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University Rankings Game and its relation to GDP per capita and GDP growth

University Rankings Game and its relation to GDP per capita and GDP growth

Many university administrato rs abhor “this form of detailed numerical ordering of the institutions” (Monks and Ehrenberg 1999) but as Merisotis (2002) has noted, university rankings are here to stay. Though they are imperfect, university rankings provide information about the quality of higher education institutes (Usher and Savino 2007). In fact, students used rankings as a basis to decide which university to attend (Hazelkorn 2008, Dill and Soo 2004). Even though uni versity rankings measure universities’ research performance, the students use the rankings to help them decide which college to attend. It is also found that two- thirds of parents felt the rankings to be very useful in evaluating a college’s quality (Machung 1998). Therefore, in view of this phenomenon, many universities used rankings as part of their strategic plans for improvement and marketing strategy ( Usher and Savino 2007). For example, Cornell University took actions to improve its rankings that had no effect on the university’s academic quality. (Monks and Ehrenber g 1999)

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An examination of global university rankings as a new mechanism influencing mission differentiation: the
UK context

An examination of global university rankings as a new mechanism influencing mission differentiation: the UK context

Universities UK (the sector body for UK higher education) during the same time period was analysed for its use and appropriation of international benchmarks (Universities UK, 2010). Benchmarks used in this text to support the claim of world class status of UK HE were framed in terms of: productivity (‘the percentage of research papers produced is second only to the US’); in efficiency (‘the most efficient research system of all G8 countries”); in research exploitation (e.g. number of research patents, spin out companies); in quality and standards of teaching (e.g. only country in the world with an external examiner system at undergraduate level’). This text is oriented to ‘securing position as a leading knowledge economy in the world’. Global university rankings are not used in supporting claims, explanations or setting directions. It references OECD benchmarks of national spending on HE as a proportion of gross domestic product and proposes a level of public investment on a par with US, Canada and Australia in order to maintain the quality of the sector.

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The Relationship Between University Rankings And Outcomes Measurement

The Relationship Between University Rankings And Outcomes Measurement

edia ranking of universities provide a reference for students and parents in deciding which university to attend. Evidence on the importance of these rankings is mixed. McNeal (2004) argues that rankings do help students narrow the many choices that have available to them. However, Hossler and Foley (1995) find that university rankings have a small impact on the choice of university. Webster (1992) argues that although rankings published in magazines have their limitations, they provide more useful information than alternative sources of information such as accrediting agencies and most college guides. McDonough Antonio, Walpole and Perez (1998) examine who use these rankings and the types of freshman students most likely to find rankings useful in making a selection. Others argue that what the rankings really measure is the shape and preferences of the higher education market (Best in show, 2003). Some articles contend that rankings cause problems by introducing competition between universities and thwarting cooperation (Ehrenberg, 2003). Yet others argue that rankings have resulted in the creation of more elite schools than were present in earlier times (Samuelson, 2005). Certainly, an inappropriate “bad” ranking can have a significant negative economic impact on a university. Similarly an inappropriate “positive” rating can lead students to enroll in a suboptimal school. Although the evidence regarding the extent to which school rankings accurately reflect the quality of education are mixed, many schools have reallocated resources to actively enhance their rankings. Machung (1998) finds that colleges use rankings to attract students, to bring in alumni donations, to recruit faculty and administrators, and to attract potential donors.

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Close-up examination of discourses associated with global university rankings: counter-narratives in UK policy context

