endophenotypes (behavioral markers of genetic or brain bases of language learning) predict reading and writing achievement in students with and without specific learning disabilities in written language (SLDs-WL). Results largely replicated prior findings that verbally gifted with dyslexia score higher on reading and writing achievement than those with average verbal ability but not on endophenotypes. The current study extended that research by comparing those with and without SLDs-WL with assessed verbal ability held constant. The verbally gifted without SLDs-WL (n = 14) scored higher than the verbally gifted with SLDs-WL (n = 27) on six language skills (oral sentence construction, best and fastest handwriting in copying, single real word oral reading accuracy, oral pseudoword reading accuracy and rate) and four endophenotypes (orthographic and morphological coding, orthographic loop, and switching attention). The verbally average without SLDs-WL (n = 6) scored higher than the verbally average with SLDs-WL (n = 22) on four language skills (best and fastest hand-writing in copying, oral pseudoword reading accuracy and rate) and two endophenotypes (orthographic coding and orthographic loop). Implications of results for translating interdisciplinary research into flexible definitions for assessment and instruction to serve students with varying verbal abilities and language learning and endophenotype profiles are discussed along with directions for future research.展开更多
Introduction: One form of child labor is early learning, which is a less worrying phenomenon in our communities in Benin. The objective of this study was to assess the practice of early learning for children in rural ...Introduction: One form of child labor is early learning, which is a less worrying phenomenon in our communities in Benin. The objective of this study was to assess the practice of early learning for children in rural areas. Methods: This was a cross-sectional study combined with a qualitative component conducted in the Kissamey district of Benin with four targets: child apprentices (52), master craftsmen (41), parents and guardians (34), local authorities (9). The collection tools were a questionnaire and an interview guide. Results: The frequency of early learning among children was 32.07% with difficult socioeconomic conditions: polygamy (75%), strong siblings (79%), out of school (33%), unmet food needs (96%). The reasons for early learning according to parents were: refusal of the child to go to school (44%), financial difficulties (31%), school failure (22%), but 38% of these children did not know the reason for their learning. The actors had little knowledge of the regulatory texts. Conclusion: Early learning remains a societal problem related to out-of-school and difficult socioeconomic conditions.展开更多
This study challenges understandings on the‘redundancy effect’of cognitive load theory and visual/verbal classifications of dual-coding theory.Current understandings assert that a multimedia mix of narration and tex...This study challenges understandings on the‘redundancy effect’of cognitive load theory and visual/verbal classifications of dual-coding theory.Current understandings assert that a multimedia mix of narration and text displayed during e-learning leads to cognitive overload,thus,impeding learning[1,2].Previous research suggests that for optimal learning to occur,the most effective multimedia mix for e-learning presentation is the use of graphics and narration[3-6].The current study was undertaken with 90 undergraduate students at a British University.Participants were allocated to one of three groups.Each group used a different multimedia mix of a music e-learning program.Participants received learning material electronically,which involved either a mix of narration and text,graphics and text,or graphics and narration.Learning was measured by differences in music knowledge scores obtained before and after receiving the learning material.Results indicate that the combination of text and narration is most effective for learning,compared to combinations of graphics and text and graphics and narration.These findings challenge the currently accepted stance on the redundancy effect in e-learning design.展开更多
The current situation of English learners in class is not optimistic. Facing with such a situation, how to promote students' learning is a big problem in English teaching. Cooperative learning is a kind of effecti...The current situation of English learners in class is not optimistic. Facing with such a situation, how to promote students' learning is a big problem in English teaching. Cooperative learning is a kind of effective learning method and teaching strategy,which is student-centered, group-centered, and aims at common learning goals. The paper focuses on the definition, advantages and effective implementation of cooperative learning.展开更多
Since entering the 19th century, cooperative learning theory has been gradually formed and popularized. It has played a very important role in the field of education and has become the most important way of learning i...Since entering the 19th century, cooperative learning theory has been gradually formed and popularized. It has played a very important role in the field of education and has become the most important way of learning in the field of education. In the process of college English teaching, reading teaching, as an important component, enables students to learn English knowledge better through reading, thus improving students' reading comprehension and arousing students to think and analyze deeply. However, in the process of college English reading teaching, there are still many problems, especially the students' poor English reading ability and the inefficient classroom teaching, which seriously hinder the healthy development of college English reading teaching. Therefore, the cooperative learning theory is integrated into the teaching process, and the teaching model and methods of college English reading are continuously innovated, thus effectively improving the efficiency and quality of college English reading teaching.展开更多
基金We would like to thank the associate editor and the reviewers for their constructive comments.This work was supported in part by the National Natural Science Foundation of China under Grant 62203234in part by the State Key Laboratory of Robotics of China under Grant 2023-Z03+1 种基金in part by the Natural Science Foundation of Liaoning Province under Grant 2023-BS-025in part by the Research Program of Liaoning Liaohe Laboratory under Grant LLL23ZZ-02-02.
