Search results

Academic rankings are facing various issues, including the use of data sources that are not publicly verifiable, subjective parameters, a narrow focus on research productivity and regional biases and so forth. This research work is intended to enhance creditability of the ranking process by using the objective indicators based on publicly verifiable data sources.

The proposed ranking methodology – OpenRank – drives the objective indicators from two well-known publicly verifiable data repositories: the ArnetMiner and DBpedia.

The resultant academic ranking reflects common tendencies of the international academic rankings published by the Shanghai Ranking Consultancy (SRC), Quacquarelli Symonds (QS) and Times Higher Education (THE). Evaluation of the proposed methodology advocates its effectiveness and quick reproducibility with low cost of data collection.

Implementation of the OpenRank methodology faced the issue of availability of the quality data. In future, accuracy of the academic rankings can be improved further by employing more relevant public data sources like the Microsoft Academic Graph, millions of graduate's profiles available in the LinkedIn repositories and the bibliographic data maintained by Association for Computing Machinery and Scopus and so forth.

The suggested use of open data sources would offer new dimensions to evaluate academic performance of the higher education institutions (HEIs) and having comprehensive understanding of the catalyst factors in the higher education.

The research work highlighted the need of a purposely built, publicly verifiable electronic data source for performance evaluation of the global HEIs. Availability of such a global database would help in better academic planning, monitoring and analysis. Definitely, more transparent, reliable and less controversial academic rankings can be generated by employing the aspired data source.

We suggested a satisfying solution for improvement of the HEIs' ranking process by making the following contributions: (1) enhancing creditability of the ranking results by merely employing the objective performance indicators extracted from the publicly verifiable data sources, (2) developing an academic ranking methodology based on the objective indicators using two well-known data repositories, the DBpedia and ArnetMiner and (3) demonstrating effectiveness of the proposed ranking methodology on the real data sources.

The visualization of ranking information in global public policy is moving away from traditional “league table” formats and toward dashboards and interactive data displays. This paper explores the rhetoric underpinning the visualization of ranking information in such interactive formats, the purpose of which is to encourage country participation in reporting on the Sustainable Development Goals. The paper unpacks the strategies that the visualization experts adopt in the measurement of global poverty and wellbeing, focusing on a variety of interactive ranking visualizations produced by the OECD, the World Bank, the Gates Foundation and the ‘Our World in Data’ group at the University of Oxford. Building on visual and discourse analysis, the study details how the politically and ethically sensitive nature of global public policy, coupled with the pressures for “decolonizing” development, influence how rankings are visualized. The study makes two contributions to the literature on rankings. First, it details the move away from league table formats toward multivocal interactive layouts that seek to mitigate the competitive and potentially dysfunctional pressures of the display of “winners and losers.” Second, it theorizes ranking visualizations in global public policy as “alignment devices” that entice country buy-in and seek to align actors around common global agendas.

The purpose of this paper is to study the effects of the choice of database and data retrieval methods on the research performance of a number of selected Asian universities from 33 countries using two different indicators (publication volume and citation count) and three subject fields (energy, environment and materials) during the period 2005‐2009.

To determine the effect of the choice of database, Scopus and Web of Science databases were queried to retrieve the publications and citations of the top ten Asian universities in three subject fields. In ascertaining the effect of data retrieval methods, the authors proposed a new data retrieval method called Keyword‐based Data Retrieval (KDR), which uses relevant keywords identified by independent experts to retrieve publications and their citations of the top 30 Asian universities in the Environment field from the entire Scopus database. The results were then compared with those retrieved using the Conventional Data Retrieval (CDR) method.

The Asian university ranking order is strongly affected by the choice of database, indicator, and the data retrieval method used. The KDR method yields many more publications and citation counts than the CDR method, shows better understanding of the university ranking results, and retrieves publications and citations in source titles outside those classified by the database. Moreover the publications found by the KDR method have a multidisciplinary research focus.

The paper concludes that KDR is a more suitable methodology to retrieve data for measuring university research performance, particularly in an environment where universities are increasingly engaging in multidisciplinary research.

Learning to rank algorithms inherently faces many challenges. The most important challenges could be listed as high-dimensionality of the training data, the dynamic nature of Web information resources and lack of click-through data. High dimensionality of the training data affects effectiveness and efficiency of learning algorithms. Besides, most of learning to rank benchmark datasets do not include click-through data as a very rich source of information about the search behavior of users while dealing with the ranked lists of search results. To deal with these limitations, this paper aims to introduce a novel learning to rank algorithm by using a set of complex click-through features in a reinforcement learning (RL) model. These features are calculated from the existing click-through information in the data set or even from data sets without any explicit click-through information.

