An Investigation of Pre-Service Teachers Using Mobile and Wearable Devices for Emotion Recognition and Social Sharing of Emotion to Support Emotion Regulation in mCSCL Environments

Suthanit Wetcho; Jaitip Na-Songkhla

doi:10.30935/cedtech/11668

Suthanit Wetcho ¹, Jaitip Na-Songkhla ¹ ^*

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CONT ED TECHNOLOGY, Volume 14, Issue 2, Article No: ep359

https://doi.org/10.30935/cedtech/11668

Submitted: 17 November 2021, Published Online: 02 February 2022

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Abstract

In the era of a workforce driven by automation and artificial intelligence, social and emotional skills are becoming increasingly relevant to online learning environments. Since social-emotional learning may be defined as a vital component of the learning process in professional instructional design practices, online learners not only need to develop the ability to apply their knowledge, attitudes, and skills but also to understand and manage their emotions. In which setting and achieving positive goals through social interaction, sharing feelings, and developing empathy for others can help with the process. This paper outlines the possibility of using emotion recognition, and social sharing of emotion techniques to support the regulation of emotion in pre-service teacher education. This study aimed to investigate pre-service teachers’ emotion recognition tools acquired by emotion tracker and physiological signals based on their perceptions (without a concrete experience and knowledge). Moreover, the predictive ability was examined along with the relationships between emotion recognition, social sharing of emotion, and emotion regulation. Finally, we investigated emotion adjustment techniques that can be adapted into mobile computer-supported collaborative learning (mCSCL). In this study, 183 pre-service teachers from three different teacher-education institutions in Thailand, were voluntarily participated based on convenience sampling. The results of a self-report via online survey revealed that most pre-service teachers own at least one of the mobile technologies e.g., smartphones, tablets, or laptops. However, there is an increasing number of additional gadgets and wearable devices like EarPods and smartwatches. At the current time, it is nearly impossible to use of the IoT and other wearable devices. According to their subjective impressions in which corresponded to emotion recognition in the scientific literature, Heart rate (HR) and Heart rate variability (HRV) have recognized the most possibilities for emotion detection among physiological signals. Regarding regression analysis, the two-predictor models of emotion recognition and the social sharing of emotion were also able to account for 31% of the variance in emotion regulation, p<.001, R2=.31, and 95% CI [.70, .77]. In addition, the mCSCL applications and the importance of these variables in different collaboration levels are also discussed.

Keywords: social emotional learning, emotion recognition, social sharing of emotion, emotion regulation, mCSCL

