Parvez Rashid | Searcharxiv

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↗

arXiv2021

ALL-IN-ONE: Multi-Task Learning BERT models for Evaluating Peer Assessments

Peer assessment has been widely applied across diverse academic fields over the last few decades and has demonstrated its effectiveness. However, the advantages of peer assessment can only be achieved with high-quality peer reviews. Previous studies have found that high-quality review comments usually comprise several features (e.g., contain suggestions, mention problems, use a positive tone). Thus, researchers have attempted to evaluate peer-review comments by detecting different features using various machine learning and deep learning models. However, there is no single study that investigates using a multi-task learning (MTL) model to detect multiple features simultaneously. This paper presents two MTL models for evaluating peer-review comments by leveraging the state-of-the-art pre-trained language representation models BERT and DistilBERT. Our results demonstrate that BERT-based models significantly outperform previous GloVe-based methods by around 6% in F1-score on tasks of detecting a single feature, and MTL further improves performance while reducing model size.

cs.CL↗