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Novice programmers can greatly improve their understanding of challenging programming concepts by studying worked examples that demonstrate the implementation of these concepts. Despite the extensive repositories of effective worked examples created by CS education experts, a key challenge remains: identifying the most relevant worked example for a given programming problem and the specific difficulties a student faces solving the problem. Previous studies have explored similar example recommendation approaches. Our research introduces a novel method by utilizing deep learning code representation models to generate code vectors, capturing both syntactic and semantic similarities among programming examples. Driven by the need to provide relevant and personalized examples to programming students, our approach emphasizes similarity assessment and clustering techniques to identify similar code problems, examples, and challenges. This method aims to deliver more accurate and contextually relevant recommendations based on individual learning needs. Providing tailored support to students in real-time facilitates better problem-solving strategies and enhances students' learning experiences, contributing to the advancement of programming education.