Application of Personalized and Adaptive Learning Models in Computer Science Classes

This research analyzes the application mechanisms, pedagogical significance, and upcoming challenges of personalized and adaptive learning models in computer science classes within the modern digital educational environment. Against the background of the inadequacy of traditional "one-size-fits-all" instructional methods for students with varying learning paces and prior knowledge, the role of adaptive technologies based on artificial intelligence, machine learning, and Learning Analytics is substantiated. The paper explores the positive impact of intelligent code editors, auto-graders, and dynamic adaptive testing on instructional quality, student motivation, and the development of algorithmic thinking. Simultaneously, strategic issues such as the digital divide, data privacy, and the necessity of implementing a blended learning framework are evaluated.