{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T16:00:02Z","timestamp":1780588802934,"version":"3.54.1"},"reference-count":55,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T00:00:00Z","timestamp":1678320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Effective classroom instruction requires monitoring student participation and interaction during class, identifying cues to simulate their attention. The ability of teachers to analyze and evaluate students\u2019 classroom behavior is becoming a crucial criterion for quality teaching. Artificial intelligence (AI)-based behavior recognition techniques can help evaluate students\u2019 attention and engagement during classroom sessions. With rapid digitalization, the global education system is adapting and exploring emerging technological innovations, such as AI, the Internet of Things, and big data analytics, to improve education systems. In educational institutions, modern classroom systems are supplemented with the latest technologies to make them more interactive, student centered, and customized. However, it is difficult for instructors to assess students\u2019 interest and attention levels even with these technologies. This study harnesses modern technology to introduce an intelligent real-time vision-based classroom to monitor students\u2019 emotions, attendance, and attention levels even when they have face masks on. We used a machine learning approach to train students\u2019 behavior recognition models, including identifying facial expressions, to identify students\u2019 attention\/non-attention in a classroom. The attention\/no-attention dataset is collected based on nine categories. The dataset is given the YOLOv5 pre-trained weights for training. For validation, the performance of various versions of the YOLOv5 model (v5m, v5n, v5l, v5s, and v5x) are compared based on different evaluation measures (precision, recall, mAP, and F1 score). Our results show that all models show promising performance with 76% average accuracy. Applying the developed model can enable instructors to visualize students\u2019 behavior and emotional states at different levels, allowing them to appropriately manage teaching sessions by considering student-centered learning scenarios. Overall, the proposed model will enhance instructors\u2019 performance and students at an academic level.<\/jats:p>","DOI":"10.3390\/bdcc7010048","type":"journal-article","created":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T02:30:33Z","timestamp":1678415433000},"page":"48","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":167,"title":["Real-Time Attention Monitoring System for Classroom: A Deep Learning Approach for Student\u2019s Behavior Recognition"],"prefix":"10.3390","volume":"7","author":[{"given":"Zouheir","family":"Trabelsi","sequence":"first","affiliation":[{"name":"Department of Information Systems and Security, College of Information Technology, UAEU, Al Ain 15551, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6102-3765","authenticated-orcid":false,"given":"Fady","family":"Alnajjar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineer, College of Information Technology, UAEU, Al Ain 15551, United Arab Emirates"},{"name":"AI and Robotics Lab (Air-Lab), UAEU, Al Ain 15551, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9336-2902","authenticated-orcid":false,"given":"Medha Mohan Ambali","family":"Parambil","sequence":"additional","affiliation":[{"name":"Department of Information Systems and Security, College of Information Technology, UAEU, Al Ain 15551, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6613-7435","authenticated-orcid":false,"given":"Munkhjargal","family":"Gochoo","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineer, College of Information Technology, UAEU, Al Ain 15551, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5996-7804","authenticated-orcid":false,"given":"Luqman","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineer, College of Information Technology, UAEU, Al Ain 15551, United Arab Emirates"},{"name":"AI and Robotics Lab (Air-Lab), UAEU, Al Ain 15551, United Arab Emirates"},{"name":"Emirates Center for Mobility Research, UAEU, Al Ain 15551, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8335","DOI":"10.1007\/s00521-020-05587-y","article-title":"Student Class Behavior Dataset: A Video Dataset for Recognizing, Detecting, and Captioning Students\u2019 Behaviors in Classroom Scenes","volume":"33","author":"Sun","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11162-005-8150-9","article-title":"Student Engagement and Student Learning: Testing the Linkages","volume":"47","author":"Carini","year":"2006","journal-title":"Res. High Educ."