Academic literature on the topic 'Student Engagement Detection'
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Journal articles on the topic "Student Engagement Detection"
Bustos-López, Maritza, Nicandro Cruz-Ramírez, Alejandro Guerra-Hernández, Laura Nely Sánchez-Morales, Nancy Aracely Cruz-Ramos, and Giner Alor-Hernández. "Wearables for Engagement Detection in Learning Environments: A Review." Biosensors 12, no. 7 (July 11, 2022): 509. http://dx.doi.org/10.3390/bios12070509.
Full textAlruwais, Nuha, and Mohammed Zakariah. "Student-Engagement Detection in Classroom Using Machine Learning Algorithm." Electronics 12, no. 3 (February 1, 2023): 731. http://dx.doi.org/10.3390/electronics12030731.
Full textVanneste, Pieter, José Oramas, Thomas Verelst, Tinne Tuytelaars, Annelies Raes, Fien Depaepe, and Wim Van den Noortgate. "Computer Vision and Human Behaviour, Emotion and Cognition Detection: A Use Case on Student Engagement." Mathematics 9, no. 3 (February 1, 2021): 287. http://dx.doi.org/10.3390/math9030287.
Full textKasatkina, D. A., A. M. Kravchenko, R. B. Kupriyanov, and E. V. Nekhorosheva. "Automatic engagement detection in the education: critical review." Современная зарубежная психология 9, no. 3 (2020): 59–68. http://dx.doi.org/10.17759/jmfp.2020090305.
Full textJiang, Lanlan. "Analysis of Students’ Role Perceptions and their Tendencies in Classroom Education Based on Visual Inspection." Occupational Therapy International 2022 (April 14, 2022): 1–11. http://dx.doi.org/10.1155/2022/3650308.
Full textMai, Tai Tan, Martin Crane, and Marija Bezbradica. "Students’ Learning Behaviour in Programming Education Analysis: Insights from Entropy and Community Detection." Entropy 25, no. 8 (August 17, 2023): 1225. http://dx.doi.org/10.3390/e25081225.
Full textWang, Zhifeng, Minghui Wang, Chunyan Zeng, and Longlong Li. "SBD-Net: Incorporating Multi-Level Features for an Efficient Detection Network of Student Behavior in Smart Classrooms." Applied Sciences 14, no. 18 (September 17, 2024): 8357. http://dx.doi.org/10.3390/app14188357.
Full textJing Yang, Lin Liu,. "Exploring the Path to Improve the Quality of Student Management Education Based on Knowledge Graph and NB-loT Architecture." Journal of Electrical Systems 20, no. 4s (April 8, 2024): 212–25. http://dx.doi.org/10.52783/jes.1907.
Full textLavanya, R., M. MEENATCHI, and R. SARANYA. "Monitoring of Participation Monitoring, Optical Somnolence Recognition and Proctorial Supervision." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 008 (August 9, 2024): 1–15. http://dx.doi.org/10.55041/ijsrem37014.
Full textkumar S*, Ashok, Ragul R.N, Praveen Kumar S, and Gokula krishnan D. "Student Monitoring System using Machine Learning." International Journal of Innovative Technology and Exploring Engineering 9, no. 6 (April 30, 2020): 1475–79. http://dx.doi.org/10.35940/ijitee.e4213.049620.
Full textDissertations / Theses on the topic "Student Engagement Detection"
Moufidi, Abderrazzaq. "Machine Learning-Based Multimodal integration for Short Utterance-Based Biometrics Identification and Engagement Detection." Electronic Thesis or Diss., Angers, 2024. http://www.theses.fr/2024ANGE0026.
Full textThe rapid advancement and democratization of technology have led to an abundance of sensors. Consequently, the integration of these diverse modalities presents an advantage for numerous real-life applications, such as biometrics recognition and engage ment detection. In the field of multimodality, researchers have developed various fusion ar chitectures, ranging from early, hybrid, to late fusion approaches. However, these architec tures may have limitations involving short utterances and brief video segments, necessi tating a paradigm shift towards the development of multimodal machine learning techniques that promise precision and efficiency for short-duration data analysis. In this thesis, we lean on integration of multimodality to tackle these previous challenges ranging from supervised biometrics identification to unsupervised student engagement detection. This PhD began with the first contribution on the integration of multiscale Wavelet Scattering Transform with x-vectors architecture, through which we enhanced the accuracy of speaker identification in scenarios involving short utterances. Going through multimodality benefits, a late fusion architecture combining lips depth videos and audio signals further improved identification accuracy under short utterances, utilizing an effective and less computational methods to extract spatiotemporal features. In the realm of biometrics challenges, there is the threat emergence of deepfakes. There-fore, we focalized on elaborating a deepfake detection methods based on, shallow learning and a fine-tuned architecture of our previous late fusion architecture applied on RGB lips videos and audios. By employing hand-crafted anomaly detection methods for both audio and visual modalities, the study demonstrated robust detection capabilities across various datasets and conditions, emphasizing the importance of multimodal approaches in countering evolving deepfake techniques. Expanding to educational contexts, the dissertation explores multimodal student engagement detection in classrooms. Using low-cost sensors to capture Heart Rate signals and facial expressions, the study developed a reproducible dataset and pipeline for identifying significant moments, accounting for cultural nuances. The analysis of facial expressions using Vision Transformer (ViT) fused with heart rate signal processing, validated through expert observations, showcased the potential for real-time monitoring to enhance educational outcomes through timely interventions
Calado, Jorge Miguel da Silva. "A Framework for Students Profile Detection." Master's thesis, 2017. http://hdl.handle.net/10362/21765.
