Visualization of Knowledge: Place in the Training Course and Methodical Foundations

Author(s):

DOI: https://doi.org/10.32782/2307-9770.2023.11.02.04

Paper Language: UKR

Download full-pdf

Abstract

The work examines the literature with an emphasis on educational applications of visualization. The need for them is indicated by students’ complaints about difficulties in perceiving concepts that exist at the micro level (DNA, genes) or those that describe invisible objects and properties (distributions of physical quantities). The effectiveness of user perception of visualization results is a key factor in the success of the method. High-quality visualization contributes to the intuitive understanding of the presented information and the acquisition and analysis of the necessary knowledge. Here, whenever possible, the quoted material is converted from textual form to visual. Eye tracking is considered a promising method of visualization research, as it contributes to the understanding of the origins of knowledge that we receive through visualization. Visualization of knowledge, including one on their own, is the most difficult for students to perceive. Therefore, they should learn to make the correct choice of the appropriate method based on the classification of types of knowledge. After that, they should move from understanding the available material (data, information, knowledge) through setting a cognitive problem to searching for and acquiring new knowledge. Thus, the purpose of this work is to identify the background and develop examples for creating a section of the training course on studying the features of knowledge visualization. The paper gives a scheme of the formed educational course on the study of visualization methods. The section devoted to the visualization of knowledge is considered in more detail. A demonstrative methodical example of such visualization is given. It is noted that to learn the correct construction of knowledge maps, students must first work with the knowledge they have a good command of. Therefore, students are given a familiar text on elementary mathematics on the topic of “Congruence of triangles”. In the first stage, based on text analysis, they build a scheme of knowledge. Next, it is analyzed, and it is concluded that this scheme is not complete. It does not answer all questions (there is no answer to the question “Why?”). But it serves to set a cognitive problem for finding an answer that will ensure the completeness of knowledge. The problem is formulated by the question “Why there is no fourth congruence test?” The answer to this question requires students to reformulate proof methods (into the form of a construction problem) to obtain an answer that is visualized in the second stage in the form of an extended knowledge scheme. In the available literature, unfortunately, the processes of knowledge analysis (including the textual presentation) with the subsequent selection of means and methods of visualization and subsequent formation of the corresponding map are considered very little. The results of experiments or scientific research of the authors are mostly given there. It seems to us that publications with elements of the methodology, which would show how the process of not only visualization of knowledge but also its preliminary analysis, setting of intermediate cognitive problems, etc., takes place, could be of particular value.

Keywords

means of visualization, cognitive visualization, visual thinking, graphic competence, visual competence, congruence of triangles

