JavaScript is disabled for your browser. Some features of this site may not work without it.
| dc.contributor.author | Соловйов, Володимир Миколайович | |
| dc.contributor.author | Моісеєнко, Наталя Володимирівна | |
| dc.contributor.author | Тарасова, Олена Юріївна | |
| dc.date.accessioned | 2021-01-04T22:01:15Z | |
| dc.date.available | 2021-01-04T22:01:15Z | |
| dc.date.issued | 2020-01 | |
| dc.identifier.uri | http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/4143 | |
| dc.identifier.uri | https://doi.org/10.31812/123456789/4143 | |
| dc.description | 1. Rutten, N., Van Joolingen, R., Van der Veen, J.T.: The learning effects of computer simulations in science education. Comput. Educ. 58(1), 136–153 (2012) 2. Lamb, R., Premo, J.: Computational modeling of teaching and learning through application of evolutionary algorithms. Computation 3, 427–443 (2015) 3. Mayor, J., Gomez, P.: Computational Models of Cognitive Processes: Proceedings of the 13th Neural Computation and Psychology Workshop (NCPW13). World Scientific Publishing Co., Singapore (2014) 4. Nikolis, G., Prigogine, I.: Exploring Complexity: An Introduction. W. H. Freeman and Company, New York (1989) 5. Kapitsa, S.P., Kurdyumov, S.P., Malinetsky, G.G.: Sinergetika i prognozyi buduschego (Synergetics and future forecasts). URSS, Moscow (2003) 6. Arnold, V.I.: Matematika i matematicheskoe obrazovanie v sovremennom mire (Math and math education in the modern world). Matematicheskoe obrazovanie 2, 109–112 (1997) 7. Harasim, L.: Shift happens: online education as a new paradigm in learning. Internet High. Educ. 3(1–2), 41–61 (2000) 8. Goh, W.P., Kwek, D., Hogan, D., Cheong, S.A.: Complex network analysis of teaching. EPJ Data Sci. (2014). https://doi.org/10.1140/epjds/s13688-014-0034-9 9. The Future of Jobs Report 2018. http://www3.weforum.org/docs/WEF_Future_of_Jobs_ 2018.pdf. Accessed 28 Nov 2019 10. Soloviev, V.M., Serdyuk, O.A., Danilchuk, G.B.: Modelyuvannya skladnih system (Complex systems modeling). Publisher Vovchok O.Yu, Cherkasy (2016) 11. Hausdorff, J., Zemany, L., Peng, C.-K., Goldberger, A.L.: Maturation of gait dynamics: stride-to-stride variability and its temporal organization in children. J. Appl. Physiol. 86(3), 1040–1047 (1999) 12. Delignieres, D., Torrex, K.: Fractal dynamics of human gait: a reassessment of the 1996 data of Hausdorff et al. J. Appl. Physiol. 106, 1272–1279 (2009) 13. Van Rooij, M.M.J.W., Nash, B.A., Rajaraman, S., Holden, J.G.: A fractal approach to dynamic inference and distribution analysis. Front. Physiol. 4(1), 1–16 (2013) 14. Ausloos, M.: Generalized Hurst exponent and multifractal function of original and translated texts mapped into frequency and length time series. Phys. Rev. E 86(3), 031108 (2012). https://doi.org/10.1103/PhysRevE.86.031108 15. Liu, X.F., Tse, C.K., Small, M.: Complex network structure of musical compositions: algorithmic generation of appealing music. Physica A 389, 126–132 (2010) 16. CompEngine. A self-organizing database of time-series data. http://www.comp-engine.org. Accessed 28 Nov 2019 17. Schmid, U., Ragni, M., Gonzalez, C., Funke, J.: The challenge of complexity for cognitive systems. Cogn. Syst. Res. 12, 211–218 (2011) 18. Bentz, C., Alikaniotis, D., Cysouw, M., Ferrer-i-Cancho, R.: The entropy of wordslearnability and expressivity across more than 1000 languages. Entropy 19(6), 275–279 (2017) 19. Hernandez-Gomez, C., Basurdo-Flores, R., Obregon-Quintana, B., Guzman-Vargas, L.: Evaluating the irregularity of natural languages. Entropy 19, 521–621 (2017). https://doi.org/ 10.3390/e19100521 20. Keshmiri, S., Sumioka, H., Yamazaki, R., Ishiguro, H.: Multiscale entropy quantifies the differential effect of the medium embodiment on older adults prefrontal cortex during the story comprehension: a comparative analysis. Entropy 21, 199–215 (2019) 21. Wu, M., Liao, L., Luo, X., et al.: Children development using gait signal dynamics parameters and ensemble learning algorithms. BioMed. Res. Int. 2016, 8 pages (2016). https://doi.org/10.1155/2016/9246280. Article ID 9246280 22. Jiang, Z.