University ranking prediction system by analyzing influential global performance indicators

Tabassum, Anika; Hasan, Mahamudul; Ahmed, Shamsuddin; Tasmin, Rahnuma; Abdullah, Deen Md.; Musharrat, Tasnim

doi:10.1109/kst.2017.7886119

Cited by 11 publications

(5 citation statements)

References 12 publications

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“…To compare our results, we could not find works directly related to predicting ranking and scoring with the QS database. However, we highlighted the importance of using time series in studies like [25] and the work with THE data mentioned above. We also demonstrated the importance of correlating the indicators of the four most influential world rankings: QS, ARWU, THE, and URAP [26].…”

Section: Related Workmentioning

confidence: 97%

A data analytics approach for university competitiveness: the QS world university rankings

Estrada-Real

Cantú-Ortiz

2022

Int J Interact Des Manuf

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In recent years, higher education has felt pressured to prepare its graduates for the highly competitive international market due to globalization. Thus, many institutions have turned to position themselves well in university rankings as a way to attract the best academic and student talent from all over the world. Our work presents a predictive model for measuring university performance in the QS world university rankings (QS-WUR). We used a ten-year dataset to build models with statistical and machine learning algorithms contained in the library Caret of the RStudio software tool, to forecast global university position in QS-WUR. With these tools, we designed a methodology to predict the university partners' Final Scores based on their historical performance, achieving errors in the range of one or two points out of 100. The modelling may be a useful aid for university officers to develop strategies for improving institutional processes to attract the best students, faculty, and funding, enhance international collaboration and outlook, and foster international university prestige.

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Section: Related Workmentioning

confidence: 97%

A data analytics approach for university competitiveness: the QS world university rankings

Estrada-Real

Cantú-Ortiz

2022

Int J Interact Des Manuf

View full text Add to dashboard Cite

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“…Frenken et al [41] used a regression analysis to assess universities' research performance and the influence of structural variables (e.g., location) on the performance differences among universities. Tabassum et al [42] specifically studied the correlation of university ranking indicators focusing on VOLUME 4, 2016 identifying the influential features using an outlier detection approach. Mikryukov et al [43] utilized Principal Component Analysis (PCA) to identify the significant factors, latent variables, and the correlations between the latent and the basic variables.…”

Section: B Competitive Rankingmentioning

confidence: 99%

Local Explanations of Global Rankings: Insights for Competitive Rankings

Anahideh

Mohabbati-Kalejahi

2022

IEEE Access

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Explaining complex algorithms and models has recently received growing attention in various domains to support informed decisions. Ranking functions are widely used for almost every form of human activity to enable effective decision-making processes. Hence, explaining ranking indicators and their importance are essential properties to enhance performance. Local explanation techniques have recently become a prominent way to interpret individual predictions of machine learning models. However, there has been limited investigation into explaining competitive rankings. This work proposes a hierarchical ranking explanation framework to capture local explanations for competitive rankings by defining a proper neighborhood construction approach. We explore various explanation techniques to identify the local contribution of ranking indicators based on the position of an instance in the ranking as well as the size of the neighborhood around the instance of interest. We evaluate the generated explanations for the Times Higher Education university ranking dataset as a benchmark of competitive ranking. The results reveal insights for a wide range of instances in the ranking list and indicate the importance of local explanations for competitive rankings.

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“…Tabassum described the system of developing a global university ranking prediction system by examining all the university performance indicators. 4 This study has used fiveyears training data set for analysis of influential performance indicators. With the help of the proposed algorithm, several calibrated performance indicators are examined, and the total rank score is generated based on the specific weighting of each performance indicator.…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of Expanding Window from Three Years to Five Years for Research Performance Parameter in India Rankings

Joorel

Solanki

Kumar

et al. 2021

DESIDOC Jl. Lib. Info. Technol.

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The India Rankings 2020 was announced the 5th annual edition of ranking for higher educational institutions in India based on National Institutional Ranking Framework (NIRF). The impact of NIRF among institutions is progressive on their output, outcomes, and outreach to the society. The government of India is also concentrating on several policy decisions in the education sector based on the results of India Rankings. This paper is focused on the study of Research Performance (RP) parameter on India Rankings while expanding five-years window instead of three-years window. Moreover, the study is also focused on Pearson correlation between three-years research performance score and five-years RP score and weighted score of institutions. It is attempted to derive the Spearman rank correlation between RP rank and overall rank based on all parameters score.

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University ranking prediction system by analyzing influential global performance indicators

Cited by 11 publications

References 12 publications

A data analytics approach for university competitiveness: the QS world university rankings

A data analytics approach for university competitiveness: the QS world university rankings

Local Explanations of Global Rankings: Insights for Competitive Rankings

Impact of Expanding Window from Three Years to Five Years for Research Performance Parameter in India Rankings

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