References

  1. Arnheim R. (2004) Art and Visual Perception. Berkeley: University of California Press. 2004. 518 pp.
  2. Arnheim R. (1986) New Essays on the Psychology of Art. Berkeley: University of California Press. 1986. 348 pp.
  3. Coggle. Online software for creating and sharing mindmaps and flowcharts. URL: https://coggle.it/ (accessed: 17.04.2023).
  4. Eppler M., Burkhard R. (2007) Visual Representations in Knowledge Management: Framework and Cases. // Journal of Knowledge Management. (2007). Vol. 11. No. 4. pp. 112–122. https://doi.org/10.1108/13673270710762756. (accessed: 17.04.2023).
  5. Gelb M. J. (2004) How to Think like Leonardo da Vinci: Seven Steps to Genius Everyday. Delacorte Press. 2004. NY: Delta Trade Paperback. 256 pp.
  6. IHMC CmapTools software. Empowers users to construct, navigate, share and criticize knowledge models represented as concept maps. Florida Institute for Human & Machine Cognition (IHMC). URL: https://www.ihmc.us/cmaptools/ (accessed: 17.04.2023).
  7. Keller T., Tergan S.-O. (2005) Visualizing Knowledge and Information: An Introduction. // Tergan S.-O., Keller T. (eds) Knowledge and Information Visualization. Lecture Notes in Computer Science, Vol. 3426. Berlin, Heidelberg: Springer. 2005. pp. 1–23. https://doi.org/10.1007/11510154_1. (accessed: 17.04.2023).
  8. Klerkx J., Verbert K., Duval E. (2014) Enhancing Learning with Visualization Techniques. In: Spector J., Merrill M., Elen J., Bishop M. (eds) Handbook of Research on Educational Communications and Technology. Springer, New York, NY. 2014. pp. 791–807. https://doi.org/10.1007/978-1-4614-3185-5_64
  9. Kudryavtsev D., Gavrilova T. (2017) From Anarchy to System: A Novel Classification of Visual Knowledge Codification Techniques. // Knowledge and Process Management. 2017. Vol. 24, No. 1, pp. 3–13. https://doi.org/10.1002/kpm.1509. (accessed: 17.04.2023).
  10. Kudryavtsev D., Gavrilova T., Leshcheva I. (2013) One Approach to the Classification of Business Knowledge Diagrams: Practical View. // Proc. of the 2013 Fed. Conf. on Computer Science and Information Systems (Sept. 8–11, 2013. Kraków, Poland). pp. 1247–1253. URL: https://annals-csis.org/Volume_1/pliks/40.pdf. (accessed: 17.04.2023).
  11. Lengler R., Eppler M. A Periodic Table of Visualization Methods. URL: http://www.visual-literacy.org/periodic_table/periodic_ table.html (accessed: 17.04.2023).
  12. Liew A. (2007) Understanding Data, Information, Knowledge and Their Inter-Relationships. // Journal of Knowledge Management Practice. 2007. Vol. 8, No. 2. URL: http://www.tlainc.com/articl134.htm (accessed: 17.05.2023).
  13. Mnguni L. E. (2014) The Theoretical Cognitive Process of Visualization for Science Education. SpringerPlus. 2014 Vol. 3, Art. 184. https://doi.org/10.1186/2193-1801-3-184. (accessed: 17.04.2023).
  14. Naps T., Cooper S., Koldehofe B., Leska C. et al. (2003) Evaluating the Educational Impact of Visualization (Report of the Working Group on Evaluating the Educational Impact of Visualization). // ACM SIGCSE Bulletin. 2003. Vol. 35. No. 4. pp. 124–136. https://doi.org/10.1145/960492.960540. (accessed: 17.04.2023)
  15. Padilla L. M., Creem-Regehr S. H., Hegarty M., Stefanucci J. K. (2018) Decision Making with Visualizations: a Cognitive Framework Across Disciplines. // Cognitive Research. 2018. Vol. 3, Art. 29. https://doi.org/10.1186/s41235-018-0120-9. (accessed: 17.04.2023).
  16. Polya G. (1973) How to Solve It. A New Aspect of Mathematical Method. Princeton: Princeton University Press, NJ, 1973. 276 pp.
