The paper introduces the Visage Face dataset, containing 16,234 face samples captured in varied classroom conditions such as different angles, lighting, expressions, and partial occlusions. Using a YOLO-based detector, the authors evaluate seven state‑of‑the‑art face recognition models across thirteen configurations, finding that FaceLiVTv2‑M achieves the highest performance with 99.75% Top‑1/Top‑5 accuracy and a 6.459 ms inference time. These findings demonstrate that the Visage Face dataset provides a realistic and challenging benchmark for classroom attendance systems.
By Himani Trivedi, Hiren Patel, Ridham Patel, Krutika Patel, Nancy Patel
arXiv:2607. 01115v1 Announce Type: cross Abstract: University stakeholders often face difficulties in accessing timely and reliable information, especially in developing countries, where there are very few intelligent support systems.
By Md Abu Hanif Shaikh, Abdullah Al Shafi
arXiv:2607. 18767v1 Announce Type: cross Abstract: The deployment of Small Language Models (SLMs) in educational settings offers significant advantages in terms of privacy, cost, and scalability.
By Lachlan McGinness
arXiv:2604. 03401v4 Announce Type: replace-cross Abstract: Understanding student engagement usually requires time-consuming manual observation or invasive recording that raises privacy concerns.
By Nolan Platt, Sehrish Nizamani, Alp Tural, Elif Tural, Saad Nizamani, Andrew Katz, Yoonje Lee, Nada Basit
arXiv:2606. 14748v1 Announce Type: cross Abstract: We present the Membership Inference Test (MINT) Demo 2, a framework designed to improve transparency in machine learning training processes.
By Daniel DeAlcala, Gonzalo Mancera, Julian Fierrez, Aythami Morales, Ruben Tolosana, Ruben Vera-Rodriguez
arXiv:2606. 08855v1 Announce Type: new Abstract: This paper examines the limitations of fully digital and partially digital e-assessment approaches in summative examinations in higher education.
By Hartwig Grabowski, Michael Canz
arXiv:2503. 06525v2 Announce Type: replace-cross Abstract: Analyzing student behavior in educational scenarios is crucial for enhancing teaching quality and student engagement.
By Xian Gao, Jiacheng Ruan, Jingsheng Gao, Mingye Xie, Zongyun Zhang, Ting Liu, Yuzhuo Fu
arXiv:2407. 13053v2 Announce Type: replace-cross Abstract: Digital textbook (e-book) systems record student interactions with textbooks as a sequence of events called EventStream data.
By Yuma Miyazaki, Valdemar \v{S}v\'abensk\'y, Yuta Taniguchi, Fumiya Okubo, Tsubasa Minematsu, Atsushi Shimada
arXiv:2606. 00015v1 Announce Type: cross Abstract: Operating Systems (OS) courses are among the most challenging in computer science education due to the complexity of internal structures and the diversity of running environments.
By Yifan Zhang, Xinkui Zhao, Zuxin Wang, Zhengyi Zhou, Guanjie Chen, Shuiguang Deng, Jianwei Yin
arXiv:2609.36502v1 Announce Type: cross
Abstract: Past research using log data has faced the "learning system wall," whereby few methods exist for generalizing models of student learning across platf...
By Danielle R. Thomas, Marie Cynthia Abijuru Kamikazi, Ashish Gurung, Ishan Miglani, Shivang Gupta, Zachary Levonian, Conrad Borchers, Kenneth R. Koedinger
arXiv:2606. 09670v1 Announce Type: cross Abstract: Recent Anomaly Detection methods achieve perfect detection and segmentation scores on well-established datasets, such as MVTec.
By Mateo Diaz-Bone, Daniel Caraballo, Florian Scheidegger, Thomas Frick, Mattia Rigotti, Andrea Bartezzaghi, Roy Assaf, Niccolo Avogaro, Yagmur G. Cinar, Brown Ebouky, Filip M. Janicki, Piotr S. Kluska, Cezary Skura, Cristiano Malossi
Recent Anomaly Detection methods achieve perfect detection and segmentation scores on well-established datasets, such as MVTec. However, many of these methods face challenges when foundational assumptions - such as consistent object scale, viewpoint, background, illumination, and centered placement - are violated.