Seeing the Room, Not the Student: A Privacy-Preserving Computer Vision Prototype for Anonymous Aggregate Classroom Visual-Engagement Trend Monitoring from Face-Visibility and Head-Orientation Cues
Main Article Content
Abstract
Camera-based classroom analytics can preserve a record of how a room behaved during a lesson, but conventionally built systems store face images that remain open to re-identification, name individual learners, and report weak visual cues as attention scores, the signal cannot support. The gap addressed here is an engineering one: neither the reported classroom-vision prototype nor the verification restricts storage to aggregate numbers, and both verify this restriction below the application layer. This paper presents the design, implementation and pilot evaluation of a prototype built to that constraint. Webcam frames are read in real time, passed through face detection and coarse head-orientation estimation, and released from memory; only numerical aggregate records reach disk, and a Streamlit dashboard renders the resulting trends. Three processing modules were compared under identical logging conditions: a Haar Cascade baseline, an OpenCV deep neural network (DNN) face detector, and a MediaPipe head-orientation module. A controlled pilot covering six predefined conditions retained 100 aggregate orientation records; the intended dominant category was recovered in 6 of 6 sessions (100%), with 29% front-facing, 21% looking left, 20% looking right, 17% looking down and 13% looking up. Of 23 DNN records, 22 carried a detection with a mean confidence of 0.890 to 1.000, all clearing the 0.60 acceptance threshold by at least 0.29, against a 4.3% zero-detection rate. A three-layer system-level audit recorded no write outside the aggregate CSV logs. The findings indicate that a research-safe aggregate visual analytics pipeline can be engineered, and its privacy boundary verified, before classroom-level deployment.
Downloads
Article Details
Section
How to Cite
References
Appleton, J.J., Christenson, S.L., and Furlong, M.J., 2008. Student engagement with school: Critical conceptual and methodological issues of the construct. Psychology in the Schools, 45(5), pp. 369–386. https://doi.org/10.1002/pits.20303
Baker, R.S.J.d., D'Mello, S.K., Rodrigo, M.M.T., and Graesser, A.C., 2010. Better to be frustrated than bored: The incidence, persistence, and impact of learners' cognitive-affective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 68(4), pp. 223–241. https://doi.org/10.1016/j.ijhcs.2009.12.003
Baltrušaitis, T., Zadeh, A., Lim, Y.C., and Morency, L.P., 2018. OpenFace 2.0: Facial behavior analysis toolkit. In Proceedings of the 13th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2018), pp. 59–66. https://doi.org/10.1109/FG.2018.00019
Barbhuyan, M.F.K., Wheeb, A.H., and Morshedur, M., 2026. Exploring the future of connectivity: A comprehensive review of wireless and optical communications. In Data-Driven AI: A Multidisciplinary Approach – Techniques, Applications, and Insights from Multiple Domains, 1st ed. Boca Raton: CRC Press, pp. 75–79. https://doi.org/10.1201/9781003753377-14
Blikstein, P., 2013. Multimodal learning analytics. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (LAK '13), pp. 102–106. https://doi.org/10.1145/2460296.2460316
Bond, M., Buntins, K., Bedenlier, S., Zawacki-Richter, O., and Kerres, M., 2020. Mapping research in student engagement and educational technology in higher education: A systematic evidence map. International Journal of Educational Technology in Higher Education, 17(1), Article 2. https://doi.org/10.1186/s41239-019-0176-8
Calvo, R.A., and D'Mello, S., 2010. Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1(1), pp. 18–37. https://doi.org/10.1109/T-AFFC.2010.1
Cerratto Pargman, T., and McGrath, C., 2021. Mapping the ethics of learning analytics in higher education: A systematic literature review of empirical research. Journal of Learning Analytics, 8(2), pp. 123–139. https://doi.org/10.18608/jla.2021.1
Climent-Pérez, P., and Flórez-Revuelta, F., 2021. Protection of visual privacy in videos acquired with RGB cameras for active and assisted living applications. Multimedia Tools and Applications, 80(15), pp. 23649–23664. https://doi.org/10.1007/s11042-020-10249-1