  17. Raschke M., Blascheck T., Ertl T. (2014) Cognitive Ergonomics in Visualization. // Building Bridges: HCI, Visualization, and Non-formal Modeling. 2014. pp. 80–94. https://doi.org/10.1007/978-3-642-54894-9_7. (accessed: 17.04.2023)
  18. Roam D. (2009) The Back of the Napkin. Penguin Books Ltd. 2009. 308 p.
  19. Robertson G. G., Card S. K., Mackinlay J. D. (1989) The Cognitive Coprocessor Architecture for Interactive User Interfaces. // UIST '89: Proceedings of the 2nd annual ACM SIGGRAPH symposium on User interface software and technology. 1989. pp. 10–18. https://doi.org/10.1145/73660.73662 (accessed: 17.04.2023).
  20. Shneiderman B. (1999) Information Visualization Manifesto. // VC blog. 1999. URL: http://www.visualcomplexity.com/
  21. Sossinsky A. B. (2012) Geometries. Student Mathematical Library, Vol. 64. Providence, RI: American Mathematical Society. 2012. 322 pp.
  22. Török Z. G., Török Á. (2019) Cognitive Data Visualization – A New Field with a Long History. // Cognitive Infocommunications, Theory and Applications. 2019. pp. 49–77. https://doi.org/10.1007/978-3-319-95996-2_3. (accessed: 17.04.2023)
  23. Vavra K. L., Janjic-Watrich V., Loerke K., Phillips L. M., Norris S. P., Macnab J. S. (2011) Visualization in Science Education. // ASEJ. 2011. Vol. 41, No. 1, pp. 22–30. URL: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.409.1681& rep=rep1&type=pdf (accessed: 17.04.2023).
    &type=pdf (дата звернення: 17.04.2023).
  24. Veřmiřovský J. (2013) The Importance of Visualisation in Education. // CeON Repository. 2013. pp. 453–463. URL: https://depot.ceon.pl/handle/123456789/14480?locale-attribute=en (accessed: 28.04.2023).
  25. Wilke C. O. (2019) Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures. Sebastopol, CA: O'Reilly Media Inc. 2019. 389 p. URL: https://clauswilke.com/dataviz/ (accessed: 17.05.2023).
  26. Zelazny G. (2001) Say It With Charts. McGraw-Hill Education. 2001. 240 p.
  27. Bezuhlyi D. Vizualizatsiia yak suchasna stratehiia navchannia. // Fizyko-matematychna osvita. 2014. Iss. 1 (2). pp. 5-11. URL: http://nbuv.gov.ua/UJRN/fmo_2014_1_3.
  28. Bezuhlyi D. (2014) Pryiomy vizualnoho podannia navchalnoi informatsii. // Fizyko-matematychna osvita. 2014. Iss. 2(3). pp. 7–15. URL: http://nbuv.gov.ua/UJRN/fmo_2014_2_3. (accessed: 17.05.2023). [in Ukrainian]
  29. Biloshapka N. M. (2017) Vizualizatsiia yak providna ideia suchasnoho navchalnoho protsesu v umovakh informatyzatsii svitu. // Naukovi zapysky TsDU im. V. Vynnychenka. Seriia: Pedahohichni nauky. 2017. Iss. 159. pp. 167–173. URL: http://nbuv.gov.ua/UJRN/Nz_p_2017_159_31. (accessed: 15.05.2023). [in Ukrainian]
  30. Biloshapka N. M. (2018) Metodolohichni pidkhody do formuvannia u maibutnikh uchyteliv matematyky vmin vykorystovuvaty zasoby kompiuternoi vizualizatsii u profesiinii diialnosti. // Suchasni informatsiini tekhnolohii ta innovatsiini metodyky navchannia v pidhotovtsi fakhivtsiv: metodolohiia, teoriia, dosvid, problemy. 2018. Iss. 51. pp. 443-447. URL: http://nbuv.gov.ua/UJRN/mitimpt_2018_51_108. (accessed: 15.05.2023). [in Ukrainian]
  31. Biloshapka N. M. (2018) Spetskurs z formuvannia v maibutnikh uchyteliv matematyky vmin vizualizuvaty navchalnyi material. // Suchasni informatsiini tekhnolohii ta innovatsiini metodyky navchannia v pidhotovtsi fakhivtsiv: metodolohiia, teoriia, dosvid, problemy. 2018. Iss. 50. pp. 129-135. URL: http://nbuv.gov.ua/UJRN/mitimpt_2018_50_26. [in Ukrainian]
