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)