Cormack, A.N., 2016. A data protection framework for learning analytics. Journal of Learning Analytics, 3(1), pp. 91–106. https://doi.org/10.18608/jla.2016.31.6
Dalal, N., and Triggs, B., 2005. Histograms of oriented gradients for human detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 886–893. https://doi.org/10.1109/CVPR.2005.177
Deng, J., Guo, J., Ververas, E., Kotsia, I., and Zafeiriou, S., 2020. RetinaFace: Single-shot multi-level face localisation in the wild. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5203–5212. https://doi.org/10.1109/CVPR42600.2020.00525
Di Mitri, D., Schneider, J., Specht, M., and Drachsler, H., 2018. From signals to knowledge: A conceptual model for multimodal learning analytics. Journal of Computer Assisted Learning, 34(4), pp. 338–349. https://doi.org/10.1111/jcal.12288
D'Mello, S., and Graesser, A., 2012. Dynamics of affective states during complex learning. Learning and Instruction, 22(2), pp. 145–157. https://doi.org/10.1016/j.learninstruc.2011.10.001
Drachsler, H., and Greller, W., 2016. Privacy and analytics - it's a DELICATE issue. A checklist to establish trusted learning analytics. In Proceedings of the Sixth International Conference on Learning Analytics and Knowledge (LAK '16), pp. 89–98. https://doi.org/10.1145/2883851.2883893
Dwork, C., 2006. Differential privacy. In Automata, Languages and Programming (ICALP 2006), pp. 1–12. https://doi.org/10.1007/11787006_1
Ferguson, R., 2012. Learning analytics: Drivers, developments and challenges. International Journal of Technology
Enhanced Learning, 4(5/6), pp. 304–317. https://doi.org/10.1504/IJTEL.2012.051816
Fredricks, J.A., Blumenfeld, P.C., and Paris, A.H., 2004. School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), pp. 59–109. https://doi.org/10.3102/00346543074001059
Gašević, D., Dawson, S., and Siemens, G., 2015. Let's not forget: Learning analytics are about learning. TechTrends, 59, pp. 64–71. https://doi.org/10.1007/s11528-014-0822-x
Goldberg, P., Sümer, Ö., Stürmer, K., Wagner, W., Göllner, R., Gerjets, P., Kasneci, E., and Trautwein, U., 2021. Attentive or not? Toward a machine learning approach to assessing students' visible engagement in classroom instruction. Educational Psychology Review, 33(1), pp. 27–49. https://doi.org/10.1007/s10648-019-09514-z
Hempel, T., Abdelrahman, A.A., and Al-Hamadi, A., 2022. 6D rotation representation for unconstrained head pose estimation. In Proceedings of the IEEE International Conference on Image Processing (ICIP), pp. 2496–2500. https://doi.org/10.1109/ICIP46576.2022.9897219
Ifenthaler, D., and Schumacher, C., 2016. Student perceptions of privacy principles for learning analytics. Educational Technology Research and Development, 64(5), pp. 923–938. https://doi.org/10.1007/s11423-016-9477-y
Jones, K.M.L., 2019. Learning analytics and higher education: A proposed model for establishing informed consent mechanisms to promote student privacy and autonomy. International Journal of Educational Technology in Higher Education, 16(1), Article 24. https://doi.org/10.1186/s41239-019-0155-0
Kahu, E.R., 2013. Framing student engagement in higher education. Studies in Higher Education, 38(5), pp. 758–773. https://doi.org/10.1080/03075079.2011.598505
Karimah, S.N., and Hasegawa, S., 2022. Automatic engagement estimation in smart education/learning settings: A systematic review of engagement definitions, datasets, and methods. Smart Learning Environments, 9(1), Article 31. https://doi.org/10.1186/s40561-022-00212-y
Kärkkäinen, K., and Joo, J., 2021. FairFace: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 1548–1558. https://doi.org/10.1109/WACV48630.2021.00159
Kazemi, V., and Sullivan, J., 2014. One millisecond face alignment with an ensemble of regression trees. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1867–1874. https://doi.org/10.1109/CVPR.2014.241
Li, W., Sun, K., Schaub, F., and Brooks, C., 2022. Disparities in students' propensity to consent to learning analytics. International Journal of Artificial Intelligence in Education, 32(3), pp. 564–608. https://doi.org/10.1007/s40593-021-00254-2
Lienhart, R., and Maydt, J., 2002. An extended set of Haar-like features for rapid object detection. In Proceedings of the IEEE International Conference on Image Processing (ICIP), vol. 1, pp. I-900–I-903. https://doi.org/10.1109/ICIP.2002.1038171