References

Abramson, L., Petranker, R., Marom, I., & Aviezer, H. (2020). Social interaction context shapes emotion recognition through body language, not facial expressions. Emotion, 21(3), 557-568. https://doi.org/10.1037/emo0000718
Augustsson, G. (2010). Web 2.0, pedagogical support for reflexive and emotional social interaction among Swedish students. The Internet and Higher Education, 13(4), 197-205. https://doi.org/10.1016/j.iheduc.2010.05.005
Bucich, M., & MacCann, C. (2019). Emotional intelligence and day-to-day emotion regulation processes: Examining motives for social sharing. Personality and Individual Differences, 137, 22-26. https://doi.org/10.1016/j.paid.2018.08.002
Butler, E. A., & Gross, J. J. (2009). Emotion and emotion regulation: Integrating individual and social levels of analysis. Emotion Review, 1(1), 86-87. https://doi.org/10.1177/1754073908099131
Carstensen, L. L. (1992). Social and emotional patterns in adulthood: Support for socioemotional selectivity theory. Psychology and Aging, 7(3), 331-338. https://doi.org/10.1037/0882-7974.7.3.331
Carstensen, L. L., Fung, H. H., & Charles, S. T. (2003). Socioemotional selectivity theory and the regulation of emotion in the second half of life. Motivation and Emotion, 27(2), 103-123. https://doi.org/10.1023/a:1024569803230
Castillo, J. C., Fernández-Caballero, A., Castro-González, Á., Salichs, M. A., & López, M. T. (2014). A framework for recognizing and regulating emotions in the elderly. In L. Pecchia, L.L. Chen, C. Nugent, & J. Bravo (Eds.), Ambient assisted living and daily activities (pp. 320-327). Springer. https://doi.org/10.1007/978-3-319-13105-4_46
Connolly, H. L., Lefevre, C. E., Young, A. W., & Lewis, G. J. (2020). Emotion recognition ability: Evidence for a supramodal factor and its links to social cognition. Cognition, 197, 104166. https://doi.org/10.1016/j.cognition.2019.104166
Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., & Taylor, J. G. (2001). Emotion recognition in human-computer interaction. IEEE Signal Processing Magazine, 18(1), 32-80. https://doi.org/10.1109/79.911197
Dores, A. R., Barbosa, F., Queirós, C., Carvalho, I. P., & Griffiths, M. D. (2020). Recognizing emotions through facial expressions: A largescale experimental study. International Journal of Environmental Research and Public Health, 17(20), 7420. https://doi.org/10.3390/ijerph17207420
Dudley, N. M., & Multhaup, K. S. (2005). When familiar social partners are selected in open-ended situations: Further tests of the socioemotional selectivity theory. Experimental Aging Research, 31(3), 331-344. https://doi.org/10.1080/03610730590948212
Egger, M., Ley, M., & Hanke, S. (2019). Emotion recognition from physiological signal analysis: A review. Electronic Notes in Theoretical Computer Science, 343, 35-55. https://doi.org/10.1016/j.entcs.2019.04.009
Fong, C. J., Williams, K. M., Williamson, Z. H., Lin, S., Kim, Y. W., & Schallert, D. L. (2018). “Inside out”: Appraisals for achievement emotions from constructive, positive, and negative feedback on writing. Motivation and Emotion, 42(2), 236-257. https://doi.org/10.1007/s11031-017-9658-y
Frey, B. B., Lohmeier, J. H., Lee, S. W., & Tollefson, N. (2006). Measuring collaboration among grant partners. American Journal of Evaluation, 27(3), 383-392. https://doi.org/10.1177/1098214006290356
Garnefski, N., & Kraaij, V. (2006). Cognitive emotion regulation questionnaire-development of a short 18-item version (CERQ-short). Personality and Individual Differences, 41(6), 1045-1053. https://doi.org/10.1016/j.paid.2006.04.010
Gross, J. J. (2015). The extended process model of emotion regulation: Elaborations, applications, and future directions. Psychological Inquiry, 26(1), 130-137. https://doi.org/10.1080/1047840X.2015.989751
Guo, H., Huang, Y., Lin, C., Chien, J., Haraikawa, K., & Shieh, J. (2016, 31 Oct.-2 Nov. 2016). Heart rate variability signal features for emotion recognition by using principal component analysis and support vectors machine [Paper presentation]. 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, Taichung, Taiwan. https://doi.org/10.1109/BIBE.2016.40
Heberle, A. E., Thomann, C. R. B., & Carter, A. S. (2020). Social and emotional development theories. Elsevier. https://doi.org/10.1016/B978-0-12-809324-5.23633-X
Hofmann, S. G., Carpenter, J. K., & Curtiss, J. (2016). Interpersonal emotion regulation questionnaire (IERQ): Scale development and psychometric characteristics. Cognitive Therapy and Research, 40(3), 341-356. https://doi.org/10.1007/s10608-016-9756-2
Hori, S., Mori, K., Mashimo, T., & Seiyama, A. (2017). Effects of light and sound on the prefrontal cortex activation and emotional function: A functional near-infrared spectroscopy study. Frontiers in Neuroscience, 11, 321. https://doi.org/10.3389/fnins.2017.00321
