Selection of criteria for a decision support system for an art university
https://doi.org/10.32517/0234-0453-2021-36-3-56-62
Abstract
Automation of decision support is proposed by including special criteria with the overall configuration of an automated decision support system for creative universities. A prototype of an automated decision support system for creative universities has been developed, which will allow assessing the achievements particularly talented students and identifying the needs in the learning process in order to help organize the educational process in accordance with identified capabilities. Use of a decision support system based on the Bayesian classifier is suggested to assess and evaluate factors contributing to the progress in teaching students particular techniques, and in perspective to assess the possible resources that will be required to make changes to the learning plan. The existing approaches to the assessment of students academic performance are analyzed. The list of specific performance indicators, which are important to be taken into account when assessing the achievements of students of creative specialties, is given. The system should contribute to the formation of the learning plan, taking into account the capabilities of both a group art workshop as a whole, and special needs of an individual to develop, if necessary an individual approach.
About the Author
S. Е. VecherskayaRussian Federation
Svetlana E. Vecherskaya, Candidate of Sciences (Chemistry), Docent, Associate Professor at the Department of Information Systems in Economics and Management
105005, Russia, Moscow, ul. Radio, 22
References
1. Pine J., Gilmore J. The experience economy. Boston, Harvard Business School Press, 1999.
2. Konkurs v tvorcheskie vuzy v 2020 godu v srednem sostavil okolo semi chelovek na mesto [The competition for creative universities in 2020 averaged about seven people per place]. (In Russian.) Available at: https://culture.gov.ru/ press/news/konkurs_v_tvorcheskie_vuzy_v_2020_godu_ sostavil_7_chelovek_na_mesto/
3. Jantan H., Hamdan A. R., Othman Z. A. Intelligent techniques for decision support system in human resource management // Decision Support Systems, Advances in. 2010. DOI: 10.5772/39401
4. Tan Tarn How, Shawn Goh Ze Song. The Art of Measuring the Arts. Available at: http://connectingaudiences. eu/resources/is-this-the-best-it-can-be?task=resource. downloadFile
5. D. Key Performance Indicators: Developing, implementing, and using Winning KPIs. Wiley, 2007. 256 p.
6. Riccio S. Talent management in higher education: Developing emerging leaders within the administration at private colleges and universities. Lincoln, 2010.
7. Top 10 talent management software systems in 2021 (Reviews). Available at: https://www.softwaretestinghelp. com/talent-management-software/
8. 1C:Upravlenie po tselyam i KPI [1C:Management by goals and KPI]. (In Russian.) Available at: https:// solutions.1c.ru/catalog/kpi/features
9. Upravlenie talantami (Talent Management) na platforme 1C:Predpriyatie 8 [Talent management on the 1C:Enterprise 8 platform]. (In Russian.) Available at: https:// www.topfactor.pro/blog/upravlenie-talantami-talentmanagement-na-platforme-1s-predpriyatie-8/
10. Davidsson P., Honig B. The role of social and human capital among nascent entrepreneurs. Journal of Business Venturing, 2003, vol. 18, is. 3, p. 301–331. DOI: 10.1016/ S0883-9026(02)00097-6
11. Kennedy C. KPI examples in higher education. SAP, 2010. Available at: https://blogs.sap.com/2010/05/03/kpiexamples-in-higher-education/ 12. Moetaz J. S. The impact of Key Performance Indicators (KPIs) on Talent Development. International Journal of Recent Research in Commerce Economics and Management (IJRRCEM), 2016, vol. 3, is. 4, p. 50–57.
12. Peterson E. T. The Big Book of Key Performance Indicators. Web Analytics Demystified, 2006. Available at: https://analyticsdemystified.com/wp-content/uploads/ 2019/01/The_Big_Book_of_Key_Performance_Indicators_ by_Eric_Peterson.pdf
13. Vecherskaya S. E. IT effect assessment for non-profit organization. International Journal of Recent Technology and Engineering (IJRTE), 2019, vol. 8, no. 4, p. 8854–8858. Available at: https://www.ijrte.org/wp-content/uploads/ papers/v8i4/D9479118419.pdf
14. Vecherskaya S. E. KPI v upravlenii ehffektivnost’yu nekommercheskoj organizatsii [KPI in the management of a non-profit organization effectiveness]. Vestnik RosNOU. Seriya: “Slozhnye sistemy: modeli, analiz, upravlenie” — RosNU Bulletin. Series: “Complex systems: models, analysis, management”, 2017, no. 4, p. 13–17. (In Russian.) Available at: http://vestnik-rosnou.ru/node/1895
15. Spider Strategies. Educational Services KPIs. Available at: https://www.spiderstrategies.com/kpi/ industry/educational-services/
16. Meier N. C. A measure of art talent. Psychological Monographs, 1928, vol. 39, is. 2, p.184–199. DOI: 10.1037/h0093346
17. Meier N. C., McCloy W. An instrument for the study of creative artistic intelligence. Psychological Monographs, 1936, vol. 48, is. 1, p. 164–172. DOI: 10.1037/h0093370
18. UCI machine learning repository. Available at: https://archive.ics.uci.edu/ml/index.php
19. Xue X., Croft B. W. Automatic query generation for patent search. CIKM ‘09: Proc. 18th ACM Conf. on Information and Knowledge Management. NYC, ACM, 2009, p. 2037–2040. DOI: 10.1145/1645953.1646295
Review
For citations:
Vecherskaya S.Е. Selection of criteria for a decision support system for an art university. Informatics and education. 2021;(3):56-62. https://doi.org/10.32517/0234-0453-2021-36-3-56-62