This study examines the relative efficiency of the top 20 Indian public colleges that offer MBAs. These colleges were chosen from a list provided by Careers 360, a magazine in India known for its university rankings. ...This study examines the relative efficiency of the top 20 Indian public colleges that offer MBAs. These colleges were chosen from a list provided by Careers 360, a magazine in India known for its university rankings. The purpose of this study was to evaluate the colleges on an efficiency basis rather than on a total score ranking scale as is the common practice of most publications that rank universities or programs. The ranking method used in this study is based on data envelopment analysis (DEA), a nonparametric procedure for evaluating entities based upon examining inputs in relation to outputs achieved. The rankings using DEA were somewhat different than those given by Careers 360. The results of the DEA analysis of this study rank the universities that are the most efficient at getting students the best salaries and return on investment (ROI) based on the inputs of diversity, work experience, and residency. The authors conclude, as previous studies have shown, that DEA analysis is a useful and non-biased method of comparing university programs.展开更多
Purpose:The aim of our paper is to investigate the role of a mentor leading a research team in the overall scientific performance of an academic institution and the possible risks of their departure with a special att...Purpose:The aim of our paper is to investigate the role of a mentor leading a research team in the overall scientific performance of an academic institution and the possible risks of their departure with a special attention to their publication output.Design/methodology/approach:By using SciVal subject area data,we composed a formula describing the level of vulnerability of any given university in the case of losing any of its leading mentors,identifying other risk factors by dividing their careers into separate stages.Findings:It turns out that the higher field-weighed citation impact is,the better position universities reach in the rankings by subject and the vulnerability of institutions highly depends on the mentors,especially in view of their contribution to the topic clusters.Research limitations:The analysis covers the publication output of leading researchers working at four Hungarian universities,the scope of the analysis is worth being extended.Practical implications:Our analysis has the potential to give an applicable systemic approach as well as a data collection scheme to university managements so as to formulate an inclusive and comprehensive research strategy involving the introduction of a reward system aimed at publications and further encouraging national and international research cooperation.Originality/value:The methodology and the principles of risk assessment laid down in our paper are not restricted to measuring the vulnerability level of a limited group of academic institutions,they can be appropriately used for investigating the role of mentors or leading researchers at every university across the globe.展开更多
On Sep. 8,2009,Switzerland tops the overall ranking in The Global Competitiveness Report 2009-2010, released by the World Economic Forum ahead of its Annual Meeting of the New Champions 2009 in Dalian.
Purpose:Building on Leydesdorff,Bornmann,and Mingers(2019),we elaborate the differences between Tsinghua and Zhejiang University as an empirical example.We address the question of whether differences are statistically...Purpose:Building on Leydesdorff,Bornmann,and Mingers(2019),we elaborate the differences between Tsinghua and Zhejiang University as an empirical example.We address the question of whether differences are statistically significant in the rankings of Chinese universities.We propose methods for measuring statistical significance among different universities within or among countries.Design/methodology/approach:Based on z-testing and overlapping confidence intervals,and using data about 205 Chinese universities included in the Leiden Rankings 2020,we argue that three main groups of Chinese research universities can be distinguished(low,middle,and high).Findings:When the sample of 205 Chinese universities is merged with the 197 US universities included in Leiden Rankings 2020,the results similarly indicate three main groups:low,middle,and high.Using this data(Leiden Rankings and Web of Science),the z-scores of the Chinese universities are significantly below those of the US universities albeit with some overlap.Research limitations:We show empirically that differences in ranking may be due to changes in the data,the models,or the modeling effects on the data.The scientometric groupings are not always stable when we use different methods.Practical implications:Differences among universities can be tested for their statistical significance.The statistics relativize the values of decimals in the rankings.One can operate with a scheme of low/middle/high in policy debates and leave the more fine-grained rankings of individual universities to operational management and local settings.Originality/value:In the discussion about the rankings of universities,the question of whether differences are statistically significant,has,in our opinion,insufficiently been addressed in research evaluations.展开更多
