发表机构
Purdue University（普渡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该案例提出逆向设计、精通导向的竞赛编程课程序列，先培养算法解题能力，后引入竞赛表现能力，并给出两层能力模型与课程模板。
AI 中文摘要
竞赛编程课程往往在阐明初学者应学习什么之前，就继承了编程竞赛的可见特征——限时回合、排名、罚时系统和主题列表。本课程设计案例认为，竞赛编程任务与竞赛表现是相关但不同的教育对象。入门课程应首先培养算法问题解决能力：识别问题结构、选择和组合范式、分解任务、分析复杂度、实现和调试解决方案，以及解释产生这些解决方案的推理过程。竞赛表现能力——快速分类、不确定性下的速度、战略性风险管理以及团队沟通——应仅在基础足够广泛之后引入。本文使用一种工程教育框架，通过逆向设计对齐内容、评估和教学法，重构了一个为期多年的三课程竞赛编程序列的重新设计。由此产生的CP1和CP2课程采用精通导向的在线裁判问题集、广度要求、抽样口头代码面试、协作式课堂推理、结对问题解决、渐隐支架和元认知反思。后续的CP3课程恢复限时个人和团队竞赛，作为真实的顶点环境。因此，竞争通过明确的教学剂量和排序决策进入该序列。本文是一个透明的设计案例，而非有效性试验。它报告了理论基础、工件、实施演变、设计权衡和分层评估议程，而不声称因果学习收益。它贡献了一个两层能力模型、一个对齐的课程架构、可实施就绪的评估协议，以及一个适用于竞赛编程教育者的改编课程设计模板。
英文摘要
Competitive programming courses often inherit the visible features of programming contests -- timed rounds, rankings, penalty systems, and topic lists -- before articulating what novices should learn. This curriculum design case argues that competitive programming tasks and competitive performance are related but distinct educational objects. Introductory courses should first develop algorithmic problem-solving competence: recognizing problem structure, selecting and combining paradigms, decomposing tasks, analyzing complexity, implementing and debugging solutions, and explaining the reasoning that produced them. Contest-performance competence -- rapid triage, speed under uncertainty, strategic risk management, and team communication -- should be introduced only after that foundation is sufficiently broad. The paper reconstructs a multi-year redesign of a three-course competitive programming sequence using an engineering-education framework that aligns content, assessment, and pedagogy through backward design. The resulting CP1 and CP2 courses use mastery-oriented online-judge problem sets, a breadth requirement, sampled oral code interviews, collaborative in-class reasoning, paired problem solving, fading scaffolds, and metacognitive reflection. A subsequent CP3 course restores timed individual and team contests as the authentic capstone environment. Competition thus enters the sequence through an explicit pedagogical dosage and sequencing decision. This article is a transparent design case rather than an efficacy trial. It reports the rationale, artifacts, implementation evolution, design tradeoffs, and a tiered evaluation agenda without claiming causal learning gains. It contributes a two-layer competence model, an aligned curriculum architecture, implementation-ready assessment protocols, and an adapted course-design template for competitive programming educators.
Comments24 pages, 1 figure, 9 tables. Submitted to the Journal of Competitive Learning
