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Curriculum Design of Competitive Programming: a Contest-based Approach

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arxiv 2504.00533 v1 pith:TDG2VHNR submitted 2025-04-01 cs.CY

classification cs.CY
keywords programmingcompetitiveapproachcontest-basedcurriculumdesignstudentsalgorithmic
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Competitive programming (CP) has been increasingly integrated into computer science curricula worldwide due to its efficacy in enhancing students' algorithmic reasoning and problem-solving skills. However, existing CP curriculum designs predominantly employ a problem-based approach, lacking the critical dimension of time pressure of real competitive programming contests. Such constraints are prevalent not only in programming contests but also in various real-world scenarios, including technical interviews, software development sprints, and hackathons. To bridge this gap, we introduce a contest-based approach to curriculum design that explicitly incorporates realistic contest scenarios into formative assessments, simulating authentic competitive programming experiences. This paper details the design and implementation of such a course at Purdue University, structured to systematically develop students' observational skills, algorithmic techniques, and efficient coding and debugging practices. We outline a pedagogical framework comprising cooperative learning strategies, contest-based assessments, and supplemental activities to boost students' problem-solving capabilities.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Evaluating Performance Consistency in Competitive Programming: Educational Implications and Contest Design Insights

    cs.CY 2025-05 conditional novelty 6.0 of 10

    Across ten years of ICPC data, Codeforces online ratings predicted World Finals rankings (tau = 0.596) better than any superregional contest, and Northern Eurasia had the strongest regional-to-superregional consistenc...

  2. The Failure of Plagiarism Detection in Competitive Programming

    cs.CY 2025-05 conditional novelty 3.0 of 10

    Code similarity detectors miss obfuscated or AI-generated submissions in competitive programming, so the author recommends combining automated screening, manual review, and oral interviews.

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