Model of a Computer Adaptive Learning Testing System

Introduction. The creation of a conceptually new CAT model was based on the results of theoretical and experimental studies by Russian (V. A. Veksler, P. P. Dyachuk, V. S. Dobryak, T. M. Zubkova, A. R. Kochura, M. S. Mazorchuk, I. P. Peregudova, L. F. Tagirova, etc.) and foreign researchers (M. V. Brus, A. J. Martin, G. Lazendic, S. L. Zahrebelnyi). The analysis of these studies revealed three established approaches to adaptation used in most CAT systems (variation of the number, difficulty level, and time for completing test tasks). However, the problem of individual CAT customization remained unsolved, as traditionally such systems performed only one function - assessment. Meanwhile, it was the other two functions - diagnostic (identifying the reasons for failing to complete a test task) and corrective (selecting content elements) - that ensured the elimination of individual learning deficits, which would contribute to improving the effectiveness of interim assessment. Materials and Methods. The following methods were used in the conducted research: theoretical analysis of scientific literature, comparative analysis of CAT systems, systematization, and generalization of local experiment results. Results. The article discusses the structure of the presented model of the Computer Adaptive Learning Testing System (CAT), the need for which development is related to the advancement of theory and practice in creating adaptive learning systems and their demand for implementing personalized learning models. The specificity of the proposed model is determined by its structure, which, unlike common CAT models, includes two mandatory additional blocks - diagnostic and learning. Accordingly, filling these blocks required the development of special requirements for test tasks, which are represented by cases or sets of cases. The novelty of the research lies in the development of theory and practice of CAT development by incorporating diagnostic and learning functions to provide additional and more flexible adaptation. Discussion and сonclusions. The inclusion of diagnostic and learning blocks expands the scope of CAT applicability and their impact on improving the success rate of solving mathematical problems by students. The continuation of the research will be related to the use of artificial intelligence technologies to improve the developed Computer Adaptive Learning Testing System.