Competitive programming: A teaching methodology analysis applied to first-year programming classes

Bandeira, Ian Nery; Machado, Thiago Veras; Dullens, Vitor F.; Canedo, Edna Dias

doi:10.1109/fie43999.2019.9028518

Cited by 4 publications

(5 citation statements)

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“…One of the most recent advances in the area of competitive programming source code analysis is represented by AlphaCode 3 [11]. With an extensive dataset, efficient transformer-based architectures and model sampling and filtering capabilities, the results are promising: AlphaCode ranked top 54.3% in Codeforces platform 4 after ten contests with over 5,000 participants each. From a different perspective, AlphaCode has achieved a rating of 1238, placing the system within the top 28,4% of users who participated in a contest in the last six months before running the experiments.…”

Section: Related Workmentioning

confidence: 99%

“…The main characteristics of the source codes from CP are its length (i.e., 25 up to 200 lines of code), its structure (i.e., the main function and few other functions) and the fact that one solution consists only of one file. We mention that CP has become a classical methodology for teaching undergraduate computer programming and algorithm design classes [4]. The main advantage of using online judges is that the student quickly and correctly gets strong evidence that the submitted solution solves the tackled problem.…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies [4] showed that CP represents an excellent ingredient within teaching methodology in first-year programming classes. CP was proven to have a positive impact in terms of effort level for doing the Data Structures and Algorithms discipline, improving programming knowledge and the final grade.…”

Section: Introductionmentioning

confidence: 99%

“…Infoarena, https://infoarena.ro/ (March 2023)3 Competitive programming with AlphaCode, https://www.deepmind.com/blog/competitive-programming-with-alphacode (March 2023)4 Codeforces platform, https://codeforces.com/ (March 2023)…”

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confidence: 99%

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Unsupervised Voting for Detecting the Algorithmic Solving Strategy in Competitive Programming Solutions

Stoica

Băbiceanu

Rebedea

et al. 2023

Preprint

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The problem of source code analysis using machine learning techniques has been gaining much attention recently as several powerful code embeddings methods have been created. Having available different embedding methods for source code opened the way for tackling many practical problems related to source code analysis. This paper addresses the issue of determining the number of distinct algorithmic strategies that may be found in a set of correct solutions for a competitive programming problem. We have investigated using five embedding methods with three algorithmic strategies in a data analysis pipeline that tackle the previously described issues on a newly created dataset consisting of 15 algorithmic problems. We propose an unsupervised algorithm that considers each embedding as a different dataset view. On each view, a hard clustering algorithm can be employed to split the correct solutions into K distinct algorithmic approaches, where K is a hyperparameter. In the ideal case, if all the algorithmic approaches in each view are determined correctly, a mapping could be done between each consecutive view such that each algorithmic approach from one view maps to a single corresponding algorithmic approach in the other. We are interested in determining the maximum subset of solutions that satisfy the assumption from above and use this subset as a base for a co-training procedure where an estimator employed on each view votes for the algorithmic approach of each solution which is not part of the subset. According to the results, the proposed unsupervised voting algorithm highly improves the baseline clustering approach. This improvement was observed across all problems in the dataset except one. Scale-up of the data analysis pipeline to datasets of thousands of problems may yield the ability to profoundly understand and learn about the innovative process of correctly designing and writing code in the context of competitive programming or even industry code.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Unsupervised Voting for Detecting the Algorithmic Solving Strategy in Competitive Programming Solutions

Stoica

Băbiceanu

Rebedea

et al. 2023

Preprint

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“…Competitive programming can be incorporated into teaching practice. For example, Zheng and Sarem [30, 31], and Wang et al [24] introduced competitive programming with online judge systems to the programming and algorithms courses. Coore and Fokum [7] used a competitive programming platform as a mechanism for auto‐grading assignments for an introductory course on algorithm design and analysis.…”

Section: Introductionmentioning

confidence: 99%

Competitive programming in computational thinking and problem solving education

Yuen

Liu

Leong

2023

Comp Applic In Engineering

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Building practical programming competency requires a long-lasting journey of discovery, trial and error, learning and improvement. This article presents essential findings of a case study of a Python programming contest with an automatic judgement system for Competitive Programming training extending the learning experiences for students in an introductory course, computational thinking and problem-solving. The benefits and challenges are discussed. Due to the coronavirus disease 2019 (COVID-19) epidemic, a hybrid model of the contest was adopted, that is, some students participated in the contest on-site, while others participated remotely. To alleviate human effort in judging the submissions, the DOMjudge platform, a web-based automatic judgement system, has been deployed as an online automatic judgement system and contest management in competitive programming. The implementation roadmap and framework were provided. The contest problems and contestants' performances were discussed. Not many junior contestants could solve at least one problem(s), and competitive computing training should be offered if the students are keen on open competitions. An empirical study was conducted to evaluate the student feedback after the contest. Preliminary results revealed that the contest offering the chance to stimulate student learning interests could enhance their independent learning, innovative thinking and problem-solving skills, and could thus lead to the overall benefits of the learning experiences, which further encourage them to participate in future contests to improve their learning and therefore enhance their employability. Employers often treasure student experiences in competitive programming events, like association for computing machinery programming contests, Google Code Jam or Microsoft Imagine Cup. Sharp vision requiring skills to tackle unseen problems within a short period is also instrumental to students planning for graduate school.

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