Enhancing Algorithm Learning with Large Language Models: Design and Evaluation of AlgoLLM in Higher Education Practice

Algorithm learning remains challenging in computer science education due to its abstract logic, steep conceptual difficulty, and lack of personalized support in traditional settings. This study presents AlgoLLM, a modular instructional system built on large language models (LLMs) to support students through natural language explanations, code-level guidance, and feedback-based refinement. The system includes four core components: Knowledge Explainer, Exercise Generator, Code Assistant and Debugger, and Feedback Evaluator. A four-week case study was conducted with 60 undergraduate students, comparing a control group using textbooks and an experimental group using AlgoLLM. Paired and independent t-tests showed that the experimental group achieved significantly higher learning gains in post-tests (mean increase of 18.3 percent, Cohen's d = 0.94). Code accuracy and task efficiency also improved. Pearson correlation revealed a moderate relationship between LLM interaction frequency and learning gain. Questionnaire feedback indicated high perceived usefulness, clarity, and satisfaction. These results suggest that LLM-based systems like AlgoLLM can enhance algorithm comprehension and offer scalable, personalized support in technical education