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Exploring emotional and cognitive dynamics of Knowledge Building in grades 1 and 2

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Abstract

Emotions have a powerful effect on learning but results regarding the nature of the impact are inconsistent and little is known about effects with young students, as participants are usually university students. This study aims to explore the emotional and cognitive dynamics of young students in both online and offline Knowledge Building. Classroom transcripts and online discourse collected for 45 grade 1 and 2 students over seven to 8 weeks were analyzed. Based on the total number of spoken and written words, the participants were classified into high- and low-participation groups. Multimodal learning analytics including speech emotion analysis, sentiment analysis, and idea improvement analysis were used in a mixed method research design incorporating co-occurrence patterns of emotions and idea improvement of students at different participation levels. High-participation students expressed significantly higher frequencies of emotions recorded as neutrality, joy, curiosity, and confidence compared to low-participation students. High-participation students were more likely to elaborate reasons, describe relationships and mechanisms surrounding ideas they explored, and to introduce new ideas and concepts into community resources. Surprise, challenge, and neutrality can be beneficial since students tended to express these emotions when producing explanation-seeking questions, new ideas, explanations, and regulation. Personalized support to students with different participation levels is proposed, to create a more discursively connected community. Future directions include collecting more diverse data to better understand students’ emotions and to provide teachers and students with real-time data to support Knowledge Building as it proceeds.

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References

  • Abdi, H., Williams, L.J.: Wires. Comput. Stat. 2(4), 433–459 (2010)

    Article  Google Scholar 

  • Arguedas, M., Daradoumis, T., Xhafa, F.: Analyzing how emotion awareness influences students’ motivation, engagement, self-regulation and learning outcome. Educ. Technol. Soc. 19(2), 87–103 (2016)

    Google Scholar 

  • 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. St. 68(4), 223–241 (2010)

    Article  Google Scholar 

  • Baker, M., Järvelä, S., Andriessen, J. (eds.): Affective learning together: social and emotional dimensions of collaborative learning. Routledge, New York (2013)

    Google Scholar 

  • Bereiter, C.: Education and Mind in the Knowledge Age. Lawrence Erlbaum Associates, Mahwah (2002)

    Google Scholar 

  • Bielaczyc, K., Kapur, M., Collins, A.: Cultivating a community of learners in K–12 classrooms. In: Hmelo-Silver, C., Chinn, C., Chan, C., O’Donnell, A. (eds.) The International Handbook of Collaborative Learning, pp. 233–249. Routledge, New York (2013)

    Google Scholar 

  • Blikstein, P., Worsley, M.: multimodal learning analytics and education data mining: using computational technologies to measure complex learning tasks. J. Learn. Anal. 3(2), 220–238 (2016)

    Article  Google Scholar 

  • Broadbent, S., Gallotti, M.: Collective intelligence, how does it emerge (2015). https://www.nesta.org.uk/sites/default/files/collective_intelligence.pdf. Accessed 29 Mar 2017

  • Brown, A., Campione, J.C.: Psychological theory and the design of innovative learning environments: on procedures, principles, and systems. In: Schauble, L., Glaser, R. (eds.) Innovations in Learning: New Environments for Education, pp. 289–325. Lawrence Erlbaum Associates, Hillsdale (1996)

    Google Scholar 

  • Cahour, B.: Characteristics, emergence and circulation in interactional learning. In: Järvelä, S. (ed.) Affective Learning Together, pp. 52–70. Routledge, New York (2013)

    Google Scholar 

  • Chen, B., Resendes, M., Chai, C.S., Hong, H.Y.: Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse. Interact. Learn. Environ. 25(2), 162–175 (2017)

    Article  Google Scholar 

  • Creswell, J.W., Clark, V.L.P.: Designing and Conducting Mixed Methods Research. Sage publications, Boston (2017)

    Google Scholar 

  • Csikszentmihalyi, M.: Flow: The Psychology of Optimal Performance. Cambridge University Press, New York (1990)

    Google Scholar 

  • Csikszentmihalyi, M., Hunter, J.: Happiness in everyday life: the uses of experience sampling. J. Happiness Stud. 4(2), 185–199 (2003)

    Article  Google Scholar 

  • D’Mello, S., Lehman, B., Pekrun, R., Graesser, A.: Confusion can be beneficial for learning. Learn. Instr. 29, 153–170 (2014)

    Article  Google Scholar 

  • Ellsworth, P.C., Smith, C.A.: Shades of joy: patterns of appraisal differentiating pleasant emotions. Cognit. Emot. 2, 301–331 (1988)

    Article  Google Scholar 

  • Glaser, B.G., Strauss, A.L.: Discovery of Grounded Theory: Strategies for Qualitative Research. Routledge, New York (2017)