Full textBook chapters on the topic "Student Engagement Detection"
Ravi, Pooja, and M. Ali Akber Dewan. "Real-time Multi-module Student Engagement Detection System." In Communication and Intelligent Systems, 261–78. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2100-3_22.
Full textDeshpande, Chinar. "AI-Based Student Emotion and Engagement Level Detection Framework." In Lecture Notes on Data Engineering and Communications Technologies, 268–80. Singapore: Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-9255-9_18.
Full textAhmed, Zeyad A. T., Mukti E. Jadhav, Ali Mansour Al-madani, Mohammed Tawfik, Saleh Nagi Alsubari, and Ahmed Abdullah A. Shareef. "Real-Time Detection of Student Engagement: Deep Learning-Based System." In Advances in Intelligent Systems and Computing, 313–23. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2594-7_26.
Full textSharma, Prabin, Shubham Joshi, Subash Gautam, Sneha Maharjan, Salik Ram Khanal, Manuel Cabral Reis, João Barroso, and Vítor Manuel de Jesus Filipe. "Student Engagement Detection Using Emotion Analysis, Eye Tracking and Head Movement with Machine Learning." In Communications in Computer and Information Science, 52–68. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-22918-3_5.
Full textToti, Daniele, Nicola Capuano, Fernanda Campos, Mario Dantas, Felipe Neves, and Santi Caballé. "Detection of Student Engagement in e-Learning Systems Based on Semantic Analysis and Machine Learning." In Advances on P2P, Parallel, Grid, Cloud and Internet Computing, 211–23. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61105-7_21.
Full textOkur, Eda, Nese Alyuz, Sinem Aslan, Utku Genc, Cagri Tanriover, and Asli Arslan Esme. "Behavioral Engagement Detection of Students in the Wild." In Lecture Notes in Computer Science, 250–61. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61425-0_21.
Full textZavarrone, Emma, Maria Gabriella Grassia, Rocco Mazza, and Alessia Forciniti. "Emergency remote teaching: an explorative tool." In Proceedings e report, 61–66. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-461-8.12.
Full textChuraev, Egor, and Andrey V. Savchenko. "To Kill a Student’s Disengagement: Personalized Engagement Detection in Facial Video." In Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky, 329–37. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64315-6_29.
Full textArwa, Allinjawi, Altuwairqi Khawlah, Kammoun Jarraya Salma, Abuzinadah Nihal, and Alkhuraiji Samar. "CNN-Based Face Emotion Detection and Mouse Movement Analysis to Detect Student’s Engagement Level." In International Conference on Advanced Intelligent Systems for Sustainable Development, 604–26. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26384-2_53.
Full textKootsookos, Alexandra. "Assessment Practices using Online Tools in Undergraduate Programs." In Student Engagement and Participation, 571–86. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2584-4.ch029.
Full textConference papers on the topic "Student Engagement Detection"
Mazumder, Deepra, Aniruddha Chatterjee, Anwesha Chakraborty, and Raja Karmakar. "A Novel Student Engagement Level Detection and Emotion Analysis Using Ensemble Learning." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–7. IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725730.
Full textLima, Mariam, Kazi Rifat Ahmed, Nusrat Jahan, Imran Mahmud, Md Shahriar Parvez, and P. Revathi. "Deep Learning Based Approach For Detecting Student Engagement Through Facial Emotions." In 2024 International Conference on Data Science and Network Security (ICDSNS), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10691098.
Full textBocanumenth, Aurora, and Elizabeth Rendón-Vélez. "Engagement State Definition and Detection in Education: A Review." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-95597.
Full textB, Perumal, Nagaraj P, Thulasi Sai Narsimha Charan, Yellampalli Venkata Siva SaiDeepak, Chennuru Venkata Vignesh Reddy, and Sanakam Nagendra. "Student Engagement Detection in Classroom using Deep CNN-based Learning Approach." In 2023 8th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2023. http://dx.doi.org/10.1109/icces57224.2023.10192809.
Full textGuo, Shouchao. "Detection of Student Engagement in 3-D Design Course Using xAPI and Levenshtein Distance." In 2020 AERA Annual Meeting. Washington DC: AERA, 2020. http://dx.doi.org/10.3102/1577638.
Full textVishnumolakala, Sai Krishna, VSNV Sadwika Vallamkonda, Sobin C. C, N. P. Subheesh, and Jahfar Ali. "In-class Student Emotion and Engagement Detection System (iSEEDS): An AI-based Approach for Responsive Teaching." In 2023 IEEE Global Engineering Education Conference (EDUCON). IEEE, 2023. http://dx.doi.org/10.1109/educon54358.2023.10125254.
Full textShan, J., and Sherin Eliyas. "Exploring AI Facial Recognition for Real-time Emotion Detection: Assessing Student Engagement in Online Learning Environments." In 2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT). IEEE, 2024. http://dx.doi.org/10.1109/aiiot58432.2024.10574587.
Full textPinto, Adam Henrique, and Roseli Aparecida Romero. "EEG signal detection and analysis with application in educational robotics." In VIII Workshop de Teses e Dissertações em Robótica/Concurso de Teses e Dissertações em Robótica. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wtdr_ctdr.2020.14951.
Full textDunaev, Mihail, George Milescu, Razvan Rughinis, and Vlad Posea. "EXPRESSIO: AUTOMATIC FEEDBACK FOR MOOC TEACHERS BASED ON AFFECTIVE COMPUTING." In eLSE 2015. Carol I National Defence University Publishing House, 2015. http://dx.doi.org/10.12753/2066-026x-15-185.
Full textBosch, Nigel. "Detecting Student Engagement." In UMAP '16: User Modeling, Adaptation and Personalization Conference. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2930238.2930371.
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