Abstract
While using online learning software, students demonstrate many reactions, various levels of engagement, and emotions (e.g. confusion, boredom, excitement). Having such information automatically accessible to teachers (or digital tutors) can aid in understanding how students are progressing, and suggest who and when needs further assistance. As part of this work, we conducted two studies using computer vision techniques to measure students’ engagement and affective states from their head pose and facial expressions, as they use an online tutoring system, MathSpring.org, designed to aid students’ practice of mathematics problem-solving. We present a Head Pose Tutor, which estimates the real-time head direction of students and responds to potential disengagement, and a Facial Expression-Augmented Teacher Dashboard, that identifies students’ affective states and provides this information to teachers. We collected video data of undergraduate students interacting with MathSpring. Preliminary results on MathSpring videos were encouraging indicating accuracy in detecting head orientation. A usability study was conducted with actual teachers to start to evaluate the possible impact of the proposed Teacher Dashboard software.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arroyo, I., Woolf, B.P., Burelson, W., Muldner, K., Rai, D., Tai, M.: A multimedia adaptive tutoring system for mathematics that addresses cognition, metacognition and affect. Int. J. Artif. Intell. Educ. 24(4), 387–426 (2014)
Baker, R.S., D’Mello, S.K., Rodrigo, M.M.T., Graesser, A.C.: 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. Int. J. Hum.-Comput. Stud. 68(4), 223–241 (2010)
Bosch, N., D’mello, S.K., Ocumpaugh, J., Baker, R.S., Shute, V.: Using video to automatically detect learner affect in computer-enabled classrooms. ACM Trans. Inter. Intell. Syst. (TiiS) 6(2), 1–26 (2016)
Bulat, A., Tzimiropoulos, G.: How far are we from solving the 2d & 3d face alignment problem?(and a dataset of 230,000 3D facial landmarks). In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1021–1030 (2017)
Chang, F.J., Tuan Tran, A., Hassner, T., Masi, I., Nevatia, R., Medioni, G.: Faceposenet: making a case for landmark-free face alignment. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 1599–1608 (2017)
Corrigan, S., Barkley, T., Pardos, Z.: Dynamic approaches to modeling student affect and its changing role in learning and performance. In: Ricci, F., Bontcheva, K., Conlan, O., Lawless, S. (eds.) UMAP 2015. LNCS, vol. 9146, pp. 92–103. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20267-9_8
D’Mello, S., Dieterle, E., Duckworth, A.: Advanced, analytic, automated (AAA) measurement of engagement during learning. Educ. Psychol. 52(2), 104–123 (2017)
D’Mello, S., Olney, A., Williams, C., Hays, P.: Gaze tutor: a gaze-reactive intelligent tutoring system. Int. J. Hum.-Comput. Stud. 70(5), 377–398 (2012)
D’Mello, S., Lehman, B., Pekrun, R., Graesser, A.: Confusion can be beneficial for learning. Learn. Instr. 29, 153–170 (2014)
D’Mello, S.K.: Gaze-based attention-aware cyberlearning technologies. In: Parsons, T.D., Lin, L., Cockerham, D. (eds.) Mind, Brain and Technology. ECTII, pp. 87–105. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-02631-8_6
Ekman, P., Friesen, W.V., Hager, J.C.: Facial action coding system. Research Nexus, Salt Lake City (2002)
Ekman, P., Friesen, W.V.: Constants across cultures in the face and emotion. J. Pers. Soc. Psychol. 17(2), 124 (1971)
Fanelli, G., Weise, T., Gall, J., Van Gool, L.: Real time head pose estimation from consumer depth cameras. In: Mester, R., Felsberg, M. (eds.) DAGM 2011. LNCS, vol. 6835, pp. 101–110. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23123-0_11
Gou, C., Wu, Y., Wang, F.Y., Ji, Q.: Coupled cascade regression for simultaneous facial landmark detection and head pose estimation. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 2906–2910. IEEE (2017)
Grafsgaard, J.F., Wiggins, J.B., Vail, A.K., Boyer, K.E., Wiebe, E.N., Lester, J.C.: The additive value of multimodal features for predicting engagement, frustration, and learning during tutoring. In: Proceedings of the 16th International Conference on Multimodal Interaction, pp. 42–49 (2014)
Hoffman, J.E., Subramaniam, B.: The role of visual attention in saccadic eye movements. Percept. Psychophysics. 57(6), 787–795 (1995)
Hu, Y., Chen, L., Zhou, Y., Zhang, H.: Estimating face pose by facial asymmetry and geometry. In: Proceedings of Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004, pp. 651–656. IEEE (2004)
Hutt, S., Mills, C., Bosch, N., Krasich, K., Brockmole, J., D’mello, S.: Out of the fr-eye-ing pan towards gaze-based models of attention during learning with technology in the classroom. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, pp. 94–103 (2017)