The proposed ranking algorithm (QRC-Rank) applies RL techniques on a set of calculated click-through features. QRC-Rank is as a two-steps process. In the first step, Transformation phase, a compact benchmark data set is created which contains a set of click-through features. These feature are calculated from the original click-through information available in the data set and constitute a compact representation of click-through information. To find most effective click-through feature, a number of scenarios are investigated. The second phase is Model-Generation, in which a RL model is built to rank the documents. This model is created by applying temporal difference learning methods such as Q-Learning and SARSA.

The proposed learning to rank method, QRC-rank, is evaluated on WCL2R and LETOR4.0 data sets. Experimental results demonstrate that QRC-Rank outperforms the state-of-the-art learning to rank methods such as SVMRank, RankBoost, ListNet and AdaRank based on the precision and normalized discount cumulative gain evaluation criteria. The use of the click-through features calculated from the training data set is a major contributor to the performance of the system.

In this paper, we have demonstrated the viability of the proposed features that provide a compact representation for the click through data in a learning to rank application. These compact click-through features are calculated from the original features of the learning to rank benchmark data set. In addition, a Markov Decision Process model is proposed for the learning to rank problem using RL, including the sets of states, actions, rewarding strategy and the transition function.

This study reviews the methods found in the literature for the ranking of authors, identifies the pros and cons of these methods, discusses and compares these methods. The purpose of this paper is to study is to find the challenges and future directions of ranking of academic objects, especially authors, for future researchers.

This study reviews the methods found in the literature for the ranking of authors, classifies them into subcategories by studying and analyzing their way of achieving the objectives, discusses and compares them. The data sets used in the literature and the evaluation measures applicable in the domain are also presented.

The survey identifies the challenges involved in the field of ranking of authors and future directions.

To the best of the knowledge, this is the first survey that studies the author ranking problem in detail and classifies them according to their key functionalities, features and way of achieving the objective according to the requirement of the problem.

The purpose of this study is to present an alternative to university ranking lists published in U.S. News & World Report, Times Higher Education, Academic Ranking of World Universities and Money Magazine. A strategy is proposed to mine a collection of university data obtained from Twitter and publicly available online academic sources to compute social media metrics that approximate typical academic rankings of US universities.

The Twitter application programming interface (API) is used to rank 264 universities using two easily collected measurements. The University Twitter Engagement (UTE) score is the total number of primary and secondary followers affiliated with the university. The authors mine other public data sources related to endowment funds, athletic expenditures and student enrollment to compute a ranking based on the endowment, expenditures and enrollment (EEE) score.

In rank-to-rank comparisons, the authors observed a significant, positive rank correlation (τ = 0.6018) between UTE and an aggregate reputation ranking, which indicates UTE could be a viable proxy for ranking atypical institutions normally excluded from traditional lists.

The UTE and EEE metrics offer distinct advantages because they can be calculated on-demand rather than relying on an annual publication and they promote diversity in the ranking lists, as any university with a Twitter account can be ranked by UTE and any university with online information about enrollment, expenditures and endowment can be given an EEE rank. The authors also propose a unique approach for discovering official university accounts by mining and correlating the profile information of Twitter friends.

The purpose of this paper is to analyze the similarity of intra-indicators used in research-focused international university rankings (Academic Ranking of World Universities (ARWU), NTU, University Ranking by Academic Performance (URAP), Quacquarelli Symonds (QS) and Round University Ranking (RUR)) over years, and show the effect of similar indicators on overall rankings for 2015. The research questions addressed in this study in accordance with these purposes are as follows: At what level are the intra-indicators used in international university rankings similar? Is it possible to group intra-indicators according to their similarities? What is the effect of similar intra-indicators on overall rankings?

Indicator-based scores of all universities in five research-focused international university rankings for all years they ranked form the data set of this study for the first and second research questions. The authors used a multidimensional scaling (MDS) and cosine similarity measure to analyze similarity of indicators and to answer these two research questions. Indicator-based scores and overall ranking scores for 2015 are used as data and Spearman correlation test is applied to answer the third research question.

Results of the analyses show that the intra-indicators used in ARWU, NTU and URAP are highly similar and that they can be grouped according to their similarities. The authors also examined the effect of similar indicators on 2015 overall ranking lists for these three rankings. NTU and URAP are affected least from the omitted similar indicators, which means it is possible for these two rankings to create very similar overall ranking lists to the existing overall ranking using fewer indicators.

CWTS, Mapping Scientific Excellence, Nature Index, and SCImago Institutions Rankings (until 2015) are not included in the scope of this paper, since they do not create overall ranking lists. Likewise, Times Higher Education, CWUR and US are not included because of not presenting indicator-based scores. Required data were not accessible for QS for 2010 and 2011. Moreover, although QS ranks more than 700 universities, only first 400 universities in 2012–2015 rankings were able to be analyzed. Although QS’s and RUR’s data were analyzed in this study, it was statistically not possible to reach any conclusion for these two rankings.