Close-up examination of discourses associated with global university rankings: counter-narratives in UK policy context

explicitly only in the Russell Group text. They were used in a range of causal semantic relations: of consequence (for example, China’s ascent in the rankings was attributed to increased investment in leading research institutions); of conditionality (of international research standing being reliant on supporting a small cadre of leading research institutions); in relations of contrast (supporting the statement that Russell Group graduates are the most highly sought after in the world, based on Times Higher Education rankings); and relations of purpose (for example, the object of ‘staying on top’ of the rankings). Global rankings were used to direct strategy and decisions of where universities must invest in order to compete with global competitors (through the formulation of a composite index that encapsulates global rankings, the ‘Global leaders index’). The three other texts did not refer to global university rankings but engaged in an associated discourse in their utilisation of the commonly associated concept of ‘world class’: re-contextualised as ‘world class skills’ (University Alliance), ‘world class efficiency in delivering social mobility’ (Million+) and a ‘world class sector’, defined by the 1994 Group as adherence high quality standards. Texts were interrogated by looking at instances of use of the term ‘international’ and its synonyms (‘world’, ‘global’) and the semantic relations that were set up. Two very different

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Global network centrality of university rankings

Global network centrality of university rankings

The interval regression analysis showed that certain aspects of air transport connectivity are closely correlated with ranking changes. While correlation cannot mean causality, we encourage researchers to use methods such as directed entropy to test the strength of causal arguments. This would involve sampling the probability space of airport and university rankings. Nonetheless, using the results presented in this paper, we can show that the most likely confounding variable (economic output of the city) was not significant in determining the fluctuations in airport hub factor nor the local university rankings. As such, we have a small degree of confidence in saying that, while there may be hidden factors related to the culture and fame of a location, they would require more detailed qualitative analysis.

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The contribution of university rankings to country's GDP per capita

The contribution of university rankings to country's GDP per capita

Many university administrators abhor “this form of detailed numerical ordering of the institutions” (Monks and Ehrenberg 1999) but as Merisotis (2002) has noted, university rankings are here to stay. Though they are imperfect, university rankings provide information about the quality of higher education institutes (Usher and Savino 2007). In fact, students used rankings as a basis to decide which university to attend (Hazelkorn 2008, Dill and Soo 2004). Even though university rankings measure ranking performance, the prestige of universities are still important basis of where students choose to attend college. It is also found that two-thirds of parents felt the rankings to be very u seful in evaluating a college’s quality (Machung 1998). Therefore, in view of this phenomenon, many universities used rankings as part of their strategic plans for improvement and marketing strategy ( Usher and Savino 2007). For example, Cornell University took actions to improve its rankings that had no effect on the university’s academic quality. (Monks and Ehrenberg 1999)

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Do university rankings matter for growth?

Do university rankings matter for growth?

contribute to having more universities ranked in the top 500 list. EUA has published a report in 2011, “Global University Rankings and Their Impact”, that analyzes the methodologies behind the main international university rankings, such as the Shanghai Ranking, QS Ranking, Times Ranking etc. Dowrick (2002) found that education and R & D promotes economic growth substantially. Howitt (2013) suggested that university research can boost economic growth. Wolff and Gittleman (1993) found that “university enrolment rates are positively associated with labor productivity growth.”

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ANGST OVER UNIVERSITY RANKINGS

ANGST OVER UNIVERSITY RANKINGS

Universities wait with trepidation each year when uni- versity rankings are published. These rankings are often viewed as indicating „excellence‟, providing prestige as well as attracting potential students and possible research or other funding. Several organizations produce compo- site indices which are used to rank universities worldwide. Two of the best known indices are the Times Higher Education (THE) and the Quacquarelli Symonds (QS) world university rankings [1,2]. They were originally part of one ranking system, but separated in 2010 with each producing its lists based on their own criteria, resulting in different universities taking the top spots.

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University rankings – a guide to choose a university?

University rankings – a guide to choose a university?

There are also magazines which prepare university rankings on a yearly basis. In United States of America, U.S. News and World Report compiles a ranking of American universities. The universities are categorized by mission and then data are gathered from the schools in order to compute up to 15 indicators, which are given different weights, based on the judgment of the proponents of the ranking. Business Week (for US) and Financial Times (for Europe) also prepare rankings for business schools.

