Abstract
The paper describes an approach to support methodological decision-making processes and explain them in the Intelligent Tutoring Systems (ITS), relying on cognitive visualization tools. The importance of XAI problems in the framework of automatic analysis of hypotheses concerning the interpretation of the learning situation is noted. In order to combine the structural and functional approaches within a single logic of analysis, various graphic notations are considered. Vertical (for different scopes of learning situation coverage) and horizontal (for different aspects of analysis) transitions between decision-making levels are shown for the proposed decision-making model. A combination of the method of Cognitive Maps of Knowledge Diagnosis (CMKD, structural aspect) and the method of Unified Graphic Visualization of Activity (UGVA, functional aspect) is taken as the basis. The steps of the process of superimposing data from the digital educational footprint on invariant graphic notations are described. The importance of ensuring the isomorphism of displaying the parameters of the learning situation when switching between maps and images is noted. Visualizations of CMKDs and anthropomorphic UGVA images are presented to illustrate examples from the real learning process. The advantages of the cross-cutting visual support for both the decision-making process and the synthesis of explanatory texts are shown. In conclusion, the limitations of the approach are pointed out, and the directions for further research are outlined.
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Uglev, V., Gavrilova, T. (2023). Cross-Cutting Visual Support of Decision Making for Forming Personalized Learning Spaces. 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_1
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