Hossain, M. S., & Muhammad, G. (2017). An emotion recognition system for mobile applications. IEEE Access, 5, 2281-2287. https://doi.org/10.1109/ACCESS.2017.2672829
Hsu, Y.-C., & Ching, Y.-H. (2013). Mobile computer-supported collaborative learning: A review of experimental research. British Journal of Educational Technology, 44(5), E111-E114. https://doi.org/10.1111/bjet.12002
Järvenoja, H., & Järvelä, S. (2009). Emotion control in collaborative learning situations: Do students regulate emotions evoked by social challenges. British Journal of Educational Psychology, 79(3), 463-481. https://doi.org/10.1348/000709909x402811
Jeong, H., Hmelo-Silver, C. E., & Jo, K. (2019). Ten years of computer-supported collaborative learning: A meta-analysis of CSCL in STEM education during 2005-2014. Educational Research Review, 28, 100284. https://doi.org/10.1016/j.edurev.2019.100284
Jerčić, P., & Sundstedt, V. (2019). Practicing emotion-regulation through biofeedback on the decision-making performance in the context of serious games: A systematic review. Entertainment Computing, 29, 75-86. https://doi.org/10.1016/j.entcom.2019.01.001
Kearney, M., & Maher, D. (2019). Mobile learning in pre-service teacher education: Examining the use of professional learning networks. Australasian Journal of Educational Technology, 35(1), 135-148. https://doi.org/10.14742/ajet.4073
Keller, M. M., & Becker, E. S. (2020). Teachers’ emotions and emotional authenticity: do they matter to students’ emotional responses in the classroom? Teachers and Teaching, 27(5), 404-422. https://doi.org/10.1080/13540602.2020.1834380
Kołakowska, A., Landowska, A., Szwoch, M., Szwoch, W., & Wróbel, M. R. (2014). Emotion recognition and its applications. In Z. S. Hippe, J. L. Kulikowski, T. Mroczek, & J. Wtorek (Eds.), Human-computer systems interaction: Backgrounds and applications 3 (pp. 51-62). Springer. https://doi.org/10.1007/978-3-319-08491-6_5
Kołakowska, A., Szwoch, W., & Szwoch, M. (2020). A review of emotion recognition methods based on data acquired via smartphone sensors. Sensors, 20(21), 6367. https://doi.org/10.3390/s20216367
Liu, C., Wan, P., Hwang, G.-J., Tu, Y.-F., & Wang, Y. (2021). From competition to social interaction: A mobile team-based competition approach to promoting students’ professional identity and perceptions. Interactive Learning Environments, 1-15. https://doi.org/10.1080/10494820.2020.1823855
Ludvigsen, S. (2016). CSCL: Connecting the social, emotional and cognitive dimensions. International Journal of Computer-Supported Collaborative Learning, 11(2), 115-121. https://doi.org/10.1007/s11412-016-9236-4
Ludvigsen, S., & Steier, R. (2019). Reflections and looking ahead for CSCL: Digital infrastructures, digital tools, and collaborative learning. International Journal of Computer-Supported Collaborative Learning, 14(4), 415-423. https://doi.org/10.1007/s11412-019-09312-3
Lyusin, D., & Ovsyannikova, V. (2016). Measuring two aspects of emotion recognition ability: Accuracy vs. sensitivity. Learning and Individual Differences, 52, 129-136. https://doi.org/10.1016/j.lindif.2015.04.010
Mayer, J. D., Caruso, D. R., & Salovey, P. (2016). The ability model of emotional intelligence: Principles and updates. Emotion Review, 8(4), 290-300. https://doi.org/10.1177/1754073916639667
Miller, M., & Hadwin, A. (2015). Scripting and awareness tools for regulating collaborative learning: Changing the landscape of support in CSCL. Computers in Human Behavior, 52, 573-588. https://doi.org/10.1016/j.chb.2015.01.050
Molinari, G., Chanel, G., Betrancourt, M., Pun, T., & Bozelle Giroud, C. (2013). Emotion feedback during computer-mediated collaboration: Effects on self-reported emotions and perceived interaction. In N. Rummel, M. Kapur, M. Nathan, & S. Puntambekar (Eds.), To see the world and a grain of sand: Learning across levels of space, time, and scale: CSCL 2013 conference proceedings volume 1-Full papers & symposia (pp. 336-343). Madison, WI, USA.
Nasoz, F., Alvarez, K., Lisetti, C., & Finkelstein, N. (2003). Emotion recognition from physiological signals for presence technologies. International Journal of Cognition, Technology, and Work-Special Issue on Presence, 6(1), 1-32. https://doi.org/10.1007/s10111-003-0143-x
National Statistical Office. (2020). The use of ICT among the people of Thailand in 2020. http://www.nso.go.th/sites/2014/Lists/Infographic/Attachments/101/Infographic_ICT63.pdf
Näykki, P., Isohätälä, J., Järvelä, S., Pöysä-Tarhonen, J., & Häkkinen, P. (2017). Facilitating socio-cognitive and socio-emotional monitoring in collaborative learning with a regulation macro script-an exploratory study. International Journal of Computer-Supported Collaborative Learning, 12(3), 251-279. https://doi.org/10.1007/s11412-017-9259-5
