Adaptive testing model with gradation of tasks by levels of assimilation

The article discusses the theoretical basis of automated testing, highlighting various approaches to conducting tests using special software tools and their impact on assessment results. The focus has shifted from creating a large bank of test items to individualizing testing for each student. Given that advancements in machine learning have led to significant changes in adaptive testing, there is an opportunity to apply modern methods to create tests that are tailored to the specific needs and knowledge levels of each student. The article also explores the process of selecting a suitable platform for implementing an adaptive model. This process includes defining key requirements, creating a job database structure, and developing a testing system. The article presents the structure of the model, with a description of all its components, and the adaptive testing algorithm, which is based on the grading of tasks by levels of learning, reflecting the depth of understanding, developed by educational theorists. This approach aims to improve the accuracy of assessing students' knowledge by individualizing the testing process. As an example, the article provides tasks based on this grading, which can be modified and applied to various academic disciplines, as well as a pseudo code example that can be adapted to the chosen programming language and task requirements. Thus, the implementation of the results of this study will have a positive impact on the improvement and optimization of the entire educational process.