Lin, T.Y., Goyal, P., Girshick, R., He, K., and Dollár, P., 2017. Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 2980–2988. https://doi.org/10.1109/ICCV.2017.324
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C., 2016. SSD: Single Shot MultiBox Detector. In Computer Vision - ECCV 2016, pp. 21–37. https://doi.org/10.1007/978-3-319-46448-0_2
Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M.G., Lee, J., Chang, W.-T., Hua, W., Georg, M., and Grundmann, M., 2019. MediaPipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172. https://doi.org/10.48550/arXiv.1906.08172
Murphy-Chutorian, E., and Trivedi, M.M., 2009. Head pose estimation in computer vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(4), pp. 607–626. https://doi.org/10.1109/TPAMI.2008.106
Ochoa, X., and Worsley, M., 2016. Editorial: Augmenting learning analytics with multimodal sensory data. Journal of Learning Analytics, 3(2), pp. 213–219. https://doi.org/10.18608/jla.2016.32.10
Pardo, A., and Siemens, G., 2014. Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), pp. 438–450. https://doi.org/10.1111/bjet.12152
Patacchiola, M., and Cangelosi, A., 2017. Head pose estimation in the wild using convolutional neural networks and adaptive gradient methods. Pattern Recognition, 71, pp. 132–143. https://doi.org/10.1016/j.patcog.2017.06.009
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A., 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788. https://doi.org/10.1109/CVPR.2016.91
Ren, S., He, K., Girshick, R., and Sun, J., 2017. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), pp. 1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031
Rowley, H.A., Baluja, S., and Kanade, T., 1998. Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1), pp. 23–38. https://doi.org/10.1109/34.655647
Rubel, A., and Jones, K.M.L., 2016. Student privacy in learning analytics: An information ethics perspective. The Information Society, 32(2), pp. 143–159. https://doi.org/10.1080/01972243.2016.1130502
Ruiz, N., Chong, E., and Rehg, J.M., 2018. Fine-grained head pose estimation without keypoints. In Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2155–2164. https://doi.org/10.1109/CVPRW.2018.00281
Siemens, G., and Baker, R.S.J.d., 2012. Learning analytics and educational data mining: Towards communication and collaboration. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK '12), pp. 252–254. https://doi.org/10.1145/2330601.2330661
Sinatra, G.M., Heddy, B.C., and Lombardi, D., 2015. The challenges of defining and measuring student engagement in science. Educational Psychologist, 50(1), pp. 1–13. https://doi.org/10.1080/00461520.2014.1002924
Slade, S., and Prinsloo, P., 2013. Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), pp. 1510–1529. https://doi.org/10.1177/0002764213479366
Sümer, Ö., Goldberg, P., D'Mello, S., Gerjets, P., Trautwein, U., and Kasneci, E., 2023. Multimodal engagement analysis from facial videos in the classroom. IEEE Transactions on Affective Computing, 14(2), pp. 1012–1027. https://doi.org/10.1109/TAFFC.2021.3127692
Sweeney, L., 2002. k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), pp. 557–570. https://doi.org/10.1142/S0218488502001648
Viola, P., and Jones, M., 2001. Rapid object detection using a boosted cascade of simple features. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1, pp. I-511–I-518. https://doi.org/10.1109/CVPR.2001.990517
Wheeb, A.H., and Khan, M.F., 2024. A survey of several machine learning (ML) algorithms for security solution in Internet of Things (IoT) networks. Journal of Artificial Intelligence Research & Advances, 12(1), pp. 1–11. https://journals.stmjournals.com/joaira/article=2024/view=191585/
Whitehill, J., Serpell, Z., Lin, Y.C., Foster, A., and Movellan, J.R., 2014. The faces of engagement: Automatic recognition of student engagement from facial expressions. IEEE Transactions on Affective Computing, 5(1), pp. 86–98. https://doi.org/10.1109/TAFFC.2014.2316163
Yang, M.-H., Kriegman, D.J., and Ahuja, N., 2002. Detecting faces in images: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(1), pp. 34–58. https://doi.org/10.1109/34.982883
Zhang, K., Zhang, Z., Li, Z., and Qiao, Y., 2016. Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Processing Letters, 23(10), pp. 1499–1503. https://doi.org/10.1109/LSP.2016.2603342