Purpose: Study how economic parameters affect positions in the Academic Ranking of World Universities' top 500 published by the Shanghai Jiao Tong University Graduate School of Education in countries/regions with ...Purpose: Study how economic parameters affect positions in the Academic Ranking of World Universities' top 500 published by the Shanghai Jiao Tong University Graduate School of Education in countries/regions with listed higher education institutions. Design/methodology/approach: The methodology used capitalises on the multi-variate characteristics of the data analysed. The multi-colinearity problem posed is solved by running principal components prior to regression analysis, using both classical(OLS) and robust(Huber and Tukey) methods. Findings: Our results revealed that countries/regions with long ranking traditions are highly competitive. Findings also showed that some countries/regions such as Germany, United Kingdom, Canada, and Italy, had a larger number of universities in the top positions than predicted by the regression model. In contrast, for Japan, a country where social and economic performance is high, the number of ARWU universities projected by the model was much larger than the actual figure. In much the same vein, countries/regions that invest heavily in education, such as Japan and Denmark, had lower than expected results.Research limitations: Using data from only one ranking is a limitation of this study, but the methodology used could be useful to other global rankings. Practical implications: The results provide good insights for policy makers. They indicate the existence of a relationship between research output and the number of universities per million inhabitants. Countries/regions, which have historically prioritised higher education, exhibited highest values for indicators that compose the rankings methodology; furthermore,minimum increase in welfare indicators could exhibited significant rises in the presence of their universities on the rankings.Originality/value: This study is well defined and the result answers important questions about characteristics of countries/regions and their higher education system.展开更多
This paper will discuss one topic in the current debate on higher education: How power is exercised between universities? How do colleges determine what the best college is? What are the differences in the excersis...This paper will discuss one topic in the current debate on higher education: How power is exercised between universities? How do colleges determine what the best college is? What are the differences in the excersise of power in the digital age? The authors analyze one of the mechanisms of relationship and contact between different universities: the rankings. They will discuss the practices that allow certain values and organizations they are becoming central nodes between universities and the influences of the information and communication technologies in the measurement mechanisms. The authors seek to show the rankings serve as mechanisms to exercise power among universities. These measurements become a tool and justification in competition between universities for resources such as funding, prestige, and student demand. The analysis is based on the University of Mexico, the authors use the ranking of the best universities in Latin America and the best universities in Mexico.展开更多
Background: Cause-of-death rankings are often used for planning or evaluating health policy measures. In the European Union, some countries produce cause-of-death statistics by a manual coding of death certificates, w...Background: Cause-of-death rankings are often used for planning or evaluating health policy measures. In the European Union, some countries produce cause-of-death statistics by a manual coding of death certificates, while other countries use an automated coding system. The outcome of these two different methods in terms of the selected underlying cause of death for statistics may vary considerably. Therefore, this study explores the effect of coding method on the ranking of countries by major causes of death. Method: Age and sex standardized rates were extracted for 33 European (related) countries from the cause-of-death registry of the European Statistical Office (Eurostat). Wilcoxon’s rank sum test was applied to the ranking of countries by major causes of death. Results: Statistically significant differences due to coding method were identified for dementia, stroke and pneumonia. These differences could be explained by a different selection of dementia or pneumonia as underlying cause of death and by a different certification practice for stroke. Conclusion: Coding method should be taken into account when constructing or interpreting rankings of countries by cause of death.展开更多
While academics and university administrators often criticize rankings,league tables have become important tools for student decision-making,especially in the Chinese sector.Yet,research has not fully explored how stu...While academics and university administrators often criticize rankings,league tables have become important tools for student decision-making,especially in the Chinese sector.Yet,research has not fully explored how students in China have engaged with both global and local rankings,as most studies have focused on one setting or the other.Likewise,researchers have not tested students’knowledge of rankings,despite the intense focus on these actors by universities.Using a survey of over 900 students from Chinese universities,the author explored how knowledge of rankings varies in different student populations.Through multivariate analysis,it is found that students from elite institutions and those with educated parents were more attuned to university rankings in general.However,when testing students’knowledge of rankings,elite university students performed better in knowing their domestic ranking,but worse when guessing their global ranking,while associations to parental education disappeared.This study,the first of its kind in terms of testing student knowledge,illustrates that the impact from university rankings are mitigated by local and individual characteristics.展开更多
Declining recognition of top university lists prompts China to look for new ways to evaluate its higher learning institutions Zhejiang University for the first time has overtaken Peking University and Tsinghua Univers...Declining recognition of top university lists prompts China to look for new ways to evaluate its higher learning institutions Zhejiang University for the first time has overtaken Peking University and Tsinghua University to rank No.1 on the latest list of Chinese college rankings.The rankings are an important part of the book Picking Your University展开更多