-Q., Xie, W.-J., Zhou, W.-X., Sornette, D.: Multifractal analysis of financial markets. Physics Reports (2018). arXiv:1805.04750v1 [q-fin.ST] 23. Wijnants, M.L: A review of theoretical perspectives in cognitive science on the presence of 1/f scaling in coordinated physiological and cognitive processes. J. Nonlinear Dyn. 2014, 17 pages (2014). https://doi.org/10.1155/2014/962043. Article ID 962043 24. Fan, C., Guo, J.-L., Zha, Y.-L.: Fractal analysis on human behaviors dynamics. Physica A: Stat. Mech. Appl. 391(24), 6617–6625 (2012) 25. Donner, R.V., et al.: Recurrence-based time series analysis by means of complex network methods. Int. J. Bifurc. Chaos 21(4), 1019–1046. https://doi.org/10.1142/ s0218127411029021 26. Webber, C.L., Ioana, C., Marwan, N. (eds.): Recurrence Plots and Their Quantifications: Expanding Horizons. SPP, vol. 180. Springer, Cham (2016). https://doi.org/10.1007/978-3- 319-29922-8 27. Wang, F., Liu, Q., Chen, E., Huang, Z.: Interpretable cognitive diagnosis with neural networks. arXiv:1908.08733v2 [cs.LG] 28. Albert, R., Barabasi, A.-L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74, 47–97 (2002) 29. Siew, C.S.Q., Wulff, D.U., Beckage, N., Kenett, Y.: Cognitive network science: a review of research on cognition through the lens of network representations, processes, and dynamics, 9 October 2018. https://doi.org/10.31234/osf.io/eu9tr 30. Lynn, C., Bassett, S.: The physics of brain network structure, function and control. Nat. Rev. Phys. 1, 318–332 (2019) 31. Chen, H., Liu, H.: How does language change as a lexical network? An investigation based on written Chinese word co-occurrence networks. PLOS One 1–22 (2018). https://doi.org/ 10.1371/journal.pone.0192545. Accessed 28 Nov 2019 32. Boccaletti, S., Bianconi, G., Criado, R., et al.: The structure and dynamics of multilayer networks. Phys. Rep. 544(1), 1–122 (2014) 33. Martincic-Ipsic, S., Margan, D., Mestrovic, A.: Multilayer networks of language: a unified framework for structural analysis of linguistic subsystems. Physica A 457, 117–128 (2016) 34. Torrisi, V., Sabato, M., Iacopo, I., Latora, V.: Based approach to understand correlations between interdisciplinary group dynamics and creative performance. In: Proceedings of the 21st International Conference on Engineering and Product Design Education, Glasgow, 12– 13 September 2019. https://doi.org/10.35199/epde2019.24 35. Jackson, E., Tiede, M., Riley, M., Whalen, D.: Recurrence quantification analysis of sentence-level speech kinematics. J. Speech Lang. Hear. Res. 59, 1315–1326 (2016) 36. Soloviev, V., Belinskij, A.: Methods of nonlinear dynamics and the construction of cryptocurrency crisis phenomena precursors. In: Ermolayev, V., et al. (eds.) Proceedings of the 14th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Volume II: Workshops, Kyiv, Ukraine, 14–17 May 2018. CEUR Workshop Proceedings, vol. 2014, pp. 116–127. http:// ceur-ws.org/Vol-2104/paper_175.pdf. Accessed 28 Nov 2019 37. Soloviev, V.N., Belinskiy, A.: Complex systems theory and crashes of cryptocurrency market. In: Ermolayev, V., Suárez-Figueroa, M.C., Yakovyna, V., Mayr, H.C., Nikitchenko, M., Spivakovsky, A. (eds.) ICTERI 2018. CCIS, vol. 1007, pp. 276–297. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-13929-2_14 38. Soloviev, V., Belinskij, A., Solovieva, V.: Entropy analysis of crisis phenomena for DJIA index. In: Ermolayev, V., et al. (eds.) Proceedings of the 15th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Volume II: Workshops, Kherson, Ukraine, 12–15 June 2019. CEUR Workshop Proceedings, vol. 2393, pp. 434–449. http://ceur-ws.org/Vol-2393/paper_375.pdf. Accessed 28 Nov 2019 39. Soloviev, V., Moiseienko, N., Tarasova, O.: Modeling of cognitive process using complexity theory methods. In: Ermolayev, V., et al. (eds.) Proceedings of the 15th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Volume II: Workshops, Kherson, Ukraine, 12–15 June 2019. CEUR Workshop Proceedings, vol. 2393, pp. 905–918. http://ceur-ws.org/Vol2393/paper_356.pdf. Accessed 28 Nov 2019 40. Lindley, C., Sennersten, C., Holopainen, J., IJsselsteijn, W.A., Niedenthal, S., Ravaja, N.: Workshop on the cognitive science of games and gameplay. In: CogSci 2006, 2671 (2006) 41. Rebetez, C., Bétrancourt, M.: Video game research in cognitive and educational sciences. Cogn. Brain Behav. 1(1), 131–142 (2007). ISSN 1224–8398 42. Chabris, C.: Six suggestions for research on games in cognitive science. Top. Cogn. Sci. 9, 497–509 (2017). https://doi.org/10.1111/tops.12267 43. Rafferty, A.N., Zaharia, M., Griffiths, T.L.: Optimally Designing Games for Cognitive Science Research. CogSci. (2012) 44. Kantelhardt, J.W., Zschiegner, S.A., Koscielny-Bunde, E., Havlin, S., Bunde, A., Stanley, H. E.: Mutifractal detrended fluctuation analysis of nonstationary time series. Physica A 316, 87–114 (2002) 45. Yang, Y., Yang, H.J.: Complex network-based time series analysis. Physica A 387, 1381– 1386 (2008) 46. Lacasa, L., Luque, B., Ballesteros, F., et al.: From time series to complex networks: the visibility graph. PNAS 105(13), 4972–4975 (2008) 47. Aldous, C.R.: Modelling the creative process and cycles of feedback. Creat. Educ. 8, 1860– 1877 (2017). https://doi.org/10.4236/ce.2017.812127 48. Kiv, A.E., Orischenko, V.G., Tavalika, L.D., Holmes, S.: Computer testing of operator’s creative thinking. Comput. Model. New Technol. 4(2), 107–109 (2000) 49. Kiv, A.E., Orischenko, V.G., Polozovskaya, I.A., Zakharchenko, I.G.: Computer modelling of the learning organization. In: Kidd, P.T., Karwowski, W. (eds.) Advances in Agil Manufacturing, 553–556. IOS Press, Amsterdam (1994) 50. Pullen, W.: Think Labyrinth! https://www.astrolog.org/labyrnth.htm. Accessed 28 Nov 2019 51. McClendon, M.S.: The complexity and difficulty of a maze. In: Sarhangi R., Jablan S. (eds.) Proceedings of Bridges 2001. Mathematical connections in art, music, and science, pp. 213– 220. Southwestern College Winfield, Kansas (2001) | |
| dc.description.abstract | The features of modeling of the cognitive component of social and humanitarian systems have been considered. An example of using entropy multiscale, multifractal, recurrence and network complexity measures has shown that these and other synergetic models and methods allow us to correctly describe the quantitative differences of cognitive systems. The cognitive process is proposed to be regarded as a separate implementation of an individual cognitive trajectory, which can be represented as a time series and to investigate its static and dynamic features by the methods of complexity theory. Prognostic possibilities of the complex systems theory will allow to correct the corresponding pedagogical technologies. It has been proposed to track and quantitatively describe the cognitive trajectory using specially transformed computer games which can be used to test the processual characteristics of thinking. | uk_UA |
| dc.language.iso | en | uk_UA |
| dc.publisher | Springer | uk_UA |
| dc.subject | cognitive systems | uk_UA |
| dc.subject | complexity | uk_UA |
| dc.subject | complex networks | uk_UA |
| dc.subject | entropy | uk_UA |
| dc.subject | recurrence plot | uk_UA |
| dc.subject | computer games | uk_UA |
| dc.subject | new pedagogical technologies | uk_UA |
| dc.title | Complexity theory and dynamic characteristics of cognitive processes | uk_UA |
| dc.type | Article | uk_UA |