The results of this study may be considered mainly by ranking bodies, policy- and decision-makers. The ranking bodies may use the results to review the indicators they use, to decide on which indicators to use in their rankings, and to question if it is necessary to continue overall rankings. Policy- and decision-makers may also benefit from the results of this study by thinking of giving up using overall ranking results as an important input in their decisions and policies.

This study is the first to use a MDS and cosine similarity measure for revealing the similarity of indicators. Ranking data is skewed that require conducting nonparametric statistical analysis; therefore, MDS is used. The study covers all ranking years and all universities in the ranking lists, and is different from the similar studies in the literature that analyze data for shorter time intervals and top-ranked universities in the ranking lists. It can be said that the similarity of intra-indicators for URAP, NTU and RUR is analyzed for the first time in this study, based on the literature review.

The aim of the study is to present the implications for the financing and sustainability of enterprises based on a ranking methodology for categorical financial data.

Taking advantage of the optimal scaling properties of correspondence analysis (CA), a ranking‐clustering procedure is proposed. The proposed method was applied to categorical financial variables (i.e family farm income, gross profit, gross income, labour income and profitability) collected from a stratified random sampling of 80 Greek pig farms using a structured questionnaire.

The cluster analysis revealed three distinct groups of pig farms. Several recommendations for managerial practices and financial development resulted from this study. For the farms belonging to cluster C₁, that present low rankings on both criteria, a development planning process must be applied that will focus on organizational and management issues. For the farms belonging to cluster C₂, that present low rankings on the “composite income” criterion, policy measures have to be undertaken, aiming at exploiting their own production coefficients, reducing fixed costs and increasing productivity. Finally, for the farms in cluster C₃, that present high scores on both ranking criteria, it is recommended to take actions that will improve their competitiveness.

The findings are limited to five selected financial variables. Therefore, future studies in the same or other business fields would benefit from incorporating a greater number of variables.

The proposed methodological scheme could be useful to practitioners and academics, due to the fact that limited studies have dealt with this ranking problem, particularly in relation to the Greek agricultural business environment.

Due to the unintentional or even the intentional mistakes arising from a survey, the purpose of this paper is to present a data-driven method for detecting students’ friendship network based on their daily behaviour data. Based on the detected friendship network, this paper further aims to explore how the considered network effects (i.e. friend numbers (FNs), structural holes (SHs) and friendship homophily) influence students’ GPA ranking.

The authors collected the campus smart card data of 8,917 sophomores registered in one Chinese university during one academic year, uncovered the inner relationship between the daily behaviour data with the friendship to infer the friendship network among students, and further adopted the ordered probit regression model to test the relationship between network effects with GPA rankings by controlling several influencing variables.

The data-driven approach of detecting friendship network is demonstrated to be useful and the empirical analysis illustrates that the relationship between GPA ranking and FN presents an inverted “U-shape”, richness in SHs positively affects GPA ranking, and making more friends within the same department will benefit promoting GPA ranking.

The proposed approach can be regarded as a new information technology for detecting friendship network from the real behaviour data, which is potential to be widely used in many scopes. Moreover, the findings from the designed empirical analysis also shed light on how to improve GPA rankings from the angle of network effect and further guide how many friends should be made in order to achieve the highest GPA level, which contributes to the existing literature.

Modern Enterprise Web Application development can exploit third-party software components, both internal and external to the enterprise, that provide access to huge and valuable data sets, tested by millions of users and often available as Web application programming interfaces (APIs). In this context, the developers have to select the right data services and might rely, to this purpose, on advanced techniques, based on functional and non-functional data service descriptive features. This paper focuses on this selection task where data service selection may be difficult because the developer has no control on services, and source reputation could be only partially known.

The proposed framework and methodology are apt to provide advanced search and ranking techniques by considering: lightweight data service descriptions, in terms of (semantic) tags and technical aspects; previously developed aggregations of data services, to use in the selection process of a service the past experiences with the services when used in similar applications; social relationships between developers (social network) and their credibility evaluations. This paper also discusses some experimental results regarding the plan to expand other experiments to check how developers feel using the approach.

In this paper, a data service selection framework that extends and specializes an existing one for Web APIs selection is presented. The revised multi-layered model for data services is discussed and proper metrics relying on it, meant for supporting the selection of data services in a context of Web application design, are introduced. Model and metrics take into account the network of social relationships between developers, to exploit them for estimating the importance that a developer assigns to other developers’ experience.

This research, with respect to the state of the art, focuses attention on developers’ social networks in an enterprise context, integrating the developers’ credibility assessment and implementing the social network-based data service selection on top of a rich framework based on a multi-perspective model for data services.