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Students Learning Results Prediction with Usage of Mixed Diagnostic Tests and 2-Simplex Prism

  • Anna YankovskayaEmail author
  • Yury Dementyev
  • Artem Yamshanov
  • Danil Lyapunov
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 794)

Abstract

Students learning results prediction is one of the “hottest” problems in modern learning process. We describe mathematical framework of students learning results assessment on the basis of mixed diagnostic tests (MDT). This framework includes cognitive graphic tools 2-simplex and 2-simplex prism, being the powerful means of data visualization for learning outcomes evaluation and efficient goal-setting. A new approach to the prediction of students’ learning results based on MDT and cognitive graphic tools is proposed. Students test results for the e-learning course “Selected Chapters of Electronics” and examples of their learning outcomes cognitive visualization are given. The proposed approach to prediction and cognitive visualization is discussed. Cross-platform software implementation specificity of cognitive graphic tools invariant to problem areas is described.

Keywords

Learning e-learning Prediction Mixed diagnostic test Cognitive tool Data visualization 2-simplex prism 

References

  1. 1.
    Brusilovsky, P., Knapp, J., Gamper, J.: Supporting teachers as content authors in intelligent educational systems. Int. J. Knowl. Learn. 2(3/4), 191–215 (2006).  https://doi.org/10.1504/IJKL.2006.010992CrossRefGoogle Scholar
  2. 2.
    Uskov, V., Uskov, A.: Innovative computer game technology curriculum. In: Proceedings of the 13th IASTED International Conference on Computers and Advanced Technology in Education, Lahaina, Maui, USA (2010).  https://doi.org/10.2316/P.2010.709-067
  3. 3.
    Yankovskaya, A.: Logical tests and means of cognitive graphics. LAP LAMBERT Academic Publishing (2011). (in Russian)Google Scholar
  4. 4.
    Yankovskaya, A., Yevtushenko, N.: Finite state machine (FSM)-based knowledge representation in a computer tutoring system. In: Kommers, P., et al. (eds.) New Media and Telematic Technologies for Education in Eastern European Countries, pp. 67–74. Twenty University Press, Enshede (1997)Google Scholar
  5. 5.
    Singer, F.M., Stoicescu, D.: Using blended learning as a tool to strengthen teaching competences. Procedia Comput. Sci. 3, 1527–1531 (2011).  https://doi.org/10.1016/j.procs.2011.01.043CrossRefGoogle Scholar
  6. 6.
    Bonk, C.J., Graham, Ch.R.: Handbook of Blended Learning: Global Perspectives, Local Designs, 624 p. Wiley, Hoboken (2006)Google Scholar
  7. 7.
    Stan, M.M.: The relationship of learning styles, learning behaviour and learning outcomes at the Romanian students. Procedia - Soc. Behav. Sci. 180, 1667–1672 (2015).  https://doi.org/10.1016/j.sbspro.2015.05.062CrossRefGoogle Scholar
  8. 8.
    Shaidullin, R.N., Safiullin, L.N., Gafurov, I.R., Safiullin, N.Z.: Blended learning: leading modern educational technologies. Procedia - Soc. Behav. Sci. 131, 105–110 (2014).  https://doi.org/10.1016/j.sbspro.2014.04.087CrossRefGoogle Scholar
  9. 9.
    Trends, E-Learning Market: Forecast 2014–2016 Report. A report by Docebo. http://www.docebo.com/landing/contactform/elearning-market-trends-and-forecast-2014-2016-docebo-report.pdf
  10. 10.
    Hattie, J., Yates, G.C.R.: Visible Learning and the Science of How We Learn, 368 p. Routledge, Abingdon (2013).  https://doi.org/10.4324/9781315885025CrossRefGoogle Scholar
  11. 11.
    Barana, A., Marchisio, M.: Ten good reasons to adopt an automated formative assessment model for learning and teaching mathematics and scientific disciplines. Procedia - Soc. Behav. Sci. 228, 608–613 (2016).  https://doi.org/10.1016/j.sbspro.2016.07.093CrossRefGoogle Scholar