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Do university rankings and accreditation really matter? Manufacturing 'legitimacy' in Singapore's higher education sector

Do university rankings and accreditation really matter? Manufacturing 'legitimacy' in Singapore's higher education sector

Murdoch University, based in Perth and founded in 1975, is one of 39 Australian public universities. It first became involved in the provision of overseas education through its Business School in Singapore in 1991 with the Singapore National Employers Federation (SNEF). This was followed shortly thereafter with a foray into Malaysia, with Kolej Damansara Utama (KDU), KDU Penang, Sibu and Kota Kinabalu. All of the initial Transnational Educational (TNE) offerings were undergraduate business degree programs. The program at SNEF was staffed with Murdoch Lecturers on a fly-in-fly-out basis for some 12 years, before changing to the current franchise- model basis. The Malaysian programs were staffed on a blended model with local Lecturers supported by Murdoch Lecturers with a couple of fly in visits per semester. In total, Murdoch has been involved in TNE for 22 years. The University’s TNE enterprise has grown from 585 students in 2005 (generating $1.2m in revenue) to 6700 students in 2015 (generating $14.5m in revenue in 2015).

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Performance of Brazilian higher education institutions in the innovation criteria in university rankings

Performance of Brazilian higher education institutions in the innovation criteria in university rankings

melhores universidades colocadas no ano de 2017. No entanto, quando os dois quesitos são analisados conjuntamente, a UFV se coloca na 5ª posição entre as mais inovadoras. Esse dado revela também a assimetria presente nas universidades na relação patentes/interação empresa, exemplo que pode ser observado na PUC Minas, terceira universidade que mais publica em parceria com empresas, mas, quando se trata de patentes depositadas, assume somente a 68ª posição, o que a coloca em 23º lugar no quesito inovação da RUF. Chama a atenção o fato de duas dessas universidades serem privadas(PUCRS e PUC-Rio) e uma comunitária (UCS). As universidades privadas não possuem tanta burocracia quanto as públicas para a celebração de contratos (Toledo, 2015), e desta forma, existe uma chance maior delas interagirem com o setor produtivo.Consequentemente, o conhecimento tecnológico, por ser gerado conjuntamente,possui maior valor tecnológico e maior facilidade de alcançara sociedade por meio da transferência de tecnologia, pois já foi criado atendendo às necessidades de um público externo (D’este e Perkmann, 2011). O THE é o único ranking internacional escolhido que também possui um indicador que mede a inovação nas universidades. O indicador de inovação utilizado por esta instituição considera os recursos oriundos da indústria e a classificação das IES brasileiras nesse quesito é retratada na Tabela 6. Esse indicador quantifica a interação universidade- empresa, tão defendida por diversos autores (Dalmarco et al., 2011, D'este e Perkmann, 2011, Baycan e Stough, 2013, Lawson, 2013). Para isso, o rankingTHE, diferente dos demais rankings discutidos neste artigo, se propõe a medir a efetividade da transferência de conhecimento entre universidade-empresa, por meio dos recursos transferidos

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Predictive Analysis of World University Rankings

Predictive Analysis of World University Rankings

From the dataset obtained, the correlation of the various factors, described in Part 3.1., with respect to world ranking was found. The influence each factor had on the university ranking was obtained from this correlation. It also gave the dependency of world ranking on each of these factors. Also, the dependence of various factors on each other was also found out using correlation. From this a large number of conclusions were derived. This proved useful in understanding the relationships between different factors.

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Is there a 'Mid-Rank Trap' for Universities?

Is there a 'Mid-Rank Trap' for Universities?

Generally, university rankings have two main functions. The first is to provide information about universities. To be more precise, university rankings have a much greater emphasis on research and publication, as compared to the other academic functions of these institutions such as teaching and services (Hazelkorn, 2008; Altbach et al., 2009; Kehm and Stansaker, 2009; Marginson, 2010; Azman et al., 2014). Hence, rankings illustrate more information about the performance and quality related to research activities rather than the overall performance and quality of a university. The second function is to provide comparative data that aim to create global standards of world class universities. However, due to the basis of the standards used, such comparison only sought to encourage universities to conform into a homogeneous model and at the same time penalise divergence and difference across universities (Marginson, 2010). Furthermore, it is pointed out that indicators used in the construction of ranking has an arbitrary ‘standard’ and may not reflect an objective measure of how a university performs in terms of quality and outcome (Venkatraman, 2010). Moreover, Figure 1: The Middle Income Trap

