Abstract
In the proposed research, we investigated whether the standardized neuropsychological tests commonly used to assess attention can be used to measure students’ engagement in online learning settings. Accordingly, we employed 73 students in three clinically relevant neuropsychological tests to assess three types of attention. Students’ engagement performance, as evidenced by their facial video, was also annotated by three independent annotators. The manual annotations observed a high level of inter-annotator reliability (Krippendorffs’ Alpha of 0.864). Further, by obtaining a correlation value of 0.673 (Spearmans’ Rank Correlation) between manual annotation and neuropsychological tests score, our results show construct validity to prove neuropsychological test scores’ significance as a latent variable for measuring students’ engagement. Finally, using non-intrusive behavioral cues, including facial action unit and eye gaze data collected via webcam, we propose a machine learning method for engagement analysis in online learning settings, achieving a low mean squared error value (0.022). The findings suggest a neuropsychological test-based machine learning technique could effectively assess students’ engagement in online education.
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References
Amos, B., Ludwiczuk, B., Satyanarayanan, M.: Openface: A general-purpose face recognition library with mobile applications. Tech. Rep. CMU-CS-16-118, CMU School of Computer Science (2016)
De Leeuw, J.: Jspsych: a javascript library for creating behavioral experiments in a web browser. Behav. Res. Methods 47, 1–12 (2014)
Ghassemi, F., Moradi, M.H., Doust, M.T., Abootalebi, V.: Classification of sustained attention level based on morphological features of eeg’s independent components. In: 2009 ICME International Conference on Complex Medical Engineering (2009)
Goldberg, P., et al.: Attentive or not? toward a machine learning approach to assessing students’ visible engagement in classroom instruction. Educ. Psychol. Rev. 33, 27–49 (2019)
Hutt, S., Krasich, K., R. Brockmole, J., K. D’Mello, S.: Breaking out of the lab: Mitigating mind wandering with gaze-based attention-aware technology in classrooms. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2021)
Ko, L.W., Komarov, O., Hairston, W.D., Jung, T.P., Lin, C.T.: Sustained attention in real classroom settings: an eeg study. Front. Hum. Neurosci. 11, 388 (2017)
Lackmann, S., Léger, P.M., Charland, P., Aubé, C., Talbot, J.: The influence of video format on engagement and performance in online learning. Brain Sci. 11(2), 128 (2021)
Lai, Y.J., Chang, K.M.: Improvement of attention in elementary school students through fixation focus training activity. Int. J. Environ. Res. Public Health 17(13), 4780 (2020)
Linson, A., Xu, Y., English, A.R., Fisher, R.B.: Identifying student struggle by analyzing facial movement during asynchronous video lecture viewing: Towards an automated tool to support instructors. In: Lecture Notes in Computer Science, pp. 53–65 (2022)
Renninger, K.A., Bachrach, J.E.: Studying triggers for interest and engagement using observational methods. Educ. Psychol. 50, 58–69 (2015)
Sohlberg, M.K.M., Mateer, C.A.: Improving attention and managing attentional problems. Ann. N. Y. Acad. Sci. 931, 359–375 (2006)
Stevens, C., Bavelier, D.: The role of selective attention on academic foundations: a cognitive neuroscience perspective. Dev. Cogn. Neurosci. 2, S30–S48 (2012)
Szafir, D., Mutlu, B.: Pay attention! In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2012)
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, 86–98 (2014)
Wright, B.C.: What stroop tasks can tell us about selective attention from childhood to adulthood. Br. J. Psychol. 108(3), 583–607 (2017)
Zagermann, J., Pfeil, U., Reiterer, H.: Studying eye movements as a basis for measuring cognitive load. In: Extended Abstracts of the 2018 CHI Conference On Human Factors In Computing Systems, pp. 1–6 (2018)
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This research work is funded by a research grant (Ref. ID.: IHUB Anubhuti/Project Grant/03) of IHUB Anubhuti-IIITD Foundation and is partly supported by the Infosys Center for AI and the Center for Design and New Media (A TCS Foundation Initiative supported by Tata Consultancy Services) at IIIT-Delhi, India.
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Yadav, S., Siddiqui, M.N., Shukla, J. (2023). EngageMe: Assessing Student Engagement in Online Learning Environment Using Neuropsychological Tests. In: Wang, N., Rebolledo-Mendez, G., Dimitrova, V., Matsuda, N., Santos, O.C. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2023. Communications in Computer and Information Science, vol 1831. Springer, Cham. https://doi.org/10.1007/978-3-031-36336-8_23
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