Abnormal Behavior in Online Exam: Distance Learning Assessments Dataset

This paper presents a newly collected and highly relevant dataset on students' abnormal behavior in online exams. This dataset focuses on assisting research in building machine-learning models that allow for maintaining academic integrity during the era of online exams. Properly, more than 8,50...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Iraqi journal for computers and informatics (Online) Ročník 51; číslo 2; s. 1 - 7
Hlavní autor: Alkhalisy, Muhanad
Médium: Journal Article
Jazyk:arabština
angličtina
Vydáno: University of Information Technology and Communications 08.08.2025
Témata:
behavioural analysis
computer vision
deep learning
online exam
student behaviour
ISSN:2313-190X, 2520-4912
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This paper presents a newly collected and highly relevant dataset on students' abnormal behavior in online exams. This dataset focuses on assisting research in building machine-learning models that allow for maintaining academic integrity during the era of online exams. Properly, more than 8,500 annotated images of normal and abnormal behaviors of students during remote examination are held in the dataset hosted at the Harvard Dataverse repository. The dataset has two versions: the original and the augmented. We utilize semantic segmentation and deep learning techniques in the applied data augmentation; this dataset provides a crucial foundation for developing and benchmarking intelligent proctoring systems. We evaluate the dataset using YOLO5 and our improved SPL-YOLO5 model, and the resulting mean average precision (mAP) is close to 1.0.
ISSN:2313-190X
2520-4912
DOI:10.25195/ijci.v51i2.595