Cognitive problems solving in the electronic learning environment: the influence of visual uncertainty

The educational content designing process leading to the active cognitive skills development is a challenge for developers of a modern innovative educational environment. Transfer of intellectual actions to the computer environment raises the issue of cognitive load. In the learning process, it is required to investigate the factors influencing the cognitive load. There is uncertainty in the multimedia environment due to the overabundance of information resources. Uncertainty arises not only from the external world, but also due to the internal state of a person, which constitutes certain types of cognitive load. The main goal of this paper is the effective presentation of educational content, in particular with the presence of visual uncertainty, and the adaptation of cognitive load, taking into account the students styles. This paper have proposed a method of automated intellectual analysis of individual characteristics of solving cognitive tasks by students in the presence of visual uncertainty in the information presentation using a new approach to measuring cognitive load. This method provides the following functions: diagnosing the students cognitive potential based on cognitive style; formation and assessment of the complex problems solving performance; assessment of the educational tasks cognitive load based on an algorithmic approach to measuring the amount of information entropy; forecasting and data visualization.

Keywords: educational environment, cognitive load, visual information uncertainty, reaction time, decision making


  1. A. R. Jensen. Clocking the mind: Mental chronometry and individual differences. Elsevier, 2006. 272 p.
  2. L. D. Sheppard, P. A. Vernon. Intelligence and speed of information-processing: A review of 50 years of research//Personality and individual differences. 2008. Vol. 44 (3). P. 535-551.
  3. A. R. Jensen. The importance of intraindividual variation in reaction time//Personality and individual Differences. 1992. Vol. 13 (8). P. 869-881.
  4. A. R. Jensen. Process differences and individual differences in some cognitivetasks//Intelligence. 1987. Vol. 11. № 2. P. 107-136.
  5. A. Welford. Choice reaction time: Basic concepts//Reaction times. 1980. P. 73-128.
  6. W. E. Hick. On the rate of gain of information//Quarterly Journal of experimental psychology. 1952. Vol. 4. № 1. P. 11-26.
  7. M. Brysbaert. Editorial QJEP classics revisited//Quarterly Journal of Experimental Psychology. 2016. Vol. 69. P. 1861-1863.
  8. R. W. Proctor, D. W. Schneider. Hick’s law for choice reaction time: A review//Quarterly Journal of Experimental Psychology. 2018. Vol. 71 (6). P. 1281-1299.
  9. W. Liu, J. Gori, O. Rioul et al. How Relevant is Hick's Law for HCI?//Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 2020. P. 1-11.
  10. J. Cao. Applying Hick’s law to Web design. Free example wireframes. 2010.
  11. J. Sauro. Five HCI laws for user experience design. Measuring U. 2013.
  12. R. Hyman. Stimulus information as a determinant of reaction time//Journal of Experimental Psychology. 1953. № 53. P. 188-196.
  13. R. K. Jamieson, D. J. K. Mewhort. Applying an exemplar model to the serial reaction-time task: Anticipating from experience//Quarterly Journal of Experimental Psychology. 2009. Vol. 62. № 9. P. 1757-1783.
  14. T. Wu, A. J. Dufford, L. J. Egan et al. Hick–Hyman law is mediated by the cognitive control network in the brain. Cerebral Cortex. 2018. Vol. 28. № 7. P. 2267-2282.
  15. T. H. Rammsayer, O. Pahud, S. J. Troche. Decomposing the functional relationship between speed of information processing in the Hick paradigm and mental ability: A fixedlinks modeling approach//Personality and individual differences. 2017. Vol. 118. P. 17-21.
  16. C. Shannon, W. Weaver. A mathematical theory of communication//The Bell system technical journal. 1948. Vol. 27. № 3. P. 379-423.
  17. G. E. Hawkins, S. D. Brown, M. Steyvers, E. J. Wagenmakers. An optimal adjustment procedure to minimize experiment time in decisions with multiple alternatives//Psychonomic bulletin & review. 2012. Vol. 19. № 2. P. 339-348.
  18. J. Wagemans, J. H. Elder, M. Kubovy et al. A century of Gestalt psychology in visual perception: I. Perceptual grouping and figure-ground organization//Psychological bulletin. 2012. Vol. 138. № 6. P. 1172-1217.
  19. J. Wagemans, J. Feldman, S. Gepshtein et al. A century of Gestalt psychology in visual perception: II. Conceptual and theoretical foundations//Psychological bulletin. 2012. Vol. 138. № 6. P. 1218.
  20. P. M. Fitts. The information capacity of the human motor system in controlling the amplitude of movement//Journal of Experimental Psychology. 1954. № 47. P. 381-391.
  21. S. Tak, A. Toet, J. van Erp. The perception of visual uncertainty representation by non-experts//IEEE transactions on visualization and computer graphics. 2013. № 20 (6). Р. 935-943.
