Purpose:Despite the growing emphasis on promoting school collaborations,few quantitative studies have investigated the relationship between school-to-school collaboration and student learning,particularly outside the ...Purpose:Despite the growing emphasis on promoting school collaborations,few quantitative studies have investigated the relationship between school-to-school collaboration and student learning,particularly outside the United Kingdom and the United States.This study examines the effects of Shanghai's Strong School Project(SSP),a policy initiated in 2018 to improve low-achieving schools through partnerships with high-performing schools,on students'academic achievement and explores potential mediating factors.Design/Approach/Methods:We used data from the 20l8 and 202I waves of the evaluation of middle school quality in Shanghai,including test scores in Chinese,English,Math,and Sciences,and surveys of students,teachers,and principals.Our analysis employed difference-in-differences(DID),propensity score matching combined with DID analysis(PSM-DID),and causal mediation analysis within the framework of DID.Findings:School-to-school collaboration through SSP significantly enhanced test scores in all four subjects.The most pronounced effect was in Math,with a 0.67 standard deviation increase,followed by Chinese at 0.63 standard deviation.Causal mediation analysis indicated that improvements in academic performance primarily stemmed from enhanced peer relationships and principal leadership.Originality/Value:This study provides quantitative evidence linking school-to-school collaboration with student learning outcomes,highlighting positive associations between collaboration and school outcomes in China,adding to the predominant literature from the United Kingdom and the United States.展开更多
Optimal retrofit of low-performance units(LPUs)is promising to abate overflow pollutant mass loading of sewer systems during wet-weathers.This study presents a combination of mathematical model and Sobol algorithm to ...Optimal retrofit of low-performance units(LPUs)is promising to abate overflow pollutant mass loading of sewer systems during wet-weathers.This study presents a combination of mathematical model and Sobol algorithm to help identify LPUs of sewer systems and design retrofitting strategies.Therefore,the solution to minimize the overflow pollutant mass loading from sewers systems can be efficiently obtained.The developed method was demonstrated at a catchment served by one wastewater treatment plant in the Chaohu City,Anhui Province of China,with five pumping stations and a total sewer length of 58.3 km.Within the catchment,there are three rivers and a small lake to receive overflows from the sewer system.Among them,one river that was mostly polluted was selected as the object of overflow pollution abatement during wet weather period.After identifying the LPUs of the sewer system and developing retrofitting strategies using Sobol sequence,the mitigation of overflow pollution during wet weather period was analyzed.Results show that the mass loading of chemical oxygen demand(COD)discharged into the target river could be reduced by 40.6%,by implementing optimal retrofit strategy of LPUs,i.e.,increasing the conveyance capacities of two pumping stations by 2.5–3.2 times and augmenting the diameters of 12 sewers by 1.25–1.29 times.To further coordinate the abatement of overflow pollution and retrofit investment,Sobol sensitivity analysis was conducted to screen the dominant LPUs to update the optimal retrofit strategy.By applying the updated strategy,the overflow COD mass loading per overflow event was close to that of non-updated strategy,while the retrofitting length of sewers was reduced by 40%.Therefore,on the basis of the presented method,decision-makers can flexibly develop retrofitting strategies of sewer system to abate overflow pollution during wet weathers in a cost-effective way.展开更多
基金financial support for the research,authorship,and/or publication of this article:This work was supported by the Shanghai Pujiang Talent Program(grant number 22PJC056).
文摘Purpose:Despite the growing emphasis on promoting school collaborations,few quantitative studies have investigated the relationship between school-to-school collaboration and student learning,particularly outside the United Kingdom and the United States.This study examines the effects of Shanghai's Strong School Project(SSP),a policy initiated in 2018 to improve low-achieving schools through partnerships with high-performing schools,on students'academic achievement and explores potential mediating factors.Design/Approach/Methods:We used data from the 20l8 and 202I waves of the evaluation of middle school quality in Shanghai,including test scores in Chinese,English,Math,and Sciences,and surveys of students,teachers,and principals.Our analysis employed difference-in-differences(DID),propensity score matching combined with DID analysis(PSM-DID),and causal mediation analysis within the framework of DID.Findings:School-to-school collaboration through SSP significantly enhanced test scores in all four subjects.The most pronounced effect was in Math,with a 0.67 standard deviation increase,followed by Chinese at 0.63 standard deviation.Causal mediation analysis indicated that improvements in academic performance primarily stemmed from enhanced peer relationships and principal leadership.Originality/Value:This study provides quantitative evidence linking school-to-school collaboration with student learning outcomes,highlighting positive associations between collaboration and school outcomes in China,adding to the predominant literature from the United Kingdom and the United States.
基金Project supported by the National Natural Science Foundation of China(Grant No.52170103),the National Key Research and Development Program of China(Grant No.2021YFC3200703)supported by the Scientific Research Program of Changjiang Institute of Survey,Planning,Design and Research(Grant No.CX2020Z24)+2 种基金the China Postdoctoral Science Foundation(Grant No.2023M730366)the Natural Science Foundation of Hubei Province(Grant No.2023AFB475)the Postdoctoral Innovation and Practice Position in Hubei Province(Grant No.2023CXGW04).
文摘Optimal retrofit of low-performance units(LPUs)is promising to abate overflow pollutant mass loading of sewer systems during wet-weathers.This study presents a combination of mathematical model and Sobol algorithm to help identify LPUs of sewer systems and design retrofitting strategies.Therefore,the solution to minimize the overflow pollutant mass loading from sewers systems can be efficiently obtained.The developed method was demonstrated at a catchment served by one wastewater treatment plant in the Chaohu City,Anhui Province of China,with five pumping stations and a total sewer length of 58.3 km.Within the catchment,there are three rivers and a small lake to receive overflows from the sewer system.Among them,one river that was mostly polluted was selected as the object of overflow pollution abatement during wet weather period.After identifying the LPUs of the sewer system and developing retrofitting strategies using Sobol sequence,the mitigation of overflow pollution during wet weather period was analyzed.Results show that the mass loading of chemical oxygen demand(COD)discharged into the target river could be reduced by 40.6%,by implementing optimal retrofit strategy of LPUs,i.e.,increasing the conveyance capacities of two pumping stations by 2.5–3.2 times and augmenting the diameters of 12 sewers by 1.25–1.29 times.To further coordinate the abatement of overflow pollution and retrofit investment,Sobol sensitivity analysis was conducted to screen the dominant LPUs to update the optimal retrofit strategy.By applying the updated strategy,the overflow COD mass loading per overflow event was close to that of non-updated strategy,while the retrofitting length of sewers was reduced by 40%.Therefore,on the basis of the presented method,decision-makers can flexibly develop retrofitting strategies of sewer system to abate overflow pollution during wet weathers in a cost-effective way.
