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Adaptive Testing Model as the Method of Quality Knowledge Control Individualizing

Shapovalova, Nonna; Rybalchenko, Olena; Dotsenko, Iryna; Bilashenko, Svitlana; Striuk, Andrii; Levan, Saitgareev

URI: http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/3174
https://doi.org/10.31812/123456789/3174

Date: 2019-06-30

Abstract:

The mission of the work is to develop and theorize the efficiency of application of the knowledge control system on the basis of adaptive testing technology, which combines the specifics of the professional and educational activity and the monitoring of the quality of training and the possibility of self-control of students, to develop a set of test assignments in the discipline “Artificial Intelligence Systems”. Object of research is a software tool for monitoring students’ knowledge in higher educational establishment. The subject of research is the development of software for an adaptive knowledge control system using machine learning device. Research goals: to develop a set of test case of different levels of complexity; to determine the structure, architecture and specificity of the application of the machine learning algorithm for the formation of a variable level of testing complexity for each student; develop appropriate software, guidelines and recommendations for adjusting and distributing issues by level of complexity. The result of the work is a complex of split-level application-oriented tasks for current and module control in the discipline “Artificial Intelligence Systems”, web-oriented software that allows you to quickly monitor the quality of students’ knowledge and is appropriate for use in online and mixed mode of training.

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)

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