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A Comprehensive Survey on Vision-Based Student Engagement Analysis: Models, Datasets, and Methods

Yixuan Liu, Yongying Xia, Xunan Wang, Renfei Huang, Yeqi Sun

Abstract


Student engagement assessment is shifting from manual observation to vision-based and deep learning approaches. This work revis
its the behavioralemotionalcognitive framework in relation to common datasets, and outlines key methods including facial analysis, pose
estimation, behavior recognition, and multimodal fusion. Challenges in real classrooms, such as occlusion, small targets, and limited data, are
also discussed to support more reliable and practical systems.

Keywords


Classroom participation; Behavior recognition;Attitude estimation; Multimodal fusion; Smart education

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References


[1] J. A. Fredricks, P. C. Blumenfeld, and A. H. Paris, "School engagement: Potential of the concept, state of the evidence, " Rev. Educ.

Res., vol. 74, no. 1,pp. 59109, 2004, doi: 10.3102/00346543074001059 .

[2] J. Liao et al., "Deep learning for student engagement recognition: A survey, " IEEE Access, vol. 9, pp. 165319165335, 2021, doi:

10.1109/ACCESS.2021.3134603.

[3] Y. Zhang et al., "Multi-modal student engagement recognition in the wild:A review, " Comput. Sci. Rev., vol. 48, p. 100551, 2023, doi:

10.1016/j.cosrev.2023.100551 .

[4] C. R. Henrie, L. R. Halverson, and C. R. Graham, "Measuring student engagement in technology-mediated learning:A review, " Com

put. Educ., vol. 90,pp. 3653, 2015.

[5] M.A.A. Dewan, M. Murshed, and F. Lin, "Engagement detection in online learning: a review, " Smart Learn. Environ., vol. 6, no. 1,pp.

120, 2019, doi: 10.1186/s40561-018-0080-z.

[6] R. Pekrun, "The control-value theory of achievement emotions:Assumptions, corollaries, and implications for educational research and

practice, " Educ. Psychol. Rev., vol. 18, no. 4,pp. 315341, 2006, doi: 10.1007/s10648-006-9029-9 .

[7] S. K. D'Mello and A. Graesser, "Dynamics of affective states during complex learning, " Learn. Instr., vol. 22, no. 2, pp. 145157, 2012,

doi: 10.1016/j.learninstruc.2011.10.001 .

[8] M. K. Abadi et al., "User-centric engagement monitoring in the wild, " IEEE Trans. Affect. Comput., vol. 6, no. 4, pp. 332344, 2015,

doi: 10.1109/TAFFC.2015.2419672.

[9] J. Whitehill, Z. Serpell, Y. C. Lin, A. Foster, and J. R. Movellan, "The faces of engagement: Automatic recognition of student engage

ment from facial expressions, " IEEE Trans.Affect. Comput., vol. 5, no. 1,pp. 8698, 2014.

[10] A. Gupta,A. Dhall, R. Subramanian, T. Gedeon, and M. Bowden, "DAiSEE: Towards user engagement recognition in the wild, " IEEE

Trans.Affect. Comput., vol. 8, no. 3,pp. 338351, 2017, doi: 10.1109/TAFFC.2017.2736028 .

[11] A. Dhall,A. Kaur, R. Goecke, and T. Gedeon, "Predicting student engagement in the wild: The EmotiW 2020 challenge, " in Proc. Int.

Conf. Multimodal Interact. (ICMI), 2020,pp. 567571, doi: 10.1145/3382507.3418869 .

[12] Z. Liu,Y. Zhang, and H. Li, "SCB:A large-scale dataset and benchmark for student classroom behavior analysis, " Sensors, vol. 21, no.

15,p. 5146, 2021, doi: 10.3390/s21155146 .

[13] S. Zhang et al., "Learning to detect human head pose and engagement in the wild, " Image Vis. Comput., vol. 96,p. 103892, 2020.

[14] A. Raza et al., "A deep learning approach for student engagement detection in smart classrooms, " IEEE Trans. Learn. Technol., vol. 17,

pp. 456470, 2024, doi: 10.1109/TLT.2023.3323055 .

[15] S. K. D'Mello and J. Kory, "A review and meta-analysis of multimodal affect detection systems, " ACM Comput. Surv., vol. 47, no. 3,

pp. 136, 2015.

[16] K. Selvaraj et al., "Comprehensive survey on computer vision-based student engagement analysis in smart classrooms, " IEEE Access,

vol. 10,pp. 1123411256, 2022, doi: 10.1109/ACCESS.2022.3144578 .




DOI: http://dx.doi.org/10.70711/aitr.v3i10.9206

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