arXiv subjects

Uchswas Paul

Publications and source records attributed to Uchswas Paul.

4 recordsLinked to original sources

arXiv2026

Students' Perceptions of Peer Grading

Peer grading is widely used in education, yet it elicits mixed reactions from educators and students. Although many studies have examined students' views of peer grading, their findings are scattered, and no clear overall picture has emerged. To address this gap, we conducted a mixed-source thematic analysis of literature and student discussions on Reddit. To scale our analysis of the Reddit data, we fine-tuned a Gemini 2.5 text-classification model to classify an initial dataset of 659 posts and 6,607 comments by relevance. Manual review of the items classified as relevant by the model yielded a final dataset of 114 posts and 300 comments. Drawing on evidence from 107 papers and the Reddit dataset, we found that students perceive peer grading as both beneficial and problematic. Positive perceptions included learning and understanding benefits, skill development, engagement, and collaboration, while negative perceptions centered on unreliable grading, unfairness, weak feedback quality, emotional stress, and workload. Reddit discussions also suggested an emerging concern that remains underexplored in the literature: AI use in peer grading may weaken students' trust in the accuracy and authenticity of the process. We further identified eight mitigation strategies and mapped them to the negative perceptions they help address. Among these, instructor oversight and training played the most central role.

cs.CY↗

arXiv2025

Configuration Defects in Kubernetes

Kubernetes is a tool that facilitates rapid deployment of software. Unfortunately, configuring Kubernetes is prone to errors. Configuration defects are not uncommon and can result in serious consequences. This paper reports an empirical study about configuration defects in Kubernetes with the goal of helping practitioners detect and prevent these defects. We study 719 defects that we extract from 2,260 Kubernetes configuration scripts using open source repositories. Using qualitative analysis, we identify 15 categories of defects. We find 8 publicly available static analysis tools to be capable of detecting 8 of the 15 defect categories. We find that the highest precision and recall of those tools are for defects related to data fields. We develop a linter to detect two categories of defects that cause serious consequences, which none of the studied tools are able to detect. Our linter revealed 26 previously-unknown defects that have been confirmed by practitioners, 19 of which have already been fixed. We conclude our paper by providing recommendations on how defect detection and repair techniques can be used for Kubernetes configuration scripts. The datasets and source code used for the paper are publicly available online.

cs.SE↗

arXiv2025

Scaling Success: A Systematic Review of Peer Grading Strategies for Accuracy, Efficiency, and Learning in Contemporary Education

Peer grading has emerged as a scalable solution for assessment in large and online classrooms, offering both logistical efficiency and pedagogical value. However, designing effective peer-grading systems remains challenging due to persistent concerns around accuracy, fairness, reliability, and student engagement. This paper presents a systematic review of 122 peer-reviewed studies on peer grading spanning over four decades. Drawing from this literature, we propose a comprehensive taxonomy that organizes peer grading systems along two key dimensions: (1) evaluation approaches and (2) reviewer weighting strategies. We analyze how different design choices impact grading accuracy, fairness, student workload, and learning outcomes. Our findings highlight the strengths and limitations of each method. Notably, we found that formative feedback -- often regarded as the most valuable aspect of peer assessment -- is seldom incorporated as a quality-based weighting factor in summative grade synthesis techniques. Furthermore, no single reviewer weighting strategy proves universally optimal; each has its trade-offs. Hybrid strategies that combine multiple techniques could show the greatest promise. Our taxonomy offers a practical framework for educators and researchers aiming to design peer grading systems that are accurate, equitable, and pedagogically meaningful.

cs.CY↗

arXiv2025

Optimizing Peer Grading: A Systematic Literature Review of Reviewer Assignment Strategies and Quantity of Reviewers

Peer assessment has established itself as a critical pedagogical tool in academic settings, offering students timely, high-quality feedback to enhance learning outcomes. However, the efficacy of this approach depends on two factors: (1) the strategic allocation of reviewers and (2) the number of reviews per artifact. This paper presents a systematic literature review of 87 studies (2010--2024) to investigate how reviewer-assignment strategies and the number of reviews per submission impact the accuracy, fairness, and educational value of peer assessment. We identified four common reviewer-assignment strategies: random assignment, competency-based assignment, social-network-based assignment, and bidding. Drawing from both quantitative data and qualitative insights, we explored the trade-offs involved in each approach. Random assignment, while widely used, often results in inconsistent grading and fairness concerns. Competency-based strategies can address these issues. Meanwhile, social and bidding-based methods have the potential to improve fairness and timeliness -- existing empirical evidence is limited. In terms of review count, assigning three reviews per submission emerges as the most common practice. A range of three to five reviews per student or per submission is frequently cited as a recommended spot that balances grading accuracy, student workload, learning outcomes, and engagement.

cs.CY↗