A Method for Building Adaptive Learning Paths for School Students Based on Academic Performance in Standardized School Exams

Abstract

we studied personalized learning in school exam preparation settings and developed a method for constructing adaptive learning paths based on student performance in standardized school exams. The approach targets individual learning needs and uses modern data-driven techniques to support instructional planning.
We collected student performance data and applied Item Response Theory (IRT) to model test responses. We estimated the probability of a correct answer using a three-parameter logistic model. We then inferred student ability levels using maximum likelihood estimation.
We grouped students according to their estimated ability using the K-means clustering method. The resulting clusters provided a basis for adapting learning trajectories to different performance levels.