},{"key":"ref_3","unstructured":"Gupta, S., and Kumar, P. (2021). Emerging Technologies for Smart Cities: Select Proceedings of EGTET 2020, Springer."},{"key":"ref_4","unstructured":"(2023, January 19). Assessment, Evaluation, and Curriculum Redesign. Available online: https:\/\/www.thirteen.org\/edonline\/concept2class\/assessment\/index.html."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"100197","DOI":"10.1016\/j.jik.2022.100197","article-title":"Review on A big Data-Based Innovative Knowledge Teaching Evaluation System in Universities","volume":"7","author":"Xin","year":"2022","journal-title":"J. Innov. Knowl."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1177\/07356331211069424","article-title":"Facing radical digitalization: Capturing teachers\u2019 transition to virtual classrooms through ideal type experiences","volume":"60","author":"Willermark","year":"2022","journal-title":"J. Educ. Comput. Res."},{"key":"ref_7","first-page":"1","article-title":"How Smart Are Smart Classrooms? A Review of Smart Classroom Technologies","volume":"56","author":"Saini","year":"2019","journal-title":"ACM Comput. Surv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103818","DOI":"10.1016\/j.compedu.2020.103818","article-title":"The Effect of Using Kahoot! For Learning\u2014A Literature Review","volume":"149","author":"Wang","year":"2020","journal-title":"Comput. Educ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1021\/acs.jchemed.9b00933","article-title":"Using augmented reality to stimulate students and diffuse escape game activities to larger audiences","volume":"97","author":"Estudante","year":"2020","journal-title":"J. Chem. Educ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s41239-019-0176-8","article-title":"Mapping Research in Student Engagement and Educational Technology in Higher Education: A Systematic Evidence Map","volume":"17","author":"Bond","year":"2020","journal-title":"Int. J. Educ. Technol. High. Educ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e19285","DOI":"10.2196\/19285","article-title":"Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review","volume":"6","author":"Sapci","year":"2020","journal-title":"JMIR Med. Educ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"100091","DOI":"10.1016\/j.caeai.2022.100091","article-title":"Artificial intelligence-based robots in education: A systematic review of selected SSCI publications","volume":"3","author":"Chu","year":"2022","journal-title":"Comput. Educ. Artif. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.ijis.2020.09.001","article-title":"Artificial Intelligence Innovation in Education: A Twenty-Year Data-Driven Historical Analysis","volume":"4","author":"Guan","year":"2020","journal-title":"Int. J. Innov. Stud."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez-Calatayud, V., Prendes-Espinosa, P., and Roig-Vila, R. (2021). Artificial Intelligence for Student Assessment: A Systematic Review. Appl. Sci., 11.","DOI":"10.3390\/app11125467"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1177\/002221949002300509","article-title":"Classroom Behavior of Children and Adolescents with Learning Disabilities: A Meta-Analysis","volume":"23","author":"Bender","year":"1990","journal-title":"J. Learn. Disabil."},{"key":"ref_16","first-page":"4753","article-title":"A Simplified Real-Time Camera-Based Attention Assessment System for Classrooms: Pilot Study","volume":"2021","author":"Renawi","year":"2021","journal-title":"Educ. Inf. Technol."},{"key":"ref_17","unstructured":"(2023, January 19). Attentive or Not? Toward a Machine Learning Approach to Assessing Students\u2019 Visible Engagement in Classroom Instruction|SpringerLink. Available online: https:\/\/link.springer.com\/article\/10.1007\/s10648-019-09514-z."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Raca, M., and Dillenbourg, P. (2013, January 8\u201313). System for assessing classroom attention. Proceedings of the Third International Conference on Learning Analytics and Knowledge, New York, NY, USA.","DOI":"10.1145\/2460296.2460351"},{"key":"ref_19","unstructured":"(2023, January 19). Monitoring Students\u2019 Attention in A Classroom Through Computer Vision. Available online: https:\/\/www.springerprofessional.de\/en\/monitoring-students-attention-in-a-classroom-through-computer-vi\/15858720."},{"key":"ref_20","unstructured":"(2023, January 19). 2(PDF) Emotion Recognition and Detection Methods: A Comprehensive Survey. Available online: https:\/\/www.researchgate.net\/publication\/339119986_Emotion_Recognition_and_Detection_Methods_A_Comprehensive_Survey."