  22. R. Finger, A. M. Bisantz. Utilizing graphical formats to convey uncertainty in a decision-making task//Theoretical Issues in Ergonomics Science. 2002. Vol. 3. Issue 1. P. 1-25.
  23. D. S. McNamara. Bringing cognitive science into education and back again: The value of interdisciplinary research//Cognitive Science. 2006. Vol. 30. P. 605-608.
  24. J. Sweller, J. J. G. van Merriлnboer, F. Paas. Cognitive architecture and instructional design//Educational Psychology Review. 1998. № 10 (3). P. 251-296.
  25. E. Pollock, P. Chandler, J. Sweller. Assimilating complex information //Learning and instruction. 2002. № 12. P. 61-86.
  26. J. Sweller. The redundancy principle in multimedia learning. The Cambridge handbook of multimedia learning. 2005. P. 159-168.
  27. J. Sweller, P. Ayres, S. Kalyuga. Cognitive load theory. 2011. Springer. doi:10.1007/978-1-4419-8126-4.
  28. J. Sweller, J. J. G. van Merriлnboer, F. Paas. Cognitive architecture and instructional design: 20 years later//Educational Psychology Review. 2019. № 31 (2). P. 261-292.
  29. M. T. H. Chi, R. Glaser, E. Rees. Expertise in problem solving/In R. Stenberg (Ed.). Advances in psychology of human intelligence. Hillsdale, NJ: Erlbaum, 1982. P. 7-75.
  30. E. J. Pollock. Bachelor of Economics (Social Sciences) (Hons). Thesis submitted to the University of New South Wales in fulfilment of the conditions for the degree of Doctor of Philosophy. School of Education University of New South Wales. Australia, 2000. 399 p.
  31. W. W. Wierwille, F. L. Eggemeier. Recommendations for mental workload measurement in a test and evaluation environment//Human Factor. 1993. Vol. 35. P. 263-281.
  32. J. Kagan. Reflection-impulsivity: The generality and dynamics of conceptual tempo//Journal of abnormal psychology. 1966. Vol. 71. № 1. P. 17-24.
  33. B. Gargallo. Basic variables in reflection-impulsivity: A training programme to increase reflectivity//European Journal of Psychology of Education. 1993. Vol. 8. № 2. P. 151-167.
  34. I. G. Skotnikova. Kognitivno-stilevye harakteristiki poznavatel'noj dejatel'nosti v zadachah s neopredelennost'ju//Obrazovanie lichnosti. 2018. № 2. S. 60-70.
    (In Russian.).
  35. I. G. Skotnikova. Psihofizicheskie harakteristiki zritel'nogo razlichenija i kognitivnyj stil'//Psihologicheskij zhurnal. 1990. T. 11. № 1. S. 84-94. (In Russian.)
  36. M. A. Holodnaja. Kognitivnye stili. O prirode individual'nogo uma. SPb.: Piter, 2004. 384 s. (In Russian.)
  37. E. E. Kotova, A. N. Pechnikov, A. S. Pisarev. Programmnyj kompleks diagnostiki kognitivnyh parametrov specialista (OntoMASTER-Diagnostika). Svid-vo o gos. registracii
    programmy dlja JeVM № 2009615001. 2009. (In Russian.)
  38. N. A. Nisa, B. Setiyadi, H. Huzairin. The Comparative Study between Reflectivity and Impulsivity Cognitive Style in Using Learning Strategy in Reading and Reading Comprehension//U-JET. 2018. Vol. 7. № 3. P. 1-62.
  39. S. L. Frank. Uncertainty reduction as a measure of cognitive load in sentence comprehension//Topics in cognitive science. 2013. Vol. 5. № 3. P. 475-494.
  40. W. Boulila. A top-down approach for semantic segmentation of big remote sensing images//Earth Science Informatics. 2019. Vol. 12. № 3. P. 295-306.
  41. R. Gonsales, R. Vuds, S. Jeddins. Cifrovaja obrabotka izobrazhenij v srede MatLab. M.: Tehnosfera, 2006. 616 s. (In Russian.)
  42. D. Sjelomon. Szhatie dannyh, izobrazhenij i zvuka. M.: Tehnosfera. 2004. 368 s. (In Russian.)
  43. D. Y. Tsai, Y. Lee, E. Matsuyama. Information entropy measure for evaluation of image quality//Journal of digital imaging. 2008. Vol. 21. № 3. P. 338-347.
  44. A. N. Kolmogorov. Teorija informacii i teorija algoritmov. M.: Nauka, 1987. 304 s. (In Russian.)
  45. R. Larsen. Mastering SVG. Packt Publishing. 2018. 312 p.
  46. M. Bakaev, E. Goltsova, V. Khvorostov, O. Razumnikova. Data Compression Algorithms in Analysis of UI Layouts Visual Complexity//International Andrei Ershov Memorial Conference on Perspectives of System Informatics. Springer. Cham. 2019. P. 167-184.
  47. P. Zhang, W. Zhou, L. Wu, H. Li. SOM: Semantic obviousness metric for image quality assessment//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. P. 2394-2402.
  48. Z. Chen, T. He. Learning based facial image compression with semantic fidelity metric//Neurocomputing. 2019. Vol. 338. P. 16-25.
  49. E. E. Kotova, A. S. Pisarev. Programma intellektual'nogo analiza produktivnosti reshenija kognitivnyh zadach v jelektronnoj srede (Jekspert-Analitik ART). Svidetel'stvo o gosudarstvennoj registracii programmy dlja JeVM № 2020667345 ot 22.12.2020. Zajavka № 2020665717 ot 03.12.2020. (In Russian.)
  50. H. Zenil, N. A. Kiani, J. Tegnйr. A review of graph and network complexity from an algorithmic information perspective//Entropy. 2018. Vol. 20. № 8. P. 1-15.
  51. T. J. McCabe. A complexity measure//IEEE Transactions on software Engineering. 1976. № P. 308-320.