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All Journal Journal of Computer Networks, Architecture and High Performance Computing
Fauzan Fadlullah
Universitas Negeri Jakarta, Indonesia
Computer Science & IT Education

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A Reproducible Ranking Pipeline for Gamified Peer-Assessment in Project-Based Learning Supporting SDG 4 Sabo Hermawan; Fauzan Fadlullah; Inkreswari Retno Hardini; Alifah Nur Rahmawati; Cornellius Seno Adriano
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 4 (2026): Research Paper October 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i4.9512

Abstract

Background: Gamified live-scoring effectively stimulates engagement in project-based learning. However, low-friction interfaces often introduce vulnerabilities, such as structurally invalid ranking selections that occur in rapid-response environments, threatening data integrity. Objective: This study develops a reproducible data-validation and lexicographic ranking pipeline for gamified peer-assessment systems to safeguard evaluation integrity and support SDG 4 (Quality Education). Methods: The proposed pipeline combines identity deduplication, structural validation of ranked selections, weighted score aggregation, and hierarchical tie-handling. The integrated validation and ranking pipeline was evaluated using 181 historical submissions from four classroom cohorts engaged in project-based learning. Results, The validation stages retained 155 valid submissions, establishing an 85.6 percent retention rate. An ablation analysis revealed that identity deduplication and ranking-rule validation detected partially overlapping data quality issues. Empirical analysis demonstrated that secondary vote-tier counts deterministically resolved ties when project groups shared the same aggregate score but differed in premium votes. Exact ties remaining across all ranking keys were assigned shared ranks. A sensitivity analysis comparing two weighting schemes found highly stable top-three rankings across all cohorts, ensuring algorithmic reproducibility. Conclusion: The findings indicate that the computational pipeline successfully produced an auditable and reproducible leaderboard under the evaluated classroom-scale dataset, thereby ensuring procedural consistency. Although the top-three rankings remained stable under the tested weighting schemes, broader validation is recommended before large-scale deployment.
Co-Authors Alifah Nur Rahmawati Cornellius Seno Adriano Hermawan, Sabo Inkreswari Retno Hardini