  32. Drushliak M. H. (2019) Slovnyk «vizualnoi» osvity: hrafichna kompetentnist i vizualna kompetentnist. // Fizyko-matematychna osvita. 2019. Iss. 3(21). pp. 59-65. https://doi.org/10.31110/2413-1571-2019-021-3-009. (accessed: 17.05.2023). [in Ukrainian]
  33. Kashynska O. Ye. (2018) Sposoby vizualizatsii navchalnoi informatsii pid chas vykladannia fakhovoi dystsypliny „Orhanizatsiia hotelnoho hospodarstva”. // II International Scientific and Practical Conference "Science of the XXI Century: Problems and Prospects of Researches". Warsaw, Poland: RS Global Sp. z O.O., Scientific Educational Center, January 2018. Vol. 5, No. 1(8), pp. 21–24. URL: https://journals.indexcopernicus.com/api/file/viewByFileId/415755.pdf. (accessed: 17.05.2023). [in Ukrainian]
  34. Kashynska O. Ye. (2018) Formy vizualizatsii navchalnoi informatsii v navchalno-naochnomu posibnyku dlia pidhotovky fakhivtsiv hotelno-restorannoi spravy. // Imidzh suchasnoho pedahoha. 2018. No. 2(179), pp. 52–56. https://doi.org/10.33272/2522-9729-2018-2(179)-52-56 (accessed: 15.05.2023). [in Ukrainian]
  35. Lipchevska I. L. (2021) Vizualizatsiia v osviti: suchasnyi pidkhid do vykorystannia naochnosti. // Svit dydaktyky: dydaktyka v suchasnomu sviti: zb. materialiv Mizhnar. nauk.-prakt. Internet-konferentsii, Kyiv, 2021. pp. 196–197. URL: http://lib.iitta.gov.ua/728087/1/Text.pdf. (accessed: 17.05.2023). [in Ukrainian]
  36. Lohvinenko V. H. (2018) Vykorystannia tekhnolohii infohrafiky dlia vizualizatsii navchalnoho kontentu. // Fizyko-matematychna osvita. 2018. Iss. 2(16). pp. 79-85. URL: http://nbuv.gov.ua/UJRN/fmo_2018_2_17 (accessed: 17.05.2023). [in Ukrainian]
  37. Melnyk K. M., Ulianych Yu. V. (2021) Instrumenty vizualizatsii strakhovoi diialnosti. // Ekonomika ta derzhava. 2021. No. 8. pp. 124–128. https://doi.org/10.32702/2306-6806.2021.8.124 (accessed: 15.05.2023). [in Ukrainian]
  38. Okunkova O. O. (2022) Vizualizatsiia danykh. Vid prostoho do skladnoho. // Vcheni zapysky Tavriiskoho natsionalnoho universytetu im. V. I. Vernadskoho. Seriia: Tekhnichni nauky. 2022. Vol. 33 (72). No. 3. pp. 61–66. https://doi.org/10.32838/2663-5941/2022.3/10 (accessed: 15.05.2023). [in Ukrainian]
    2663-5941/2022.3/10 (дата звернення: 15.05.2023).
  39. Semenikhina O. V. (2013) Z dosvidu stvorennia stendovykh materialiv .// Pedahohichni nauky: teoriia, istoriia, innovatsiini tekhnolohii. Sumy : Vyd-vo SumDPU imeni A.S. Makarenka, 2013. No. 2 (28). pp. 312-321. URL: http://repository.sspu.sumy.ua/handle/123456789/2942. (accessed: 15.05.2023). [in Ukrainian]
    123456789/2942. (дата звернення: 15.05.2023)
  40. Semenikhina O. V., Drushliak M. H. (2016) Vizualizatsiia znan yak aktualnyi zapyt informatsiinoho suspilstva do sfery osvity. // Materialy Mizhnar. nauk.-prakt. Internet-konf. «Vykorystannia innovatsiinykh tekhnolohii v protsesi pidhotovky fakhivtsiv», Vinnytskyi NTU. 2016. URL: http://ir.lib.vntu.edu.ua/bitstream/handle/123456789/28252/Semenikhina.pdf (accessed: 15.05.2023). [in Ukrainian]
  41. Semenikhina O. V., Drushliak M. H. (2017) Pryntsyp kohnityvnoi vizualizatsii i yoho vykorystannia u navchanni matematyky. // Fizyko-matematychna osvita. 2017. Iss. 3 (13). pp. 136–140. URL: http://nbuv.gov.ua/UJRN/fmo_2017_3_26. (accessed: 17.05.2023). [in Ukrainian]
  42. Tiutiunnyk A. V. (2020) Tekhnolohii vizualizatsii u svitovykh doslidzhenniakh. // Elektronne naukove fakhove vydannia «Vidkryte osvitnie e-seredovyshche suchasnoho universytetu». 2020. No. 9. pp. 161-168. https://doi.org/10.28925/2414-0325.2020.9.13. (accessed: 15.05.2023)