dc.description |
1. Amelkin, V.V.: Differential equations in Applications. Nauka, Moscow (1987)
2. Avanesov, V.S.: Difficulty of a Test and Testing Tasks. Management of School. 40 (1999)
3. Barbe, W.B., Milone M.N.: What we know about modality strengths. Educational
Leadership. February, 378–380 (1981).
4. Bondarenko, M.F., Semenets, V.V., Belous, N.V., Kutsevich, I.V., Belous, I.A.:
Assessment of Testing Tasks of Various Types and Their Complexity Determination.
Artificial Intelligence. 4, 322–329. http://www.foibg.com/ibs_isc/ibs-12/ibs-12-p07.pdf
(2009). Accessed 13 Feb 2019
5. Chelombitko, V.F., Mazhuga, M.O.: Use of Illustrative Materials for Creating Book and
Electronic Multimedia Publications. Bionics of Intelligence. 1(86), 112–115
http://openarchive.nure.ua/bitstream/document/4852/1/Chelombitko_VF_112-115.pdf
(2016). Accessed 10 Feb 2019
6. Domingos, P., Pazzani, M.: On the Optimality of the Simple Bayesian Classifier under
Zero-One Loss. Machine Learning. 29(2-3), 103–130 (1997).
doi:10.1023/A:1007413511361
7. Flach, P.A.: Machine Learning: The Art and Science of Algorithms that Make Sense of
Data. Cambridge University Press, Cambridge (2012)
8. Fleming, N.D., Mills C.: Not Another Inventory, Rather a Catalyst for Reflection. To
Improve the Academy. 11, 137–149 (1992)
9. Forcier, J., Bissex, P., Chun, W.: Python Web Development with Django. Pearson
Education, Boston (2008)
10. Garipova, Z.F.: Determination od the type of education material perception by Primary
Students. In: North of Russia: Development Strategies and Outlook, Surgut (2016)
11. GOST: Data Organization in Data processing Systems. Terms and Definitions. GOST
20886-85.
12. Hodovaniuk, T.L.: Idividyal Training at Higher School. NPU im. M. P. Drahomanova,
Kyiv (2010)
13. Holovaty, A., Kaplan-Moss, J.: The Definitive Guide to Django. Apress, New York (2009)
14. Koltsov, Yu.V., Dobrovolskaya, N.Yu.: Neural Network Models in Adaptive Computer
Learning. Educational Technology & Society. 5(2), 213–216 (2002)
15. Luo, F.L., Unbehauen, R.: Applied Neural Networks for Signal Processing. Cambridge
University Press, Cambridge (1998)
16. Malygin, A.A: Adaptive Testing of Students’ Learning Results at Distant Learning.
Abstract of Cand. Sci. (Pedagogy) Dissertation (2011)
17. Mazorchuk, M.S., Dobriak, V.S., Honcharova, K.A.: Model-Based Test Parameter
Assessment. In: International Scientific and Practical Conference “Problems and Outlooks
of IT-Industry Development”, pp. 123–125 (2010)
18. Perceptual Learning Style Preference Questionnaire
https://ru.scribd.com/document/258495684/Perceptual-Learning-Style-PreferenceQuestionnaire (1984). Accessed 30 Jan 2019 19. Petrova, M.Ye., Mintii, M.M., Semerikov, S.O., Volkova, N.P.: Development of adaptive
educational software on the topic of “Fractional Numbers” for students in grade 5. In: Kiv,
A.E., Semerikov, S.O., Soloviev, V.N., Striuk, A.M. (eds.) Proceedings of the 1
st Student
Workshop on Computer Science & Software Engineering (CS&SE@SW 2018), Kryvyi
Rih, Ukraine, November 30, 2018. CEUR Workshop Proceedings. 2292, 162–192.
http://ceur-ws.org/Vol-2292/paper19.pdf (2018). Accessed 21 Mar 2019
20. Principe, J.C., Euliano, N.R., Lefebvre, W.C.: Neural and Adaptive Systems. Fundamentals
Through Simulations. John Wiley & Sons, New York (2000)
21. Rash, G.: Probabilistic models for some intelligence and attainment tests. University of
Chicago Press, Chicago (1993)
22. Registry of the Information Intake style by A.R. Gregos.
http://psytests.org/cognitive/gregos.html. Accessed 28 Jan 2019
23. Richert, W., Coelho, L.P.: Building Machine Learning Systems with Python. Packt
Publishing, Birmingham (2016)
24. Saternos, C.: Client-Server Web Apps with JavaScript and Java: Rich, Scalable, and
RESTful. O’Reilly Media, Sebastopol (2014)
25. Semerikov, S.O., Teplytskyi, I.O., Yechkalo, Yu.V., Kiv, A.E.: Computer Simulation of
Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot. In: Kiv, A.E.,
Soloviev, V.N. (eds.) Proceedings of the 1st International Workshop on Augmented Reality
in Education (AREdu 2018), Kryvyi Rih, Ukraine, October 2, 2018. CEUR Workshop
Proceedings. 2257, 122–147. http://ceur-ws.org/Vol-2257/paper14.pdf (2018). Accessed
21 Mar 2019
26. Shapovalova, N.N., Rybalchenko, O.H. Kuropyatnyk, D.I.: Comparative Analysis of
Methods of Optimizing the Functional Machine Learning Models Quality. Bulletin of
Kryvyi Rih National University. 46, 104–112 (2018)
27. Striuk, A.: Design of Educational Objects of Augmented Reality. Transactions. Georgian
Technical University. Automated control systems. 2(26), 127–134.
http://gtu.ge/Journals/mas/Referat/N26_conf_unesco_2018_2_26.pdf (2018). Accessed 29
Oct 2018
28. Sviridenko, O.M.: Use of the Specific Character of Representative Systems of Students for
Successful Information Uptake. Bulletin of National Aviation University, Pedagogy,
Psychology. 1 (2009)
29. Syrovatskyi, O.V., Semerikov, S.O., Modlo, Ye.O., Yechkalo, Yu.V., Zelinska, S.O.:
Augmented reality software design for educational purposes. In: Kiv, A.E., Semerikov,
S.O., Soloviev, V.N., Striuk, A.M. (eds.) Proceedings of the 1
st Student Workshop on
Computer Science & Software Engineering (CS&SE@SW 2018), Kryvyi Rih, Ukraine,
November 30, 2018. CEUR Workshop Proceedings. 2292, 193–225. http://ceurws.org/Vol-2292/paper20.pdf (2018). Accessed 21 Mar 2019
30. Wulansari, Y.: The Use of Visual Auditory Kinesthetic (VAK) Learning Model to Improve
Students’ Reading Comprehension: Graduating Paper. State Institute for Islamic Studies,
Salatiga (2016)
31. Yefremtsev, S.: Diagnostics of Dominant Perceptive Modality. In: Fetiskin, N.P., Kozlov,
V.V., Manuilov, G.M.: Social and Psychological Diagnostics of development of a person
and small groups, pp. 237–238. https://psycabi.net/testy/289-test-audial-vizual-kinestetikdiagnostika-dominiruyushchej-pertseptivnoj-modalnosti-s-efremtseva (2002). Accessed
25 Jan 2019
32. Zahrebelnyi, S.L., Brus, M.V.: Adaptive Testing as a Means of Students’ Knowledge
Control at Technical Higher Educational Institutions. DHMA Bulleting. 1(22), 155–162
(2017) |
|