Abstract
In this paper, we speculate about the use of social robots as convenient tools for improving learning in an educational scenario. We introduce an experimental setup in which students listen a story read by a storyteller while their attention levels are monitored through electrophysiological and behavioral measures: if the participants are judged inattentive by an electroencephalogram based measure or by the head’s movements, a social robot will produce feedbacks to stimulate their attention to the shared task. We hypothesize that the participants will then realize their attention drop and will shift back their focus to the task, improving their learning. A comprehension questionnaire together with the score of a Narrative Transport Questionnaire, joined with the analysis of the collected electrophysiological data are explored to verify the effectiveness of this approach. First results with 16 adult students indicate how in learning scenarios social robots could act as a potential elements of distraction.
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
Notes
- 1.
Intel RealSense D435i: https://www.intelrealsense.com/depth-camera-d435i/.
- 2.
Enobio from NeuroElectrics: https://www.neuroelectrics.com/solutions/enobio.
- 3.
Nao robot from Softbank Robotics: https://www.softbankrobotics.com/emea/en/nao.
- 4.
ROS, Robot Operating System: https://wiki.ros.org/kinetic.
- 5.
Clochette is the story of a seamstress who becomes lame because of a man: https://librivox.org/short-story-collection-096-by-various/.
- 6.
Mediapipe library from Google: https://mediapipe.dev/.
- 7.
Neurosurfer from NeuroElectrics: https://www.neuroelectrics.com/wiki/index.php/MediaWiki:Neurofeedback-url.
- 8.
SciPy library: https://scipy.org/.
References
Alchalabi, A.E., Shirmohammadi, S., Eddin, A.N., Elsharnouby, M.: Focus: Detecting ADHD patients by an EEG-based serious game. IEEE Trans. Instrum. Measur. 67(7), 1512–1520 (2018)
American Psychological Association: APA dictionary of psychology, 2nd edn. American Psychological Association, Washington, D.C (2015)
Anzalone, S., Tanet, A., Pallanca, O., Cohen, D., Chetouani, M.: A humanoid robot controlled by neurofeedback to reinforce attention in autism spectrum disorder. In: Proceedings of the 3rd Italian Workshop on Artificial Intelligence and Robotics (2016)
Anzalone, S., Tanet, A., Pallanca, O., Cohen, D., Chetouani, M.: A humanoid robot controlled by neurofeedback to reinforce attention in autism spectrum disorder. In: Proceedings of the 3rd Italian Workshop on Artificial Intelligence and Robotics. A workshop of the XV International Conference of the Italian Association for Artificial Intelligence (AI*IA 2016) (2016)
Anzalone, S.M., Boucenna, S., Ivaldi, S., Chetouani, M.: Evaluating the engagement with social robots. Int. J. Soc. Robot. 7(4), 465–478 (2015)
Beatty, J., Legewie, H.: Biofeedback and Behavior: Introduction to the Proceedings, pp. 1–5. Springer, US, Boston, MA (1977), ISBN 978-1-4684-2526-0, https://doi.org/10.1007/978-1-4684-2526-0_1
Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F.: Social robots for education: a review. Sci. Robot. 3(21), eaat5954 (2018)
Bussalb, A., et al.: Clinical and experimental factors influencing the efficacy of neurofeedback in ADHD: a meta-analysis. Front. Psychiatry 10, 35 (2019)
Clarke, A.R., Barry, R.J., Karamacoska, D., Johnstone, S.J.: The EEG theta/beta ratio: a marker of arousal or cognitive processing capacity? Appl. Psychophysiol. Biofeedback 44(2), 123–129 (2019). https://doi.org/10.1007/s10484-018-09428-6
Coelli, S., Sclocco, R., Barbieri, R., Reni, G., Zucca, C., Bianchi, A.M.: EEG-based index for engagement level monitoring during sustained attention. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1512–1515 (2015)
Cortese, S., et al.: Neurofeedback for attention-deficit/hyperactivity disorder: meta-analysis of clinical and neuropsychological outcomes from randomized controlled trials. J. Am. Acad. Child Adolesc. Psychiatry 55(6), 444–455 (2016)
Dobrakowski, P., Łebecka, G.: Individualized neurofeedback training may help achieve long-term improvement of working memory in children with adhd. Clin. EEG Neurosci. 51(2), 94–101 (2020)
Donnermann, M., Schaper, P., Lugrin, B.: Integrating a social robot in higher education - a field study. In: 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 573–579 (2020)
Enriquez Geppert, S., Smit, D., Garcia Pimenta, M., Arns, M.: Neurofeedback as a treatment intervention in ADHD: current evidence and practice, June 2019
Feng, H., Gutierrez, A., Zhang, J., Mahoor, M.H.: Can NAO robot improve eye-gaze attention of children with high functioning autism? In: 2013 IEEE International Conference on Healthcare Informatics, pp. 484–484 (2013)