Khan, A.Z., Blohm, G., McPeek, R.M., Lefevre, P.: Differential influence of attention on gaze and head movements. J. Neurophysiol. 101(1), 198–206 (2009)
Khorrami, P., Paine, T., Huang, T.: Do deep neural networks learn facial action units when doing expression recognition? In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 19–27 (2015)
Kumar, A., Alavi, A., Chellappa, R.: Kepler: keypoint and pose estimation of unconstrained faces by learning efficient H-CNN regressors. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), pp. 258–265. IEEE (2017)
Martins, P., Batista, J.: Accurate single view model-based head pose estimation. In: 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition, pp. 1–6. IEEE (2008)
Meng, Z., Liu, P., Cai, J., Han, S., Tong, Y.: Identity-aware convolutional neural network for facial expression recognition. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), pp. 558–565. IEEE (2017)
Mukherjee, S.S., Robertson, N.M.: Deep head pose: Gaze-direction estimation in multimodal video. IEEE Trans. Multimedia. 17(11), 2094–2107 (2015)
Ranjan, R., Patel, V.M., Chellappa, R.: Hyperface: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE Trans. Pattern Anal. Mach. Intell. 41(1), 121–135 (2017)
Rayner, K.: Eye movements in reading and information processing: 20 years of research. Psychol. Bull. 124(3), 372 (1998)
Ruiz, N., Chong, E., Rehg, J.M.: Fine-grained head pose estimation without keypoints. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 2074–2083 (2018)
Shan, C., Gong, S., McOwan, P.W.: Facial expression recognition based on local binary patterns: A comprehensive study. Image Vis. Comput. 27(6), 803–816 (2009)
Sharma, K., Alavi, H.S., Jermann, P., Dillenbourg, P.: A gaze-based learning analytics model: in-video visual feedback to improve learner’s attention in MOOCs. In: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, pp. 417–421 (2016)
Whitehill, J., Serpell, Z., Lin, Y.C., Foster, A., Movellan, J.R.: The faces of engagement: automatic recognition of student engagement from facial expressions. IEEE Trans. Affect. Comput. 5(1), 86–98 (2014)
Wixon, M., Arroyo, I.: When the question is part of the answer: examining the impact of emotion self-reports on student emotion. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, G.-J. (eds.) UMAP 2014. LNCS, vol. 8538, pp. 471–477. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08786-3_42
Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., Picard, R.: Affect-aware tutors: recognising and responding to student affect. Int. J. Learn. Technol. 4(3–4), 129–164 (2009)
Yang, T.Y., Chen, Y.T., Lin, Y.Y., Chuang, Y.Y.: FSA-net: learning fine-grained structure aggregation for head pose estimation from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1087–1096 (2019)
Zatarain-Cabada, R., BarrĂłn-Estrada, M.L., Camacho, J.L.O., Reyes-GarcĂa, C.A.: Affective tutoring system for android mobiles. In: Huang, D.-S., Jo, K.-H., Wang, L. (eds.) ICIC 2014. LNCS (LNAI), vol. 8589, pp. 1–10. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09339-0_1
Zhang, F., Zhang, T., Mao, Q., Xu, C.: Joint pose and expression modeling for facial expression recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3359–3368 (2018)
Zhi, R., Flierl, M., Ruan, Q., Kleijn, W.B.: Graph-preserving sparse nonnegative matrix factorization with application to facial expression recognition. IEEE Trans. Syst. Man Cybern. B Cybern. 41(1), 38–52 (2010)
Zhong, L., Liu, Q., Yang, P., Liu, B., Huang, J., Metaxas, D.N.: Learning active facial patches for expression analysis. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2562–2569. IEEE (2012)
Zhu, X., Lei, Z., Liu, X., Shi, H., Li, S.Z.: Face alignment across large poses: a 3D solution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 146–155 (2016)
Zhu, X., Liu, X., Lei, Z., Li, S.Z.: Face alignment in full pose range: a 3D total solution. IEEE Trans. Pattern Anal. Mach. Intell. 41(1), 78–92 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Yu, H. et al. (2021). Measuring and Integrating Facial Expressions and Head Pose as Indicators of Engagement and Affect in Tutoring Systems. In: Sottilare, R.A., Schwarz, J. (eds) Adaptive Instructional Systems. Adaptation Strategies and Methods. HCII 2021. Lecture Notes in Computer Science(), vol 12793. Springer, Cham. https://doi.org/10.1007/978-3-030-77873-6_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-77873-6_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77872-9
Online ISBN: 978-3-030-77873-6
eBook Packages: Computer ScienceComputer Science (R0)