Learning Path Analysis of Optimizing Educational Data Mining Based on Genetic Algorithm

With the extensive use of educational data mining in the field of learning analysis, identifying an effective approach to maximize learning route analysis has become a research priority. This study provides a genetic algorithm-based optimization framework for learning paths that simulates selection, crossover, and mutation operations in biological evolution. The framework first initializes a set of potential learning paths, which are then evaluated for fitness using input on learning outcomes. The iterative process of genetic algorithms preserves high fitness pathways while generating new ones. Throughout this process, the computer continuously selects and merges the current ideal path attributes, gradually optimizing the learning path. The experimental results suggest that this strategy may significantly improve learning efficiency and optimize learning experience, as well as having substantial practical application value for individualized learning path creation. Learning path analysis is an essential issue in educational settings since it includes generating individualized learning resources and activity sequences based on students’ learning behavior data to improve learning outcomes. Finding the best learning path in a vast amount of educational data is a hard task with many variables and limitations, and traditional analysis methods frequently fail to solve this challenge.