基于多种判分模式的Python编程考试研究与实践

Research and Practice on Python Programming Examinations Based on Multiple Scoring Modes

摘要: 本文旨在研究和实践基于多种判分模式的Python编程考试，探讨其对学生编程能力评估的有效性和重要性。通过对传统单一判分模式的局限性进行分析，引入结果判分、过程判分、代码质量判分等多种模式，并结合实际案例和实验数据，阐述这些判分模式的具体实施方法、优缺点及组合运用方式。同时利用AI辅助判分解决判分效率低下和评分标准不统一等问题，旨在为Python编程教学和评估提供更为全面、准确且科学的判分方案，以促进学生编程能力的综合提升和教育质量的优化。

Abstract: This paper aims to research and practice Python programming examinations based on multiple scoring models, exploring their effectiveness and significance in evaluating students’ programming abilities. Analyzing the limitations of the traditional single scoring model introduces various models such as result scoring, process scoring, and code quality scoring. Combined with practical cases and experimental data, it elaborates on the specific implementation methods, advantages, disadvantages, and combined application modes of these scoring models. At the same time, it uses AI-assisted scoring to solve problems such as low scoring efficiency and inconsistent scoring standards, aiming to provide a more comprehensive, accurate, and scientific scoring scheme for Python programming teaching and evaluation, so as to promote the comprehensive improvement of students’ programming abilities and the optimization of educational quality.