Niven, K., Totterdell, P., Stride, C. B., & Holman, D. (2011). Emotion regulation of others and self (EROS): The development and validation of a new individual difference measure. Current Psychology, 30, 53-73. https://doi.org/10.1007/s12144-011-9099-9
Papoutsi, C., & Drigas, A. (2017). Empathy and mobile applications. International Journal of Interactive Mobile Technologies, 11(3), 57-66. https://doi.org/10.3991/ijim.v11i3.6385
Preece, D. A., Becerra, R., Robinson, K., Dandy, J., & Allan, A. (2018). Measuring emotion regulation ability across negative and positive emotions: The Perth emotion regulation competency inventory (PERCI). Personality and Individual Differences, 135, 229-241. https://doi.org/10.1016/j.paid.2018.07.025
Resta, P., & Laferrière, T. (2007). Technology in support of collaborative learning. Educational Psychology Review, 19(1), 65-83. https://doi.org/10.1007/s10648-007-9042-7
Rimé, B. (2009). Emotion elicits the social sharing of emotion: Theory and empirical review. Emotion Review, 1(1), 60-85. https://doi.org/10.1177/1754073908097189
Rimé, B. (2017). The social sharing of emotion in interpersonal and in collective situations. In J. A. Holyst (Ed.), Cyberemotions: Collective emotions in cyberspace (pp. 53-69). Springer. https://doi.org/10.1007/978-3-319-43639-5_4
Rimé, B., Finkenauer, C., Luminet, O., Zech, E., & Philippot, P. (1998). Social sharing of emotion: New evidence and new questions. European Review of Social Psychology, 9(1), 145-189. https://doi.org/10.1080/14792779843000072
Rodríguez Hidalgo, C. T., Tan, E. S. H., & Verlegh, P. W. J. (2015). The social sharing of emotion (SSE) in online social networks: A case study in Live Journal. Computers in Human Behavior, 52, 364-372. https://doi.org/10.1016/j.chb.2015.05.009
Salovey, P., & Mayer, J. D. (1990). Emotional intelligence. Imagination, Cognition, and Personality, 9(3), 185-211. https://doi.org/10.2190/DUGG-P24E-52WK-6CDG
Sarprasatham, M. (2015). Emotion recognition: A survey. International Journal of Advanced Research in Computer Science, 3, 14-19.
Scherer, K. R., & Scherer, U. (2011). Assessing the ability to recognize facial and vocal expressions of emotion: Construction and validation of the emotion recognition index. Journal of Nonverbal Behavior, 35(4), 305. https://doi.org/10.1007/s10919-011-0115-4
Scherr, S. A., Polst, S., Müller, L., Holl, K., & Elberzhager, F. (2019). The perception of emojis for analyzing app feedback. International Journal of Interactive Mobile Technologies, 13(2), 19-36. https://doi.org/10.3991/ijim.v13i02.8492
Schutte, N. S., Malouff, J. M., Hall, L. E., Haggerty, D. J., Cooper, J. T., Golden, C. J., & Dornheim, L. (1998). Development and validation of a measure of emotional intelligence. Personality and Individual Differences, 25(2), 167-177. https://doi.org/10.1016/S0191-8869(98)00001-4
Seneviratne, S., Hu, Y., Nguyen, T., Lan, G., Khalifa, S., Thilakarathna, K., Hassan, M., & Seneviratne, A. (2017). A survey of wearable devices and challenges. IEEE Communications Surveys & Tutorials, 19(4), 2573-2620. https://doi.org/10.1109/COMST.2017.2731979
Shu, L., Xie, J., Yang, M., Li, Z., Li, Z., Liao, D., Xu, X., & Yang, X. (2018). A review of emotion recognition using physiological signals. Sensors, 18(7), 2074. https://doi.org/10.3390/s18072074
Shu, L., Yu, Y., Chen, W., Hua, H., Li, Q., Jin, J., & Xu, X. (2020). Wearable emotion recognition using heart rate data from a smart bracelet. Sensors, 20(3), 718. https://doi.org/10.3390/s20030718
Sung, Y.-T., Yang, J.-M., & Lee, H.-Y. (2017). The effects of mobile-computer-supported collaborative learning: Meta-analysis and critical synthesis. Review of Educational Research, 87(4), 768-805. https://doi.org/10.3102/0034654317704307
Suthers, D. D. (2012). Computer-supported collaborative learning. In N. M. Seel (Ed.), Encyclopedia of the sciences of learning (pp. 719-722). Springer. https://doi.org/10.1007/978-1-4419-1428-6_389
Thompson, R. A., Meyer, S., & Jochem, R. (2008). Emotion regulation. In M. M. Haith, & J. B. Benson (Eds.), Encyclopedia of infant and early childhood development (pp. 431-441). Academic Press. https://doi.org/10.1016/B978-012370877-9.00055-4
Zaki, J., & Williams, W. C. (2013). Interpersonal emotion regulation. Emotion, 13(5), 803-810. https://doi.org/10.1037/a0033839
Zelkowitz, R. L., & Cole, D. A. (2016). Measures of emotion reactivity and emotion regulation: Convergent and discriminant validity. Personality and Individual Differences, 102, 123-132. https://doi.org/10.1016/j.paid.2016.06.045