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CONSTRUCTING NATIONAL RANKING METHODOLOGY: DILEMMAS, CHOICES, AND DECISIONS

CONSTRUCTING NATIONAL RANKING METHODOLOGY: DILEMMAS, CHOICES, AND DECISIONS

Simultaneously with the above-mentioned trends the Russian universities are making concerted efforts to get top positions in global rankings. The Russian government set a goal for the national universities to reach top-positions among global universities by 2020. However, at the moment the results are modest: there are only two Russian universities in the Times Higher Education World University Rankings (THE) (The Times Higher Education, 2012, retrieved 5 November from URL: http://www.timeshighereducation.co.uk/world-university-rankings/) and 14 universities in Quacquarelli Symonds World University Rankings (QS Ranking) (QS Rankings, 2012, retrieved 1 November, 2012, from URL: http://www.topuniversities. com/university-rankings/world-university-rankings).Therefore, special systematic efforts are required to strengthen the national universities competitiveness in the global higher education area.

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Contributors to the American Sociological Review, 2010

Contributors to the American Sociological Review, 2010

or Top 10. This is because those doing the rankings tend to focus on any set of variables or criteria, which might lead to an institution being ranked high or low. Furthermore, there are certain universities such as Harvard Uni- versity, Yale University or Princeton University that tend to have any of their departments ranked very high by many different ranking studies or entities. Among United States colleges and universities, in the three social science disciplines of sociology, economics and political science, there are a set of institutions that have their departments ranked on top almost all of the time regardless of the variables examined or criteria of the study. These institutions are also among those that award the most number of doctorates in the social sciences. Among the criteria or variables examined in these ranking studies are prestige or reputation, number of scholarly articles published, especially in “top journals”, number of citations of the scholars in a department, size of faculty, num- ber of graduate students and number of doctorates awarded annually, number of new doctorate recipients imme- diately employed at top ranked universities or colleges, and endowment of an institution (Amir and Knauff, 2008; Nelson and Brammer, 2010; Burris, 2004; DiFuccia et al., 2007; “Economics: Ranked in 2009” 7 ; Eliason, 2008: pp. 51-52; Hinshaw and Siegfried, 1995; Kaba, 2009, 2012; Keith and Babchuk, 1998; Marwell, 2012; Pax- ton and Bollen, 2003; Oprisko, 2012; “Sociology: Ranked in 2009” 8 ; “Table 4. Top 20 doctorate-granting Ins- titutions,” 2012 9 ; “Top 100 QS World University Rankings for Sociology 2011,” 2011 10 ; Weakliem et al., 2012).

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Internationalization of Higher Education: Theoretical and Empirical Investigation of Its Influence on University Institution Rankings

Internationalization of Higher Education: Theoretical and Empirical Investigation of Its Influence on University Institution Rankings

source used in this evaluation is Scopus, the world’s largest abstract and citation database of research literature (World University Rankings: methodology, 2009). Whilst the Students-Faculty Ratio may not be a perfect measure of teaching quality, it is the most globally available and accessible measure of commitment to teaching. This indicator is made up from two datasets: Full Time Equivalent (FTE) students and Full Time Equivalent (FTE) faculty. Employ Review is based on a global online survey distributed to employers. Results are also based on three years of ‘latest response’ data. Similarly, geographical weightings are applied to ensure a fair representation for all the regions of the world. Internationalization is an undeniable component of today’s world class universities. It reflects the proportion of international students and faculty who are attracted to that institution. The international migration of students and faculty is a major trend in higher education due to globalization. Each of those groups represents 5% in the total score of this ranking. Consequently, the total weight given to internationalization in the THES ranking methodology is 10%.

