Abstract
The article discusses the problem concept maps are crucial tools for visualizing informatics knowledge in adaptive learning systems. In computer science education, concept maps are essential for visualizing complex knowledge structures and supporting adaptive learning. This study presents a learning path generation (LPG) algorithm designed to improve personalized learning in computer science. By analyzing students' concept maps, the LPG algorithm effectively distinguishes between groups of students based on their performance and generates customized learning paths. The LPG algorithm uses advanced computational methods to analyze concept maps and learning paths. Then, simplified learning paths are generated using a topological sorting algorithm. By using this method in computer science education, the following objectives can be achieved: Effectively distinguish between groups of students and generate customized learning paths based on their performance. Knowledge acquisition and increased student engagement. Learning path generation can be applied to any subject area.