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1186\/s13640-017-0228-8","article-title":"Predicting Students\u2019 Attention in the Classroom from Kinect Facial and Body Features","volume":"2017","author":"Zaletelj","year":"2017","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ngoc Anh, B., Tung Son, N., Truong Lam, P., Phuong Chi, L., Huu Tuan, N., Cong Dat, N., Huu Trung, N., Umar Aftab, M., and Van Dinh, T. (2019). A Computer-Vision Based Application for Student Behavior Monitoring in Classroom. Appl. Sci., 9.","DOI":"10.3390\/app9224729"},{"key":"ref_23","unstructured":"(2023, January 22). Translating Head Motion into Attention\u2014Towards Processing of Student\u2019s Body-Language, Available online: https:\/\/files.eric.ed.gov\/fulltext\/ED560534.pdf."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Broussard, D.M., Rahman, Y., Kulshreshth, A.K., and Borst, C.W. (April, January 37). An Interface for Enhanced Teacher Awareness of Student Actions and Attention in a VR Classroom. Proceedings of the 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), Lisbon, Portugal.","DOI":"10.1109\/VRW52623.2021.00058"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lin, F.C., Ngo, H.H., Dow, C.R., Lam, K.H., and Le, H.L. (2021). Student Behavior Recognition System for the Classroom Environment Based on Skeleton Pose Estimation and Person Detection. Sensors, 21.","DOI":"10.3390\/s21165314"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"10273","DOI":"10.3390\/s130810273","article-title":"Recognizing the Degree of Human Attention Using EEG Signals from Mobile Sensors","volume":"13","author":"Liu","year":"2013","journal-title":"sensors"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1111\/bjet.12359","article-title":"Assessing the Attention Levels of Students by Using a Novel Attention Aware System Based on Brainwave Signals","volume":"48","author":"Chen","year":"2017","journal-title":"Br. J. Educ. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Ober, S., and Jafari, R. (2017, January 9\u201312). Modeling and Detecting Student Attention and Interest Level Using Wearable Computers. Proceedings of the 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Eindhoven, The Netherlands.","DOI":"10.1109\/BSN.2017.7935996"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Pinkwart, N., and Liu, S. (2020). Artificial Intelligence Supported Educational Technologies, Springer International Publishing. Advances in Analytics for Learning and Teaching.","DOI":"10.1007\/978-3-030-41099-5"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hutt, S., Krasich, K., Brockmole, J.R., and K. D\u2019Mello, S. (2021, January 8\u201313). Breaking out of the Lab: Mitigating Mind Wandering with Gaze-Based Attention-Aware Technology in Classrooms. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Yokohama, Japan.","DOI":"10.1145\/3411764.3445269"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, X., Wu, C.W., Fournier-Viger, P., Van, L.D., and Tseng, Y.C. (2017, January 12\u201315). Analyzing Students\u2019 Attention in Class Using Wearable Devices. Proceedings of the 2017 IEEE 18th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), Macau, China.","DOI":"10.1109\/WoWMoM.2017.7974306"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1007\/s12008-019-00640-0","article-title":"Technologies for the Future of Learning: State of the Art","volume":"14","year":"2020","journal-title":"Int. J. Interact. Des. Manuf."},{"key":"ref_33","unstructured":"Bosch, N., D\u2019Mello, S.K., Baker, R.S., Ocumpaugh, J., Shute, V., Ventura, M., Wang, L., and Zhao, W. (2016, January 9\u201315). Detecting Student Emotions in Computer-Enabled Classrooms. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, Palo Alto, CA, USA."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Savva, A., Stylianou, V., Kyriacou, K., and Domenach, F. (2018, January 17\u201320). Recognizing Student Facial Expressions: A Web Application. Proceedings of the 2018 IEEE Global Engineering Education Conference (EDUCON), Santa Cruz de Tenerife, Spain.","DOI":"10.1109\/EDUCON.2018.8363404"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5490","DOI":"10.30534\/ijatcse\/2020\/191942020","article-title":"Class-EyeTention A Machine Vision Inference Approach of Student Attentiveness\u2019 Detection","volume":"9","year":"2020","journal-title":"Int. J. Adv. Trends Comput. Sci. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/s40299-019-00469-x","article-title":"Emotions in Education: Asian Insights on the Role of Emotions in Learning and Teaching","volume":"28","author":"King","year":"2019","journal-title":"Asia-Pac. Edu Res"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. arXiv.