文摘High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.
文摘English is a very important subject at school.I want to learn it well so I work hard.I can remember most new word s.I like pair work best because I like to talk with my classmates in English.I think writing is the most difficult part in English learning,so I will spend more time on it every day.My writing needs improving.I think reading texts aloud is a very useful way in English learning.
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supported in part by the National Natural Science Foundation of China under Grant U1908212,62203432 and 92067205in part by the State Key Laboratory of Robotics of China under Grant 2023-Z03 and 2023-Z15in part by the Natural Science Foundation of Liaoning Province under Grant 2020-KF-11-02.
文摘The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.
文摘In recent years,deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields.In the diagnosis of sucker-rod pump working conditions,due to the lack of a large-scale dynamometer card data set,the advantages of a deep convolutional neural network are not well reflected,and its application is limited.Therefore,this paper proposes an intelligent diagnosis method of the working conditions in sucker-rod pump wells based on transfer learning,which is used to solve the problem of too few samples in a dynamometer card data set.Based on the dynamometer cards measured in oilfields,image classification and preprocessing are conducted,and a dynamometer card data set including 10 typical working conditions is created.On this basis,using a trained deep convolutional neural network learning model,model training and parameter optimization are conducted,and the learned deep dynamometer card features are transferred and applied so as to realize the intelligent diagnosis of dynamometer cards.The experimental results show that transfer learning is feasible,and the performance of the deep convolutional neural network is better than that of the shallow convolutional neural network and general fully connected neural network.The deep convolutional neural network can effectively and accurately diagnose the working conditions of sucker-rod pump wells and provide an effective method to solve the problem of few samples in dynamometer card data sets.
基金The Deanship of Scientific Research (DSR)at King Abdulaziz University (KAU),Jeddah,Saudi Arabia has funded this project,under grant no.KEP-4-120-42.
文摘The current advancement in cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT)transformed the traditional healthcare system into smart healthcare.Healthcare services could be enhanced by incorporating key techniques like AI and IoT.The convergence of AI and IoT provides distinct opportunities in the medical field.Fall is regarded as a primary cause of death or post-traumatic complication for the ageing population.Therefore,earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary support.Lately,the emergence of IoT,AI,smartphones,wearables,and so on making it possible to design fall detection(FD)systems for smart home care.This article introduces a new Teamwork Optimization with Deep Learning based Fall Detection for IoT Enabled Smart Healthcare Systems(TWODLFDSHS).The TWODL-FDSHS technique’s goal is to detect fall events for a smart healthcare system.Initially,the presented TWODL-FDSHS technique exploits IoT devices for the data collection process.Next,the TWODLFDSHS technique applies the TWO with Capsule Network(CapsNet)model for feature extraction.At last,a deep random vector functional link network(DRVFLN)with an Adam optimizer is exploited for fall event detection.A wide range of simulations took place to exhibit the enhanced performance of the presentedTWODL-FDSHS technique.The experimental outcomes stated the enhancements of the TWODL-FDSHS method over other models with increased accuracy of 98.30%on the URFD dataset.
