IMPROVING THE METHOD OF BUILDING AND OPTIMIZING LEARNING PATHS IN INTELLIGENT ADAPTIVE E-LEARNING SYSTEMS - DUTCHAK, MARIIA (Herald of Khmelnytskyi National University. Technical sciences, 2023)
The article describes an improved method for automated construction, optimization, and choice of the best adaptive learning path according to a certain criterion, which gives an ability to improve the quality of the process of learning in intelligent adaptive e-learning systems. This method allows to build an adaptive learning trajectory depending on the parameters of the student's model, the identified level of knowledge of key and intermediate concepts and academic requirements, and to compactly arrange more related learning units, which contributes to their better assimilation. The presented research contributes to the further development of methods for automated construction of educational material from educational blocks (modules). Also, the author propose an improved model of learning material and an improved method of setting semantic coefficients of connection between learning units, which allows to increase the correctness and optimality of automatically generated variants of learning trajectories.The approbation of the proposed methods has shown that the quality of students' learning has increased by 15%, that is, there was a transition of students from categories E and D to one of the categories A, B, C, which indicates a serious effect on the level of learning of students belonging to the researched categories. The arithmetic mean of the level of learning in the experimental group increased by 6.7% and 5.2% compared to the first and second control groups, respectively. Thus, the implementation and use of the results of this study can improve the quality of the automatically organized learning process, more rational use of labor and technical resources of web-based self-education systems, and can also be used by traditional educational institutions.
Keywords: artificial intelligence; intelligent e-learning systems; automated learning path construction; optimization of the adaptability function