Combined with the features and strategies of project-based collaborative learning, micro-lesson—as one of modern teaching means, can display the teaching design and implementation of PBL, thus students can improve th...Combined with the features and strategies of project-based collaborative learning, micro-lesson—as one of modern teaching means, can display the teaching design and implementation of PBL, thus students can improve their awareness and ability of cross-cultural communication in the process of experience PBL.展开更多
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 l...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.展开更多
The fine classification of large-scale scenes is becoming more and more important in optical remote sensing applications.As two kinds of typical optical remote sensing data,multispectral images(MSIs)and hyperspectral ...The fine classification of large-scale scenes is becoming more and more important in optical remote sensing applications.As two kinds of typical optical remote sensing data,multispectral images(MSIs)and hyperspectral images(HSIs)have complementary characteristics.The MSI has a large swath and short revisit period,but the number of bands is limited with low spectral resolution,leading to weak separability of between class spectra.Compared with MSI,HSI has hundreds of bands and each of them is narrow in bandwidth,which enable it to have the ability of fine classification,but too long in aspects of revisit period.To make efficient use of their combined advantages,multispectral-hyperspectral remote sensing image collaborative classification has become one of hot topics in remote sensing.To deal with the collaborative classification,most of current methods are unsupervised and only consider the HSI reconstruction as the objective.In this paper,a class-guided coupled dictionary learning method is proposed,which is obviously distinguished from the current methods.Specifically,the proposed method utilizes the labels of training samples to construct discriminative sparse representation coefficient error and classification error as regularization terms,so as to enforce the learned coupled dictionaries to be both representational and discriminative.The learned coupled dictionaries facilitate pixels from the same category have similar sparse represent coefficients,while pixels from different categories have different sparse represent coefficients.The experiments on three pairs of HSI and MSI have shown better classification performance.展开更多
文摘Combined with the features and strategies of project-based collaborative learning, micro-lesson—as one of modern teaching means, can display the teaching design and implementation of PBL, thus students can improve their awareness and ability of cross-cultural communication in the process of experience PBL.
基金funded by the Zhejiang Provincial Educational Science Planning Project“Reconstructing Young Children’s Learning Experience:Research on the Construction and Practice of Sustainable Development Curriculum in Kindergarten”(No.2020SCG202).
文摘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.
基金supported by the National Natural Youth Science Foundation Project (Grant No. 62001142)the Key International Cooperation Project (Grant No. 61720106002)+1 种基金the Distinguished Young Scholars of National Natural Science Foundation of China (Grant No. 62025107)Heilongjiang Postdoctoral Fund (Grant No. LBH-Z20068)
文摘The fine classification of large-scale scenes is becoming more and more important in optical remote sensing applications.As two kinds of typical optical remote sensing data,multispectral images(MSIs)and hyperspectral images(HSIs)have complementary characteristics.The MSI has a large swath and short revisit period,but the number of bands is limited with low spectral resolution,leading to weak separability of between class spectra.Compared with MSI,HSI has hundreds of bands and each of them is narrow in bandwidth,which enable it to have the ability of fine classification,but too long in aspects of revisit period.To make efficient use of their combined advantages,multispectral-hyperspectral remote sensing image collaborative classification has become one of hot topics in remote sensing.To deal with the collaborative classification,most of current methods are unsupervised and only consider the HSI reconstruction as the objective.In this paper,a class-guided coupled dictionary learning method is proposed,which is obviously distinguished from the current methods.Specifically,the proposed method utilizes the labels of training samples to construct discriminative sparse representation coefficient error and classification error as regularization terms,so as to enforce the learned coupled dictionaries to be both representational and discriminative.The learned coupled dictionaries facilitate pixels from the same category have similar sparse represent coefficients,while pixels from different categories have different sparse represent coefficients.The experiments on three pairs of HSI and MSI have shown better classification performance.
