Assessing and predicting the quality of research master’s theses: an application of scientometrics

Zheng Xie (), Yanwu Li and Zhemin Li
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Zheng Xie: National University of Defense Technology
Yanwu Li: National University of Defense Technology
Zhemin Li: National University of Defense Technology

Scientometrics, 2020, vol. 124, issue 2, No 7, 953-972

Abstract: Abstract The educational quality of research master’s degree can be in part reflected by the examiner score of the thesis. This study focuses on finding positive predictors of this score with the aim of developing assessment and prediction methods for the educational quality of postgraduates. This study is based on regression analysis of the characteristics extracted from publications and references involving 1038 research master’s theses written at three universities in China. The analysis indicates that for a thesis, the number and the integrated impact factor of its references in Science Citation Index Expanded (SCIE) journals are significantly positive predictors of having publications in such journals. Additionally, the number and the integrated impact factor of a thesis’ representative publications, defined as the publications authored by the master’s student as a first author or second author with tutors in lead position, in SCIE journals, are significantly positive predictors of its examiner score. Based on these predictors, a range of indicators is provided to assess thesis quality, to measure the contributions of disciplines to postgraduate education, to predict postgraduates’ research outcomes, and to provide benchmarks regarding the quality and quantity of their reading work.

Keywords: Data science applications in education; Higher education; Assessment methodologies (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11192-020-03489-3

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