Abnormal Behavior in Online Exam: Distance Learning Assessments Dataset
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| Title: | Abnormal Behavior in Online Exam: Distance Learning Assessments Dataset |
|---|---|
| Authors: | Muhanad Alkhalisy |
| Source: | Iraqi Journal for Computers and Informatics, Vol 51, Iss 2, Pp 1-7 (2025) |
| Publisher Information: | University of Information Technology and Communications, 2025. |
| Publication Year: | 2025 |
| Collection: | LCC:Technology |
| Subject Terms: | computer vision, behavioural analysis, online exam, student behaviour, deep learning, Technology |
| Description: | 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | Arabic English |
| ISSN: | 2313-190X 2520-4912 |
| Relation: | https://ijci.uoitc.edu.iq/index.php/ijci/article/view/595; https://doaj.org/toc/2313-190X; https://doaj.org/toc/2520-4912 |
| DOI: | 10.25195/ijci.v51i2.595 |
| Access URL: | https://doaj.org/article/2b420c984cb14a2ca3efe8c029cf943c |
| Accession Number: | edsdoj.2b420c984cb14a2ca3efe8c029cf943c |
| Database: | Directory of Open Access Journals |
Nájsť tento článok vo Web of Science | Abstract: | 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: | 2313190X 25204912 |
| DOI: | 10.25195/ijci.v51i2.595 |