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_38","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 11\u201313). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Computer Society Annual Symposium on VLSI, Pittsburgh, PA, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yao, J., Qi, J., Zhang, J., Shao, H., Yang, J., and Li, X. (2021). A Real-Time Detection Algorithm for Kiwifruit Defects Based on YOLOv5. Electronics, 10.","DOI":"10.3390\/electronics10141711"},{"key":"ref_41","unstructured":"Dwivedi, P. (2023, January 19). YOLOv5 Compared to Faster RCNN. Who Wins?. Available online: https:\/\/towardsdatascience.com\/yolov5-compared-to-faster-rcnn-who-wins-a771cd6c9fb4."},{"key":"ref_42","unstructured":"Chablani, M. (2023, January 19). YOLO\u2014You Only Look Once, Real Time Object Detection Explained. Available online: https:\/\/towardsdatascience.com\/yolo-you-only-look-once-real-time-object-detection-explained-492dc9230006."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Otgonbold, M.E., Gochoo, M., Alnajjar, F., Ali, L., Tan, T.H., Hsieh, J.W., and Chen, P.Y. (2022). SHELK: An Extended Dataset and Benchmarking for Safety Helmet Detection. Sensors, 22.","DOI":"10.3390\/s22062315"},{"key":"ref_44","unstructured":"Jocher, G., Stoken, A., Borovec, J., Christopher, S.T.A.N., and Laughing, L.C. (2022, July 20). Ultralytics\/Yolov5: V4.0\u2014Nn.SiLU() Activations, Weights & Biases Logging, PyTorch Hub Integration. Available online: https:\/\/zenodo.org\/record\/4418161."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ali, L., Alnajjar, F., Parambil, M.M.A., Younes, M.I., Abdelhalim, Z.I., and Aljassmi, H. (2022). Development of YOLOv5-Based Real-Time Smart Monitoring System for Increasing Lab Safety Awareness in Educational Institutions. Sensors, 22.","DOI":"10.3390\/s22228820"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wojke, N., Bewley, A., and Paulus, D. (2017, January 17\u201320). Simple Online and Realtime Tracking with A Deep Association Metric. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"ref_47","unstructured":"(2023, January 22). Introduction to Kalman Filter and Its Applications. Available online: https:\/\/www.intechopen.com\/chapters\/63164."},{"key":"ref_48","unstructured":"Dwivedi, P. (2023, January 19). People Tracking Using Deep Learning. Available online: https:\/\/towardsdatascience.com\/people-tracking-using-deep-learning-5c90d43774be."},{"key":"ref_49","unstructured":"Padilla, R., Filho, C., and Costa, M. (2012). Evaluation of Haar Cascade Classifiers for Face Detection. Venice Italy World Acad. Sci., 6."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Alexandrova, S., Tatlock, Z., and Cakmak, M. (2015, January 25\u201330). RoboFlow: A Flow-Based Visual Programming Language for Mobile Manipulation Tasks. Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7139973"},{"key":"ref_51","unstructured":"Tzutalin (2022, July 20). LabelImg. Git Code. Available online: https:\/\/github.com\/tzutalin\/labelImg."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/TAFFC.2017.2740923","article-title":"AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild","volume":"10","author":"Mollahosseini","year":"2019","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_53","unstructured":"Anwar, A., and Raychowdhury, A. (2023, January 19). Masked Face Recognition for Secure Authentication. Available online: https:\/\/arxiv.org\/abs\/2008.11104."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Parambil, M.M.A., Ali, L., Alnajjar, F., and Gochoo, M. (2022, January 21\u201324). Smart Classroom: A Deep Learning Approach towards Attention Assessment through Class Behavior Detection. Proceedings of the 2022 Advances in Science and Engineering Technology International Conferences (ASET), Dubai, United Arab Emirates.","DOI":"10.1109\/ASET53988.2022.9735018"},{"key":"ref_55","unstructured":"(2023, January 19). Personalized Robot Interventions for Autistic Children: An Automated Methodology for Attention Assessment|SpringerLink. Available online: https:\/\/link.springer.com\/article\/10.1007\/s12369-020-00639-8."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/7\/1\/48\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:51:57Z","timestamp":1760122317000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/7\/1\/48"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,9]]},"references-count":55,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["bdcc7010048"],"URL":"https:\/\/doi.org\/10.3390\/bdcc7010048","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,9]]}}}