Contribution: This research illuminates information entropy's efficacy as a pivotal educational tool in programming, enabling the precise quantification of algorithmic complexity and student abstraction levels for solving problems. This approach can provide students quantitative, comparative insights into the differences between optimal and student implemented solution, and allowing educators to offer targeted feedback, thereby optimizing the learning and abstraction processes in algorithm design through deliberate practice. Background: Abstraction is considered one of the most important skills in problem solving. Many studies in programming have shown that higher abstraction capability can significantly simplify problems, reduce program complexity and improve efficiency. However, it is difficult to develop criteria to measure the level of abstraction, and there is still a lack of relevant systematic research. Research Questions: 1) How can students' abstraction ability in programming be effectively measured? and 2) How to develop programming education and training methods based on the measurement of abstraction ability? Methodology: Forty-six grade 10 students participated in the experiment, divided into two groups for programming training using information-entropy-based assessment and traditional learning methods. Their level of computational thinking, algorithmic efficiency improvements, and test scores were used to measure performance and to analyze the effectiveness of the training methods. Findings: Through empirical research, this article finds that information-entropy-based assessment can reflect the differences in problem solving among students possessing varying capabilities. Information entropy can be crucial for evaluating and improving students' abstraction performance and algorithm efficiency.