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Striving for Higher University World Rankings: The Role of the Language Centre

Striving for Higher University World Rankings: The Role of the Language Centre

The University of Pardubice (UPa) is a relatively new Czech university (established for only 65 years). The institution educates about 10, 000 students and consists of seven faculties, the oldest being the Faculty of chemical technology. The prevailing focus of the institution rests in sciences. At such a place, naturally, the language instruction cyclically faces challenges and often is insufficiently incorporated into respective study programmes. The UPa LC has received positive feedback for its work from its students and universities’ top management. Faculties, however, sometimes view the LC activities as an unnecessary and troublesome element in their study programme structures. With a certain reservation, they support an idea of credited language courses but do not wish to “waste” too many credits on them, let alone to discuss funding the language instruction. Generally, allocation of credits to languages is a “political issue” requiring numerous negotiations. On such occasions, disputes occur about the appropriate role of the LC within the UPa – is the LC more a service or an academic teaching unit? The faculties generally appreciate LC work and its language teaching, but would love to perceive it as a service department providing language instruction and tailor-made translations with a background desire to economize on it as much as possible. The financial limits then do not provide proper room for further development of staff, courses and establishing research.

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The reporting of the university employability rankings : a critical review

The reporting of the university employability rankings : a critical review

There are clear differences in the emphasis given by different sources in their reporting on the tables. Publishers emphasise the authority of their tables, universities are more ambivalent, and other sources appear to echo content about the tables that may come from both publishers and universities. More complex discussion of the employability ranking and a more nuanced reflection of DLHE are not present in the reporting. Dominant discourses of marketisation and consumer choice, as well as the functional purpose of higher education to prepare graduates for the labour market are not contested. The disciplining effect of the rankings emerges in university responses and it is possible to see how hard it is for universities to resist the quantitative metrics especially as stakeholders such as students appear to embrace the rankings. The mechanism of ‘commensuration’ (Espeland and Sauder 2007) is played out in the reporting as the employability rankings appear to control how commentators (not just universities) think and speak about employability. Aspects of employability that may be of most interest to both current and prospective students are completely ignored by the metric used, and yet we witness student commentary accepting the ranking uncritically. Within this debate students are encouraged to attribute considerable responsibility to universities for providing good career prospects for them, rather than consider wider labour market and structural issues that affect career options for graduates.

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Provide Data Analysis Ideas for Small Travel Agencies

Provide Data Analysis Ideas for Small Travel Agencies

Although the factor of quarter was not considered in the factor analysis, the rankings of destinations and travel modes were taken to the rankings of Japan, Thailand, and Taiwan. For small trips, the following improvements can be made to the three-purpose products: 1. Multi-organizational products for Japanese tourists, regardless of the quarter, Japan is a popular destination choice. 2. It is possible to launch products targeting Thailand in the first, second and third quarters. 3. For the travel portfolio to Taiwan, you can focus on the first quarter. However, compared with Japan and Thailand, the proportion of Taiwan as a destination can be less. From Table 7, we can see that China, Taiwan in the second, third and fourth quarters are in a relatively backward ranking.

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An Exponential Model for Infinite Rankings

An Exponential Model for Infinite Rankings

This paper presents a statistical model for expressing preferences through rankings, when the num- ber of alternatives (items to rank) is large. A human ranker will then typically rank only the most preferred items, and may not even examine the whole set of items, or know how many they are. Similarly, a user presented with the ranked output of a search engine, will only consider the highest ranked items. We model such situations by introducing a stagewise ranking model that operates with finite ordered lists called top-t orderings over an infinite space of items. We give algorithms to estimate this model from data, and demonstrate that it has sufficient statistics, being thus an expo- nential family model with continuous and discrete parameters. We describe its conjugate prior and other statistical properties. Then, we extend the estimation problem to multimodal data by intro- ducing an Exponential-Blurring-Mean-Shift nonparametric clustering algorithm. The experiments highlight the properties of our model and demonstrate that infinite models over permutations can be simple, elegant and practical.

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