Citation

Wetcho, S., & Na-Songkhla, J. (2022). An Investigation of Pre-Service Teachers Using Mobile and Wearable Devices for Emotion Recognition and Social Sharing of Emotion to Support Emotion Regulation in mCSCL Environments. Contemporary Educational Technology, 14(2), ep359. https://doi.org/10.30935/cedtech/11668

Wetcho, S., and Na-Songkhla, J. (2022). An Investigation of Pre-Service Teachers Using Mobile and Wearable Devices for Emotion Recognition and Social Sharing of Emotion to Support Emotion Regulation in mCSCL Environments. Contemporary Educational Technology, 14(2), ep359. https://doi.org/10.30935/cedtech/11668

Wetcho S, Na-Songkhla J. An Investigation of Pre-Service Teachers Using Mobile and Wearable Devices for Emotion Recognition and Social Sharing of Emotion to Support Emotion Regulation in mCSCL Environments. CONT ED TECHNOLOGY. 2022;14(2), ep359. https://doi.org/10.30935/cedtech/11668

Wetcho, Suthanit, and Jaitip Na-Songkhla. "An Investigation of Pre-Service Teachers Using Mobile and Wearable Devices for Emotion Recognition and Social Sharing of Emotion to Support Emotion Regulation in mCSCL Environments". Contemporary Educational Technology 2022 14 no. 2 (2022): ep359. https://doi.org/10.30935/cedtech/11668

Wetcho, Suthanit et al. "An Investigation of Pre-Service Teachers Using Mobile and Wearable Devices for Emotion Recognition and Social Sharing of Emotion to Support Emotion Regulation in mCSCL Environments". Contemporary Educational Technology, vol. 14, no. 2, 2022, ep359. https://doi.org/10.30935/cedtech/11668

Wetcho S, Na-Songkhla J. An Investigation of Pre-Service Teachers Using Mobile and Wearable Devices for Emotion Recognition and Social Sharing of Emotion to Support Emotion Regulation in mCSCL Environments. CONT ED TECHNOLOGY. 2022;14(2):ep359. https://doi.org/10.30935/cedtech/11668