Abstract
While using online learning software, students demonstrate many reactions, various levels of engagement, and emotions (e.g. confusion, excitement, frustration). Having such information automatically accessible to teachers (or digital tutors) can aid in understanding how students progress and suggest when and who needs further assistance. We developed the Affective Teacher Tools, a report card and dashboard that present teachers measures of students’ engagement and affective states as they use an online tutoring system, MathSpring.org, which supports students as they practice mathematics problem-solving at the middle school level. We conducted two development and research studies – one that assesses teachers perception of the affective report card and a second study that assesses a live affective dashboard, which senses students’ affect and performance in a live class that is using MathSpring. We use computer vision techniques to measure students’ engagement and affective states from their facial expressions while they use the tutoring system. In this paper, we summarize both the report card and affective dashboard, the research studies and results, and we also discuss implications, and future planned experiments for the next phase of this research.
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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). https://doi.org/10.1007/s40593-014-0023-y
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)
Bienkowski, M., Feng, M., Means, B.: Enhancing teaching and learning through educational data mining and learning analytics: an issue brief, October 2012. http://archvie2.cra.org/ccc/files/docs/learning-analytics-ed.pdf
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. Interact. Intell. Syst. (TiiS) 6(2), 1–26 (2016)
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., Lehman, B., Pekrun, R., Graesser, A.: Confusion can be beneficial for learning. Learn. Instr. 29, 153–170 (2014)
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)
Gupta, A.: Live Performance and Emotional Analysis of MathSpring Intelligent Tutor System Students. Master’s thesis, Worcester Polytechnic Institute (2020)
Heffernan, N., Heffernan, C.: The ASSISTments ecosystem: building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching. Int. J. Artif. Intell. Educ. 24, 470–497 (2014). https://doi.org/10.1007/s40593-014-0024-x
Holstein, K., McLaren, B.M., Aleven, V.: Student learning benefits of a mixed-reality teacher awareness tool in AI-enhanced classrooms. In: Penstein Rosé, C., et al. (eds.) AIED 2018. LNCS (LNAI), vol. 10947, pp. 154–168. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93843-1_12
Menon, N.: Improving User Interface and User Experience of MathSpring Intelligent Tutoring System for Teachers. Master’s thesis, Worcester Polytechnic Institute (2018)
Van Leeuwen, A., Rummel, N., Van Gog, T.: What information should CSCL teacher dashboards provide to help teachers interpret CSCL situations? Int. J. Comput.-Support. Collab. Learn 14, 261–289 (2019). https://doi.org/10.1007/s11412-019-09299-x
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)
Zatarain-Cabada, R., Barrón-Estrada, M.L., Camacho, J.L.O., Reyes-García, 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
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Gupta, A. et al. (2021). Affective Teacher Tools: Affective Class Report Card and Dashboard. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12748. Springer, Cham. https://doi.org/10.1007/978-3-030-78292-4_15
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