The notion of ranking was initiated into control theory by Glad a few yearsago. In this paper we mainly discuss one kind of rankings and study its invarianceproperties. This kind of rankings is closely related to the ...The notion of ranking was initiated into control theory by Glad a few yearsago. In this paper we mainly discuss one kind of rankings and study its invarianceproperties. This kind of rankings is closely related to the structure algorithm. The relationsbetween invariants of this kind of rankings and the invariants given by other studies, e.g.the invertibility indices of a system and so on, are examined. As applications of rankings,we deal with some well-known problems in "ranking technique". In this paper we mainlyintroduce a new method rather than solve new problems.展开更多
Recent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks(CNNs).However,these methods focus primarily on predicting generally perceived pref...Recent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks(CNNs).However,these methods focus primarily on predicting generally perceived preference of an image,making them usually have limited practicability,since each user may have completely different preferences for the same image.To address this problem,this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal taste.We achieve this in a coarse to fine manner,by joint regression and learning from pairwise rankings.Specifically,we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image pairs.We then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores,and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based regression.Next,we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge loss.Experiments demonstrate that our method can effectively learn personalized image aesthetic preferences,clearly outperforming state-of-the-art methods.Moreover,we show that the learned personalized image aesthetic benefits a wide variety of applications.展开更多
Currently,learning early warning mainly uses two methods,student classification and performance regression,both of which have some shortcomings.The granularity of student classification is not fine enough.The performa...Currently,learning early warning mainly uses two methods,student classification and performance regression,both of which have some shortcomings.The granularity of student classification is not fine enough.The performance regression gives an absolute score value,and it cannot directly show the position of a student in the class.To overcome the above shortcomings,we will focus on a rare learning early warning method-ranking prediction.We propose a dual-student performance comparison model(DSPCM)to judge the ranking relationship between a pair of students.Then,we build the model using data including class quiz scores and online behavior times and find that these two sets of features improve the Spearman correlation coefficient for the ranking prediction by 0.2986 and 0.0713,respectively.We also compare the process proposed with the method of first using a regression model to predict scores and then ranking students.The result shows that the Spearman correlation coefficient of the former is 0.1125 higher than that of the latter.This reflects the advantage of the DSPCM in ranking prediction.展开更多
文摘This study examines the relative efficiency of the top 20 Indian public colleges that offer MBAs. These colleges were chosen from a list provided by Careers 360, a magazine in India known for its university rankings. The purpose of this study was to evaluate the colleges on an efficiency basis rather than on a total score ranking scale as is the common practice of most publications that rank universities or programs. The ranking method used in this study is based on data envelopment analysis (DEA), a nonparametric procedure for evaluating entities based upon examining inputs in relation to outputs achieved. The rankings using DEA were somewhat different than those given by Careers 360. The results of the DEA analysis of this study rank the universities that are the most efficient at getting students the best salaries and return on investment (ROI) based on the inputs of diversity, work experience, and residency. The authors conclude, as previous studies have shown, that DEA analysis is a useful and non-biased method of comparing university programs.
文摘Purpose:The aim of our paper is to investigate the role of a mentor leading a research team in the overall scientific performance of an academic institution and the possible risks of their departure with a special attention to their publication output.Design/methodology/approach:By using SciVal subject area data,we composed a formula describing the level of vulnerability of any given university in the case of losing any of its leading mentors,identifying other risk factors by dividing their careers into separate stages.Findings:It turns out that the higher field-weighed citation impact is,the better position universities reach in the rankings by subject and the vulnerability of institutions highly depends on the mentors,especially in view of their contribution to the topic clusters.Research limitations:The analysis covers the publication output of leading researchers working at four Hungarian universities,the scope of the analysis is worth being extended.Practical implications:Our analysis has the potential to give an applicable systemic approach as well as a data collection scheme to university managements so as to formulate an inclusive and comprehensive research strategy involving the introduction of a reward system aimed at publications and further encouraging national and international research cooperation.Originality/value:The methodology and the principles of risk assessment laid down in our paper are not restricted to measuring the vulnerability level of a limited group of academic institutions,they can be appropriately used for investigating the role of mentors or leading researchers at every university across the globe.