基金supported by National Natural Science Foundationof China (Grant No. 51874300)the National Natural Science Foundation of China andShanxi Provincial People’s Government Jointly Funded Project of China for Coal Baseand Low Carbon (Grant No. U1510115)+2 种基金National Natural Science Foundation of China(51104157)the Qing Lan Project, the China Postdoctoral Science Foundation (Grant No.2013T60574)the Scientific Instrument Developing Project of the Chinese Academy ofSciences (Grant No. YJKYYQ20170074).
文摘Video object tracking is an important research topic of computer vision, whichfinds a wide range of applications in video surveillance, robotics, human-computerinteraction and so on. Although many moving object tracking algorithms have beenproposed, there are still many difficulties in the actual tracking process, such asillumination change, occlusion, motion blurring, scale change, self-change and so on.Therefore, the development of object tracking technology is still challenging. Theemergence of deep learning theory and method provides a new opportunity for theresearch of object tracking, and it is also the main theoretical framework for the researchof moving object tracking algorithm in this paper. In this paper, the existing deeptracking-based target tracking algorithms are classified and sorted out. Based on theprevious knowledge and my own understanding, several solutions are proposed for theexisting methods. In addition, the existing deep learning target tracking method is stilldifficult to meet the requirements of real-time, how to design the network and trackingprocess to achieve speed and effect improvement, there is still a lot of research space.
基金supported by the Cooperation Project between Xidian University and Shenzhen Fuxin Technology Company Ltd.(Electronic Design Automation Technology Innovation Center Project in Guangdong-Hong Kong Macao Greater Bay Area)well as by the Project of Science and Technology on Reliability Physics and Application Technology of Electronic Component Laboratory(6142806230302).
文摘The standard design technology co-optimization(DTCO)involves frequent interactions between circuit design and process manufacturing,which requires several months.To assist designers in establishing a bridge between device parameters and circuit metrics efficiently,and provide guidance for parameter optimization in the early stages of circuit design.In this paper,we propose an efficient machine learning(ML)-enhanced DTCO framework.This framework achieves the co-optimization of device parameters and circuit metrics.We select the gate metal work function(WF)as the parameter to validate the effectiveness of our framework.And the ridge regression approach is used to bypass TCAD simulation,compact model extraction and cell library characterization.We reduces time consumption by at least 92%compared to traditional DTCO framework,while ensuring that errors of delay,internal power consumption and leakage power below 4 ps,0.035mJ,and 0.4μW,respectively.By adjusting the WF,we achieved a better balance between circuit delay and power consumption.This work contributes to designers exploring a broader design space and achieving a efficient DTCO flow.
基金funded by the National Natural Science Foundation of China(Grant No.51775391).
文摘The change of working conditions not only makes the data distribution inconsistent,but also increases the diagnosis difficulty of fuzzy samples at the fault boundary.The traditional distance-based deep metric learning cannot effectively classify the fuzzy samples at the fault boundary.In the traditional transfer learning models,the maximum mean discrepancy(MMD)and joint maximum mean discrepancy only increase the transferability of same-class samples,and neglect the discriminability of different-class samples across different domains.The discriminative joint probability MMD(DJP-MMD)increases the transferability of same-class samples and the discriminability of different-class samples across different domains,but it only considers the global transferability of all fault classes,ignoring the different transferability of each same fault class.Therefore,a Yu norm-based deep transfer metric learning based on weighted DJP-MMD is proposed to further improve the diagnosis accuracy of bearings under variable working conditions.The deep transfer metric learning model adopts the Yu norm-based similarity instead of the distance-based similarity to effectively classify the data samples,especially those at the fault boundary,and uses the weighted DJP-MMD to measure the data distribution discrepancy between the source and target domains to increase the transferability of each same-class samples and discriminability of different-class samples across different domains.Through the fault diagnosis analysis on bearings under variable working conditions,the diagnosis results demonstrate that the proposed deep transfer metric learning model can diagnose bearing faults with higher accuracy,stronger generalization and anti-noise capabilities compared with other fault diagnosis methods based on transfer learning.