  12. 12.
    Samigulina, G., Samigulina, Z.: Intelligent system of distance education of engineers, based on modern innovative technologies. Procedia - Soc. Behav. Sci. 228, 229–236 (2016).  https://doi.org/10.1016/j.sbspro.2016.07.034CrossRefGoogle Scholar
  13. 13.
    Harandi, S.R.: Effects of e-learning on students’ motivation. Procedia - Soc. Behav. Sci. 181, 423–430 (2015).  https://doi.org/10.1016/j.sbspro.2015.04.905CrossRefGoogle Scholar
  14. 14.
    Dzandu, M.D., Tang, Y.: Beneath a learning management system-understanding the human information interaction in information systems. Procedia Manuf. 3, 1946–1952 (2015).  https://doi.org/10.1016/j.promfg.2015.07.239CrossRefGoogle Scholar
  15. 15.
    Andreicheva, L., Latypov, R.: Design of E-learning system: M-learning component. Procedia - Soc. Behav. Sci. 191, 628–633 (2015).  https://doi.org/10.1016/j.sbspro.2015.04.580CrossRefGoogle Scholar
  16. 16.
    Urh, M., Vukovic, G., Jereb, E., et al.: The model for introduction of gamification into e-learning in higher education. Procedia - Soc. Behav. Sci. 197, 388–397 (2015).  https://doi.org/10.1016/j.sbspro.2015.07.154CrossRefGoogle Scholar
  17. 17.
    Yankovskaya, A.E.: Design of optimal mixed diagnostic test with reference to the problems of evolutionary computation. In: Proceedings of the First International Conference on Evolutionary Computation and Its Applications, Moscow, pp. 292–297 (1996)Google Scholar
  18. 18.
    Yankovskaya, A.E., Semenov, M.E.: Intelligent system for knowledge estimation on the base of mixed diagnostic tests and elements of fuzzy logic. In: Proceedings of IASTED International Conference on Technology for Education (TE 2011), Dallas, USA, pp. 108–113 (2011).  https://doi.org/10.2316/P.2011.754-001
  19. 19.
    Yankovskaya, A.E., Semenov, M.E.: Decision making in intelligent training-testing systems based on mixed diagnostic texts. Sci. Tech. Inf. Process. 40(6), 329–336 (2013).  https://doi.org/10.3103/s0147688213060087CrossRefGoogle Scholar
  20. 20.
    Yankovskaya, A.E., Semenov, M.E.: Foundation of the construction of mixed diagnostic tests in systems for quality control of education. In: Proceedings of 13th IASTED International Conference Computers and Advanced Technology in Education (CATE 2010), Maui, Hawaii, USA, pp. 142–145 (2010)Google Scholar
  21. 21.
    Yankovskaya, A.E: Mixed diagnostic tests are a new paradigm of construction of intelligent learning and training systems. In: Proceedings of the New Quality of Education in the New Conditions, Tomsk, Russia, vol. 1, pp. 195–203 (2011). (in Russian)Google Scholar
  22. 22.
    Yankovskaya, A.E., Semenov, M.E.: Application mixed diagnostic tests in blended education and training. In: Proceedings of the IASTED International Conference Web-based Education (WBE 2013), Innsbruck, Austria, pp. 935–939 (2013).  https://doi.org/10.2316/P.2013.792-037
  23. 23.
    Yankovskaya, A.E., Fuks, I.L., Dementyev, Y.N.: Mixed diagnostic tests in construction technology of the training and testing systems. Int. J. Eng. Innov. Technol. 3(5), 169–174 (2013)Google Scholar
  24. 24.
    Yankovskaya, A., Dementyev, Y., Lyapunov, D., Yamshanov, A.: Intelligent information technology in education. In: Information Technologies in Science, Management, Social Sphere and Medicine (2016).  https://doi.org/10.2991/itsmssm-16.2016.11
  25. 25.
    Yankovskaya, A., Razin, V.: Learning management system based on mixed diagnostic tests and semantic web technology. Tomsk State Univ. J. 2(35), 78–98 (2016).  https://doi.org/10.17223/19988605/35/9. (in Russian)CrossRefGoogle Scholar
  26. 26.
