Abstract
The article focuses on the need to maintain data from the digital educational footprint throughout the student’s life in the frame of institutional, corporate and independent learning. The grounds for decision-making such as the scope of the learning situation and the depth of analysis are identified. The possibilities of multidimensional analysis and cognitive computer graphics to enhance decision-making in LMS are considered. The advantages of Chernoff faces and their modifications are emphasized. It is suggested to form an image of a specialist in the form of an anthropomorphic figure and overlay individual attributes from the digital educational footprint of a student. The method of Unified Graphic Visualization of Activity (UGVA) is described. It allows forming images of specialists, comparing them with each other, and estimating the balance of educational material contribution. An example of the formation of a specific profile for the academic program “Informatics and Computer Science” and its visualization in UGVA notation is given. Images are formed for several students, including current learning achievements from individual digital educational footprints (competence aspect). The example is accompanied by recommendations for teachers of the academic department implementing the corresponding program in Siberian Federal University.
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Uglev, V. (2023). Unified Graphic Visualization of Activity (UGVA) Method. In: Krouska, A., Troussas, C., Caro, J. (eds) Novel & Intelligent Digital Systems: Proceedings of the 2nd International Conference (NiDS 2022). NiDS 2022. Lecture Notes in Networks and Systems, vol 556. Springer, Cham. https://doi.org/10.1007/978-3-031-17601-2_25
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