The Open Cybernetics & Systemics Journal

2015, 9 : 1262-1269
Published online 2015 September 14. DOI: 10.2174/1874110X01509011262
Publisher ID: TOCSJ-9-1262

Features of Academic Misconducts on Master Dissertation from a Perspective of Fuzzy Comprehensive Evaluation

Yalou Liu , Miaomiao Jing and Junyong Han
Hebei United University, Tangshan, China.

ABSTRACT

As an important segment of postgraduate education, the quality of the dissertation, therefore, turns into a vital means to measure the normalization, scientificity and rigor of each college. Misconducts behavior among the master dissertation writing has long been highly concerned by the society and degree-conferring units. Hebei United University (HUU) involves 19 first level disciplines and 6 secondary disciplines for academic master-accredited fields as well as 7 categories for vocational master-accredited fields. There are over one thousand people apply for a master degree here for recent three years, which all these make HUU a key unit for postgraduate students training in Hebei province. Same as all other degree-conferring units, HUU entrusts assigned person to detect the replication and reference rate of each paper by utilizing the TMLC on the basis of China Academic Journal Network Publishing Database. The related staff found that the replication and reference rate can objectively show the basic characteristics of the plagiarism in master dissertation. This paper focus on 1234 master dissertation applying for master degrees in HUU in 2014 and do the statistical analysis of the initial replication and reference rate detection conclusion. The statistics complies according to the subject (natural science and social science), degree type (Full-time academic, Full-time vocational, professional education personnel and equivalent). The result divided into three effective intervals: replication ratio 0-19.9% (Qualified), 20-49.9% (to be modify), over 50% (disqualification). With the data we can build a mathematical model and conduct the relevant analysis and evaluation thus to obtain the essential features of academic misconducts among master dissertation.

Keywords:

Academic misconducts, features analysis, fuzzy comprehensive evaluation, grey relevancy.