文摘On Sep. 8,2009,Switzerland tops the overall ranking in The Global Competitiveness Report 2009-2010, released by the World Economic Forum ahead of its Annual Meeting of the New Champions 2009 in Dalian.
基金the National Natural Science Foundation of China(Grant No.71974150,71573085)。
文摘Purpose:Building on Leydesdorff,Bornmann,and Mingers(2019),we elaborate the differences between Tsinghua and Zhejiang University as an empirical example.We address the question of whether differences are statistically significant in the rankings of Chinese universities.We propose methods for measuring statistical significance among different universities within or among countries.Design/methodology/approach:Based on z-testing and overlapping confidence intervals,and using data about 205 Chinese universities included in the Leiden Rankings 2020,we argue that three main groups of Chinese research universities can be distinguished(low,middle,and high).Findings:When the sample of 205 Chinese universities is merged with the 197 US universities included in Leiden Rankings 2020,the results similarly indicate three main groups:low,middle,and high.Using this data(Leiden Rankings and Web of Science),the z-scores of the Chinese universities are significantly below those of the US universities albeit with some overlap.Research limitations:We show empirically that differences in ranking may be due to changes in the data,the models,or the modeling effects on the data.The scientometric groupings are not always stable when we use different methods.Practical implications:Differences among universities can be tested for their statistical significance.The statistics relativize the values of decimals in the rankings.One can operate with a scheme of low/middle/high in policy debates and leave the more fine-grained rankings of individual universities to operational management and local settings.Originality/value:In the discussion about the rankings of universities,the question of whether differences are statistically significant,has,in our opinion,insufficiently been addressed in research evaluations.
基金funded by CAPES (Coordinacao de Aperfeicoamento do Ensino) grant N. BEX 8354/13-8 awarded to Esteban Fernández Tuesta
文摘Purpose: Study how economic parameters affect positions in the Academic Ranking of World Universities' top 500 published by the Shanghai Jiao Tong University Graduate School of Education in countries/regions with listed higher education institutions. Design/methodology/approach: The methodology used capitalises on the multi-variate characteristics of the data analysed. The multi-colinearity problem posed is solved by running principal components prior to regression analysis, using both classical(OLS) and robust(Huber and Tukey) methods. Findings: Our results revealed that countries/regions with long ranking traditions are highly competitive. Findings also showed that some countries/regions such as Germany, United Kingdom, Canada, and Italy, had a larger number of universities in the top positions than predicted by the regression model. In contrast, for Japan, a country where social and economic performance is high, the number of ARWU universities projected by the model was much larger than the actual figure. In much the same vein, countries/regions that invest heavily in education, such as Japan and Denmark, had lower than expected results.Research limitations: Using data from only one ranking is a limitation of this study, but the methodology used could be useful to other global rankings. Practical implications: The results provide good insights for policy makers. They indicate the existence of a relationship between research output and the number of universities per million inhabitants. Countries/regions, which have historically prioritised higher education, exhibited highest values for indicators that compose the rankings methodology; furthermore,minimum increase in welfare indicators could exhibited significant rises in the presence of their universities on the rankings.Originality/value: This study is well defined and the result answers important questions about characteristics of countries/regions and their higher education system.
文摘This paper will discuss one topic in the current debate on higher education: How power is exercised between universities? How do colleges determine what the best college is? What are the differences in the excersise of power in the digital age? The authors analyze one of the mechanisms of relationship and contact between different universities: the rankings. They will discuss the practices that allow certain values and organizations they are becoming central nodes between universities and the influences of the information and communication technologies in the measurement mechanisms. The authors seek to show the rankings serve as mechanisms to exercise power among universities. These measurements become a tool and justification in competition between universities for resources such as funding, prestige, and student demand. The analysis is based on the University of Mexico, the authors use the ranking of the best universities in Latin America and the best universities in Mexico.