文摘The current research was grounded in prior interdisciplinary research that showed cognitive ability (verbal ability for translating cognitions into oral language) and multiple-working memory endophenotypes (behavioral markers of genetic or brain bases of language learning) predict reading and writing achievement in students with and without specific learning disabilities in written language (SLDs-WL). Results largely replicated prior findings that verbally gifted with dyslexia score higher on reading and writing achievement than those with average verbal ability but not on endophenotypes. The current study extended that research by comparing those with and without SLDs-WL with assessed verbal ability held constant. The verbally gifted without SLDs-WL (n = 14) scored higher than the verbally gifted with SLDs-WL (n = 27) on six language skills (oral sentence construction, best and fastest handwriting in copying, single real word oral reading accuracy, oral pseudoword reading accuracy and rate) and four endophenotypes (orthographic and morphological coding, orthographic loop, and switching attention). The verbally average without SLDs-WL (n = 6) scored higher than the verbally average with SLDs-WL (n = 22) on four language skills (best and fastest hand-writing in copying, oral pseudoword reading accuracy and rate) and two endophenotypes (orthographic coding and orthographic loop). Implications of results for translating interdisciplinary research into flexible definitions for assessment and instruction to serve students with varying verbal abilities and language learning and endophenotype profiles are discussed along with directions for future research.
文摘Introduction: One form of child labor is early learning, which is a less worrying phenomenon in our communities in Benin. The objective of this study was to assess the practice of early learning for children in rural areas. Methods: This was a cross-sectional study combined with a qualitative component conducted in the Kissamey district of Benin with four targets: child apprentices (52), master craftsmen (41), parents and guardians (34), local authorities (9). The collection tools were a questionnaire and an interview guide. Results: The frequency of early learning among children was 32.07% with difficult socioeconomic conditions: polygamy (75%), strong siblings (79%), out of school (33%), unmet food needs (96%). The reasons for early learning according to parents were: refusal of the child to go to school (44%), financial difficulties (31%), school failure (22%), but 38% of these children did not know the reason for their learning. The actors had little knowledge of the regulatory texts. Conclusion: Early learning remains a societal problem related to out-of-school and difficult socioeconomic conditions.
文摘This study challenges understandings on the‘redundancy effect’of cognitive load theory and visual/verbal classifications of dual-coding theory.Current understandings assert that a multimedia mix of narration and text displayed during e-learning leads to cognitive overload,thus,impeding learning[1,2].Previous research suggests that for optimal learning to occur,the most effective multimedia mix for e-learning presentation is the use of graphics and narration[3-6].The current study was undertaken with 90 undergraduate students at a British University.Participants were allocated to one of three groups.Each group used a different multimedia mix of a music e-learning program.Participants received learning material electronically,which involved either a mix of narration and text,graphics and text,or graphics and narration.Learning was measured by differences in music knowledge scores obtained before and after receiving the learning material.Results indicate that the combination of text and narration is most effective for learning,compared to combinations of graphics and text and graphics and narration.These findings challenge the currently accepted stance on the redundancy effect in e-learning design.
文摘The current situation of English learners in class is not optimistic. Facing with such a situation, how to promote students' learning is a big problem in English teaching. Cooperative learning is a kind of effective learning method and teaching strategy,which is student-centered, group-centered, and aims at common learning goals. The paper focuses on the definition, advantages and effective implementation of cooperative learning.
文摘Since entering the 19th century, cooperative learning theory has been gradually formed and popularized. It has played a very important role in the field of education and has become the most important way of learning in the field of education. In the process of college English teaching, reading teaching, as an important component, enables students to learn English knowledge better through reading, thus improving students' reading comprehension and arousing students to think and analyze deeply. However, in the process of college English reading teaching, there are still many problems, especially the students' poor English reading ability and the inefficient classroom teaching, which seriously hinder the healthy development of college English reading teaching. Therefore, the cooperative learning theory is integrated into the teaching process, and the teaching model and methods of college English reading are continuously innovated, thus effectively improving the efficiency and quality of college English reading teaching.
