Abstract
Intrinsic motivation is the psychological construct that defines our reasons and interests to perform a set of actions. It has shown to be associated with positive outcomes across domains, especially in the academic context. Therefore, understanding and identifying peoples’ levels of intrinsic motivation can be crucial for professionals of many domains, e.g. teachers aiming to offer better support to students’ learning processes and enhance their academic outcomes. In a first attempt to tackle this issue, we propose an end-to-end approach for recognition of intrinsic motivation, using only facial expressions as input. Our results show that visual cues from students’ facial expressions are an important source of information to detect their levels of intrinsic motivation (AUC \(=0.570\), \(F_1=0.556\)). We also show how to avoid potential bias that might be present in datasets. When dividing the training samples per gender, we achieved a substantial improvement for both genders (AUC \(=0.739\) and \(F_1=0.852\) for male students, AUC \(=0.721\) and \(F_1=0.723\) for female students).
P. B. Santos and C. V. Bhowmik—Authors share equal contribution.
The FAZIT-STIFTUNG supported this work. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU and the TitanX Pascal GPU used for this research.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Azevedo, R., Millar, G., Taub, M., Mudrick, N., Bradbury, A., Price, M.: Using data visualizations to foster emotion regulation during self-regulated learning with advanced learning technologies: a conceptual framework. In: International Learning Analytics and Knowledge Conference, Vancouver, BC, Canada, pp. 444–448 (2017)
Deci, E.: Intrinsic motivation, extrinsic reinforcement, and inequity. J. Pers. Soc. Psychol. 22(1), 113–120 (1972)
D’Mello, S., Craig, S., Witherspoon, A., McDaniel, B., Graesser, A.: Automatic detection of learner’s affect from conversational cues. User Model. User-Adap. Interact. 18(1–2), 45–80 (2008). https://doi.org/10.1007/s11257-007-9037-6
D’Mello, S., Graesser, A.: Automatic detection of learner’s affect from gross body language. Appl. Artif. Intell. 23(2), 123–150 (2009)
D’Mello, S., Graesser, A.: Mind and body: dialogue and posture for affect detection in learning environments. In: International Conference on Artificial Intelligence in Education, Los Angeles, CA, USA, pp. 161–168 (2007)
Dwork, C., Immorlica, N., Kalai, A.T., Leiserson, M.: Decoupled classifiers for group-fair and efficient machine learning. In: Proceedings of the 1st Conference on Fairness, Accountability and Transparency, New York, NY, USA, vol. 81, pp. 119–133 (2018)
Frey, A., et al. (eds.): PISA 2006 Handbook of Scales. Documentation of Assessment Instruments (PISA 2006 Skalenhandbuch. Dokumentation der Erhebungsinstrumente). Waxmann (2009)
Johnson, W.L., Lester, J.C.: Face-to-face interaction with pedagogical agents, twenty years later. Int. J. Artif. Intell. Educ. 26(1), 25–36 (2015). https://doi.org/10.1007/s40593-015-0065-9
Kaur, A., Mustafa, A., Mehta, L., Dhall, A.: Prediction and localization of student engagement in the wild. CoRR abs/1804.00858 (2018)
Maehr, M.L., Meyer, H.A.: Understanding motivation and schooling: where we’ve been, where we are, and where we need to go. Educ. Psychol. Rev. 9(4), 371–409 (1997). https://doi.org/10.1023/A:1024750807365
Monkaresi, H., Bosch, N., Calvo, R., D’Mello, S.: Automated detection of engagement using video-based estimation of facial expressions and heart rate. IEEE Trans. Affect. Comput. 8(1), 15–28 (2017)
Rotgans, J., Schmidt, H.: Situational interest and academic achievement in the active-learning classroom. Learn. Instr. 21(1), 58–67 (2011)
Ryu, H., Mitchell, M., Adam, H.: Improving smiling detection with race and gender diversity. CoRR abs/1712.00193 (2017). https://arxiv.org/abs/1712.00193
Schiefele, U.: Interest, learning, and motivation. Educ. Psychol. 26(3–4), 299–323 (1991)
de Vicente, A., Pain, H.: Motivation diagnosis in intelligent tutoring systems. In: Goettl, B.P., Halff, H.M., Redfield, C.L., Shute, V.J. (eds.) ITS 1998. LNCS, vol. 1452, pp. 86–95. Springer, Heidelberg (1998). https://doi.org/10.1007/3-540-68716-5_14
de Vicente, A., Pain, H.: Informing the detection of the students’ motivational state: an empirical study. In: Cerri, S.A., Gouardères, G., Paraguaçu, F. (eds.) ITS 2002. LNCS, vol. 2363, pp. 933–943. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-47987-2_93
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)
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., Chang, K.W.: Men also like shopping: reducing gender bias amplification using corpus-level constraints. In: Empirical Methods in Natural Language Processing. pp. 2979–2989. Copenhagen, Denmark (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Santos, P.B., Bhowmik, C.V., Gurevych, I. (2020). Avoiding Bias in Students’ Intrinsic Motivation Detection. In: Kumar, V., Troussas, C. (eds) Intelligent Tutoring Systems. ITS 2020. Lecture Notes in Computer Science(), vol 12149. Springer, Cham. https://doi.org/10.1007/978-3-030-49663-0_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-49663-0_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-49662-3
Online ISBN: 978-3-030-49663-0
eBook Packages: Computer ScienceComputer Science (R0)