文摘Background: Cause-of-death rankings are often used for planning or evaluating health policy measures. In the European Union, some countries produce cause-of-death statistics by a manual coding of death certificates, while other countries use an automated coding system. The outcome of these two different methods in terms of the selected underlying cause of death for statistics may vary considerably. Therefore, this study explores the effect of coding method on the ranking of countries by major causes of death. Method: Age and sex standardized rates were extracted for 33 European (related) countries from the cause-of-death registry of the European Statistical Office (Eurostat). Wilcoxon’s rank sum test was applied to the ranking of countries by major causes of death. Results: Statistically significant differences due to coding method were identified for dementia, stroke and pneumonia. These differences could be explained by a different selection of dementia or pneumonia as underlying cause of death and by a different certification practice for stroke. Conclusion: Coding method should be taken into account when constructing or interpreting rankings of countries by cause of death.
文摘While academics and university administrators often criticize rankings,league tables have become important tools for student decision-making,especially in the Chinese sector.Yet,research has not fully explored how students in China have engaged with both global and local rankings,as most studies have focused on one setting or the other.Likewise,researchers have not tested students’knowledge of rankings,despite the intense focus on these actors by universities.Using a survey of over 900 students from Chinese universities,the author explored how knowledge of rankings varies in different student populations.Through multivariate analysis,it is found that students from elite institutions and those with educated parents were more attuned to university rankings in general.However,when testing students’knowledge of rankings,elite university students performed better in knowing their domestic ranking,but worse when guessing their global ranking,while associations to parental education disappeared.This study,the first of its kind in terms of testing student knowledge,illustrates that the impact from university rankings are mitigated by local and individual characteristics.
文摘Declining recognition of top university lists prompts China to look for new ways to evaluate its higher learning institutions Zhejiang University for the first time has overtaken Peking University and Tsinghua University to rank No.1 on the latest list of Chinese college rankings.The rankings are an important part of the book Picking Your University
基金This work was supported by NSFC grant No.68974008.
文摘The notion of ranking was initiated into control theory by Glad a few yearsago. In this paper we mainly discuss one kind of rankings and study its invarianceproperties. This kind of rankings is closely related to the structure algorithm. The relationsbetween invariants of this kind of rankings and the invariants given by other studies, e.g.the invertibility indices of a system and so on, are examined. As applications of rankings,we deal with some well-known problems in "ranking technique". In this paper we mainlyintroduce a new method rather than solve new problems.
基金supported partially by the National Key Research and Development Program of China(2018YFB1004903)National Natural Science Foundation of China(61802453,U1911401,U1811461)+1 种基金Fundamental Research Funds for the Central Universities(19lgpy216)Research Projects of Zhejiang Lab(2019KD0AB03).
文摘Recent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks(CNNs).However,these methods focus primarily on predicting generally perceived preference of an image,making them usually have limited practicability,since each user may have completely different preferences for the same image.To address this problem,this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal taste.We achieve this in a coarse to fine manner,by joint regression and learning from pairwise rankings.Specifically,we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image pairs.We then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores,and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based regression.Next,we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge loss.Experiments demonstrate that our method can effectively learn personalized image aesthetic preferences,clearly outperforming state-of-the-art methods.Moreover,we show that the learned personalized image aesthetic benefits a wide variety of applications.
文摘Currently,learning early warning mainly uses two methods,student classification and performance regression,both of which have some shortcomings.The granularity of student classification is not fine enough.The performance regression gives an absolute score value,and it cannot directly show the position of a student in the class.To overcome the above shortcomings,we will focus on a rare learning early warning method-ranking prediction.We propose a dual-student performance comparison model(DSPCM)to judge the ranking relationship between a pair of students.Then,we build the model using data including class quiz scores and online behavior times and find that these two sets of features improve the Spearman correlation coefficient for the ranking prediction by 0.2986 and 0.0713,respectively.We also compare the process proposed with the method of first using a regression model to predict scores and then ranking students.The result shows that the Spearman correlation coefficient of the former is 0.1125 higher than that of the latter.This reflects the advantage of the DSPCM in ranking prediction.