Artificial Intelligence and Decision Making
RUS  ENG    JOURNALS   PEOPLE   ORGANISATIONS   CONFERENCES   SEMINARS   VIDEO LIBRARY   PACKAGE AMSBIB  
General information
Latest issue
Archive
Guidelines for authors

Search papers
Search references

RSS
Latest issue
Current issues
Archive issues
What is RSS



Artificial Intelligence and Decision Making:
Year:
Volume:
Issue:
Page:
Find






Personal entry:
Login:
Password:
Save password
Enter
Forgotten password?
Register


Artificial Intelligence and Decision Making, 2019, Issue 3, Pages 70–82
DOI: https://doi.org/10.14357/20718594190308
(Mi iipr182)
 

Cognitive research and modeling

The fundamentals of development of cognitive graphic tools for evaluation of faculty performance

A. E. Yankovskayaab, D. Yu. Lyapunovcd

a Tomsk State University of Architecture and Building, Tomsk, Russia
b Tomsk State University, Tomsk, Russia
c Tomsk Polytechnic University, Tomsk, Russia
d Scientific Research Institute Automatics and Electromechanics, Tomsk, Russia
Abstract: The paper substantiates the relevance of evaluation of faculty performance that is due to the faculty transition to an effective contract. The analysis of various approaches to faculty performance evaluation is given. The advisability of improving the methods of faculty testing is reasoned. Their performance evaluation based on cognitive graphic tools (CGT) is substantiated. The outlined fundamentals of CGT construction allow transforming the space of features into the space of patterns. A theorem for CGT construction based on a 2-simplex prism is given. This makes it possible to ensure the distances between the patterns constant as well as the ratios between these distances. The CGT were developed to evaluate the faculty performance: of similar structural subdivisions (e.g. departments) in accordance with the structure of the university; on the career path of a faculty member depending on the moments of testing; of similar subdivisions by types of activity (educational, managerial, scientific); on priorities path depending on the moments of testing, for example, annually. In the future perspective we propose to create an intelligent testing system with a cognitive component. Based on the analysis of the space of features we suggest its expansion allowing to improve the effective contract system.
Keywords: intelligent testing system, matrix model of data and knowledge representation, regularities, cognitive graphic tools, 2-simplex prism, construction, intelligent instrumental software IMSLOG.
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: A. E. Yankovskaya, D. Yu. Lyapunov, “The fundamentals of development of cognitive graphic tools for evaluation of faculty performance”, Artificial Intelligence and Decision Making, 2019, no. 3, 70–82
Citation in format AMSBIB
\Bibitem{YanLya19}
\by A.~E.~Yankovskaya, D.~Yu.~Lyapunov
\paper The fundamentals of development of cognitive graphic tools for evaluation of faculty performance
\jour Artificial Intelligence and Decision Making
\yr 2019
\issue 3
\pages 70--82
\mathnet{http://mi.mathnet.ru/iipr182}
\crossref{https://doi.org/10.14357/20718594190308}
\elib{https://elibrary.ru/item.asp?id=41216285}
Linking options:
  • https://www.mathnet.ru/eng/iipr182
  • https://www.mathnet.ru/eng/iipr/y2019/i3/p70
  • Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    Artificial Intelligence and Decision Making
    Statistics & downloads:
    Abstract page:6
    Full-text PDF :6
    References:1
     
      Contact us:
     Terms of Use  Registration to the website  Logotypes © Steklov Mathematical Institute RAS, 2024