    Yankovskaya, A., Dementyev, Y., Lyapunov, D., Yamshanov A.: Design of individual learning trajectory based on mixed diagnostic tests and cognitive graphic tools. In: Proceedings of the 35th IASTED International Conference Modelling, Identification and Control (MIC 2016), Innsbruck, Austria, pp. 59–65 (2016)Google Scholar
  27. 27.
    Yankovskaya, A., Yamshanov, A., Krivdyuk, N.: Application of cognitive graphics tools in intelligent systems. IJEIT 3(7), 58–65 (2014)Google Scholar
  28. 28.
    Yankovskaya, A., Yamshanov, A.: Development of cross-platform cognitive tools invariant to problem areas and their integration into intelligent systems. Key Eng. Mater. 683, 609–616 (2016).  https://doi.org/10.4028/www.scientific.net/KEM.683.609CrossRefGoogle Scholar
  29. 29.
    Yankovskaya, A.E., Yamshanov, A.V.: Application of 2-simplex prism for researching and modelling of processes in different problem areas. In: Proceedings of the Seventh International Conference on Cognitive Science, Svetlogorsk, Russia, pp. 655–657 (2016). (in Russian)Google Scholar
  30. 30.
    Yankovskaya, A., Yamshanov, A.: Family of 2-simplex cognitive tools and their applications for decision-making and its justification. In: Computer Science & Information Technology (CS & IT), pp. 63–76 (2016).  https://doi.org/10.5121/csit.2016.60107
  31. 31.
    Yankovskaya, A., Levin, I., Fuks, I.: Assessment of teaching and learning by mixed diagnostic testing. In: Proceedings of the Frontiers in Mathematics and Science Education Research Conference (FISER-14), Famagusta, North Cyprus, pp. 86–93 (2014)Google Scholar
  32. 32.
    Yankovskaya, A.E., Semenov, M.E.: Construction of the mixed tests of the quality system of education. In: Proceedings of the International Scientific Conference (Modern IT & (e-) Training), Astrakhan, Russia, pp. 125–129 (2009). (in Russian)Google Scholar
  33. 33.
    Yankovskaya, A.E., Semenov, M.E., Yamshanov, A.V., Semenov, D.E.: Cognitive tools in learning and testing systems based on mixed diagnostic tests. Artif. Intell. Decis. Making 4, 51–61 (2015). (in Russian)Google Scholar
  34. 34.
    Zhuravlev, Yu.I., Gurevitch, I.B.: Pattern recognition and image analysis. In: Pospelov, D.A. (ed.) Artificial Intelligence in 3 Books. Book 2: Models and Methods: Reference Book, pp. 149–191. Radio and Comm., Moscow (1990). (in Russian)Google Scholar
  35. 35.
    Yankovskaya, A.E.: Transformation of features space in patterns space on the base of the logical-combinatorial methods and properties of some geometric figures. In: Proceedings of the International Conference Pattern Recognition and Image Analysis: New Information, Abstracts of the I All-Union Conference, Part II, Minsk, pp. 178–181 (1991). (in Russian)Google Scholar
  36. 36.
    Kondratenko, S.V., Yankovskaya, A.E.: System of visualization TRIANG for decision-making justification with cognitive graphics usage. In: Proceedings of the Third Conference on Artificial Intelligence, vol. I, Tver, pp. 152–155 (1992). (in Russian)Google Scholar
  37. 37.
    Demo for Developed Cognitive Tool. http://cogntool.tsuab.ru/demos/2-simplex-prediction/
  38. 38.
    Source Code of Cognitive Tools Prototype Visualization Library. https://github.com/zZLOiz/cogn-proto
  39. 39.
    Source Code of Cognitive Tools Visualization Library. https://github.com/zZLOiz/cogn-render

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Anna Yankovskaya
    • 1
    • 2
    • 3
    • 4
    Email author
  • Yury Dementyev
    • 4
  • Artem Yamshanov
    • 3
  • Danil Lyapunov
    • 3
    • 4
  1. 1.Tomsk State University of Architecture and BuildingTomskRussia
  2. 2.National Research Tomsk State UniversityTomskRussia
  3. 3.Tomsk State University of Control Systems and RadioelectronicsTomskRussia
  4. 4.National Research Tomsk Polytechnic UniversityTomskRussia

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