Furutani, R., Seino, Y., Tezuka, T., Satoh, T.: Monitoring the level of attention by posture measurement and EEG. In: Papafragou, A., Grodner, D., Mirman, D., Trueswell, J.C. (eds.) Proceedings of the 38th Annual Meeting of the Cognitive Science Society, Recogbizing and Representing Events, CogSci 2016, Philadelphia, PA, USA, 10–13 August 2016, cognitivesciencesociety.org (2016)
Goldberg, P., et al.: Attentive or Not? toward a machine learning approach to assessing students’ visible engagement in classroom instruction. Educ. Psychol. Rev. 33(1), 27–49 (2019). https://doi.org/10.1007/s10648-019-09514-z
Green, M., Brock, T.: Transport narrative questionnaire (2013)
Maeda, R., Even, J., Kanda, T.: Can a social robot encourage children’s self-study? In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1236–1242 (2019)
Manna, A., et al.: Neural correlates of focused attention and cognitive monitoring in meditation. Brain Res. Bull. 82(1), 46–56 (2010). ISSN 0361–9230
Mijović, P., Ković, V., Vos, M.D., Mačužić, I., Todorović, P., Jeremić, B., Gligorijević, I.: Towards continuous and real-time attention monitoring at work: reaction time versus brain response. Ergonomics 60(2), 241–254 (2017)
Müller, M., Gruber, T., Keil, A.: Modulation of induced gamma band activity in the human EEG by attention and visual information processing, January 2000
Nahaltahmasebi, P., Chetouani, M., Cohen, D., Anzalone, S.: Detecting attention breakdowns in robotic neurofeedback systems. In: Proceedings of the 4th Italian Workshop on Artificial Intelligence and Robotics. A workshop of the XVI International Conference of the Italian Association for Artificial Intelligence (AI*IA 2017) (2017)
Nasir, J., Bruno, B., Chetouani, M., Dillenbourg, P.: What if social robots look for productive engagement? Int. J. Soc. Robot. 14(1), 55–71 (2022)
Ogrim, G., Kropotov, J., Hestad, K.: The quantitative EEG theta/beta ratio in attention deficit/hyperactivity disorder and normal controls: Sensitivity, specificity, and behavioral correlates. Psychiatry Res. 198(3), 482–488 (2012). ISSN 0165–1781
Osanai, H., Kusumi, T.: Reliability and validity of the narrative transportation scale in Japanese. Japan. J. Pers. 25(1), 50–61 (2016)
Phyo Wai, A.A., Dou, M., Guan, C.: Generalizability of EEG-based mental attention modeling with multiple cognitive tasks. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 2959–2962 (2020)
Shamsuddin, S., Yussof, H., Ismail, L.I., Mohamed, S., Hanapiah, F.A., Zahari, N.I.: Initial response in HRI- a case study on evaluation of child with autism spectrum disorders interacting with a humanoid robot NAO. Procedia Engineering, vol. 41, pp. 1448–1455 (2012). ISSN 1877–7058, international Symposium on Robotics and Intelligent Sensors 2012 (IRIS 2012)
Slattery, E.J., O’Callaghan, E., Ryan, P., Fortune, D.G., McAvinue, L.P.: Popular interventions to enhance sustained attention in children and adolescents: a critical systematic review. Neurosci. Biobehav. Rev. 137, 104633 (2022). ISSN 0149–7634
Szulewski, A., Roth, N., Howes, D.: The use of task-evoked pupillary response as an objective measure of cognitive load in novices and trained physicians: a new tool for the assessment of expertise. Acad. Med. J. the Assoc. Am. Med. Colleges 90(7), 981–987 (2015)
Wang, K.J., Sugaya, M.: Focus and concentrate! exploring the use of conversational robot to improve self-learning performance during pandemic isolation by closed-loop brainwave neurofeedback. In: 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 928–932 (2021)
Weiss, A., Bartneck, C.: Meta analysis of the usage of the godspeed questionnaire series. In: 2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 381–388 (2015)
Whitton, N., Moseley, A.: Deconstructing engagement: rethinking involvement in learning. Simul. Gaming 45(4–5), 433–449 (2014)
Yi-Jung, L., Kang-Ming, C.: Improvement of attention in elementary school students through fixation focus training activity. Int. J. Environ. Res. Public Health 17, 4780 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Charpentier, S., Chetouani, M., Truck, I., Cohen, D., Anzalone, S.M. (2022). Social Robots in Learning Scenarios: Useful Tools to Improve Students’ Attention or Potential Sources of Distraction?. In: Cavallo, F., et al. Social Robotics. ICSR 2022. Lecture Notes in Computer Science(), vol 13818. Springer, Cham. https://doi.org/10.1007/978-3-031-24670-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-24670-8_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-24669-2
Online ISBN: 978-3-031-24670-8
eBook Packages: Computer ScienceComputer Science (R0)