METHODOLOGY OF CONTINUOUS ASSESSMENT OF STUDENTS' RESIDUAL KNOWLEDGE USING THE EXAMPLE OF FLOW DISCIPLINES

Smolentseva Tatiana Evgenievna

Knowledge control and assessment are necessary components of the pedagogical educational space. Despite the variety of forms of certification, the organization of conducting and evaluating the results of the educational process using the example of stream disciplines has not been sufficiently studied. The article examines the contradictions in the organization of the educational process of flow disciplines and formulates the problem of the lack of the possibility of assessing residual knowledge and implementing feedback for timely changes in the structure of disciplines in the learning process. The proposed solution to the stated problem is a methodology for the continuous assessment of residual knowledge of stream disciplines. The essential components and structural content of the methodology of continuous assessment of residual knowledge in the process of studying streaming academic disciplines are substantiated with the experimental testing of the proposed methodology using the example of educational and scientific structural units of higher educational institutions. A distinctive feature of the methodology is the inclusion of a discipline classification model, technology for continuous assessment of residual knowledge of streaming academic disciplines, a bank of test assignments, a point rating system and a digital educational environment platform combining the proposed model and technology. The integration of artificial intelligence (AI) into the educational process is used to form answers in a free form, which in turn represents a paradigm shift towards an adaptive and responsive higher education system. The article considers an example of using AI to analyze the results of the technology of continuous assessment of residual knowledge (TCARK), using the example of flow disciplines.

Keywords:assessment of residual knowledge, bank of test tasks, streaming discipline, classification of streaming disciplines, point rating system, artificial intelligence