{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:11:56Z","timestamp":1776442316764,"version":"3.51.2"},"reference-count":29,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T00:00:00Z","timestamp":1661904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Teaching and exam proctoring represent key pillars of the education system. Human proctoring, which involves visually monitoring examinees throughout exams, is an important part of assessing the academic process. The capacity to proctor examinations is a critical component of educational scalability. However, such approaches are time-consuming and expensive. In this paper, we present a new framework for the learning and classification of cheating video sequences. This kind of study aids in the early detection of students\u2019 cheating. Furthermore, we introduce a new dataset, \u201cactions of student cheating in paper-based exams\u201d. The dataset consists of suspicious actions in an exam environment. Five classes of cheating were performed by eight different actors. Each pair of subjects conducted five distinct cheating activities. To evaluate the performance of the proposed framework, we conducted experiments on action recognition tasks at the frame level using five types of well-known features. The findings from the experiments on the framework were impressive and substantial.<\/jats:p>","DOI":"10.3390\/data7090122","type":"journal-article","created":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T00:13:56Z","timestamp":1661904836000},"page":"122","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Advances in Contextual Action Recognition: Automatic Cheating Detection Using Machine Learning Techniques"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5290-515X","authenticated-orcid":false,"given":"Fairouz","family":"Hussein","sequence":"first","affiliation":[{"name":"Department of Computer Information System, Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan"}]},{"given":"Ayat","family":"Al-Ahmad","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Applications, Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9700-4862","authenticated-orcid":false,"given":"Subhieh","family":"El-Salhi","sequence":"additional","affiliation":[{"name":"Department of Computer Information System, Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan"}]},{"given":"Esra\u2019a","family":"Alshdaifat","sequence":"additional","affiliation":[{"name":"Department of Computer Information System, Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan"}]},{"given":"Mo\u2019taz","family":"Al-Hami","sequence":"additional","affiliation":[{"name":"Department of Computer Information System, Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Oreifej, O., and Liu, Z. (2013, January 23\u201328). Hon4d: Histogram of oriented 4d normals for activity recognition from depth sequences. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.98"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Alshdaifat, E., Alshdaifat, D., Alsarhan, A., Hussein, F., and El-Salhi, S.M.F.S. (2021). The effect of preprocessing techniques, applied to numeric features, on classification algorithms\u2019 performance. 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