    Book  Google Scholar 

  • Grafsgaard, J. F., Wiggins, J. B., Boyer, K. E., Wiebe, E. N., Lester, J. C.: Automatically recognizing facial indicators of frustration: a learning-centric analysis. In: Proceedings of 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, pp. 159–165 (2013)

  • Grafsgaard, J., Wiggins, J., Boyer, K. E., Wiebe, E., Lester, J.: Predicting learning and affect from multimodal data streams in task-oriented tutorial dialogue. In: Proceedings of Educational Data Mining, pp. 122–129 (2014)

  • Grawemeyer, B., Mavrikis, M., Holmes, W., Gutiérrez-Santos, S., Wiedmann, M., Rummel, N.: Affective learning: improving engagement and enhancing learning with affect-aware feedback. User Model. User Adapt. Interact. 27(1), 119–158 (2017)

    Article  Google Scholar 

  • Goetz, J.L., Keltner, D., Simon-Thomas, E.: Compassion: an evolutionary analysis and empirical review. Psychol. Bull. 136(3), 351–374 (2010)

    Article  Google Scholar 

  • Hmelo-Silver, C.E., Barrows, H.S.: Facilitating collaborative knowledge building. Cognit. Instruct. 26(1), 48–94 (2008)

    Article  Google Scholar 

  • Izard, C.E.: Human Emotions. Plenum Press, New York (1977)

    Book  Google Scholar 

  • Lazarus, R.S.: Emotion and Adaptation. Oxford University Press, New York (1991)

    Google Scholar 

  • Keltner, D., Cordaro, D.T.: Understanding multimodal emotional expressions. In: Russell, J.A., Fernández-Dols, J.-M. (eds.) The Science of Facial Expression, pp. 57–75. Oxford University Press, Oxford (2017)

    Google Scholar 

  • Khanlari, A., Resendes, M., Zhu, G., Scardamalia, M.: Productive Knowledge Building Discourse Through Student-Generated Questions. In: Proceedings of the 12th International Conference on Computer Supported Collaborative Learning (CSCL2017), pp. 585–588 (2017)

  • Kirschner, P.A., Erkens, G.: Toward a framework for CSCL research. Educ. Psychol. 48(1), 1–8 (2013)

    Article  Google Scholar 

  • Koolagudi, S.G., Rao, K.S.: Emotion recognition from speech: a review. Int. J. Speech. Technol. 15(2), 99–117 (2012)

    Article  Google Scholar 

  • Kort, B., Reilly, R.: An affective module for an intelligent tutoring system. In: Proceedings of the 6th International Conference on Intelligent Tutoring Systems (ITS2002), pp. 955–962 (2002)

    Chapter  MATH  Google Scholar 

  • Lallé, S., Mudrick, N. V., Taub, M., Grafsgaard, J. F., Conati, C., Azevedo, R.: Impact of individual differences on affective reactions to pedagogical agents scaffolding. In: Proceedings of the International Conference on Intelligent Virtual Agents, pp. 269–282

    Chapter  Google Scholar 

  • Lampert, M.: Teaching Problems and the Problems of Teaching. Yale University Press, New Haven (2001)

    Google Scholar 

  • Loia, V., Senatore, S.: A fuzzy-oriented sentic analysis to capture the human emotion in web-based content. Knowl. Based Syst. 58, 75–85 (2014)

    Article  Google Scholar 

  • Lossman, H., So, H.J.: Toward pervasive knowledge building discourse: analyzing online and offline discourses of primary science learning in Singapore. Asia Pac. Educ. Rev. 11(2), 121–129 (2010)

    Article  Google Scholar 

  • Lutz, C.A., Abu-Lughod, L.E.: Language and the Politics of Emotion. Cambridge University Press, New York (1990)

    Google Scholar 

  • Mega, C., Ronconi, L., De Beni, R.: What makes a good student? How emotions, self-regulated learning, and motivation contribute to academic achievement. J. Educ. Psychol. 106(1), 121 (2014)

    Article  Google Scholar 

  • Nakamura, J., Csikszentmihalyi, M.: The concept of flow. In: Csikszentmihalyi, M. (ed.) Flow and the Foundations of Positive Psychology, pp. 239–263. Springer, Netherlands (2014)

    Google Scholar 

  • Pardos, Z. A., Baker, R. S., San Pedro, M. O., Gowda, S. M., Gowda, S. M.: Affective states and state tests: Investigating how affect throughout the school year predicts end of year learning outcomes. In: Proceedings of the Third International Conference on Learning Analytics and Knowledge (LAK2013), pp. 117–124 (2013)

  • Pekrun, R.: A social cognitive, control–value theory of achievement motions. In: Heckhausen, J. (ed.) Motivational Psychology of Human Development, pp. 143–163. Elsevier, Oxford (2000)

    Google Scholar 

  • Pekrun, R., Lichtenfeld, S., Marsh, H.W., Murayama, K., Goetz, T.: Achievement emotions and academic performance: longitudinal models of reciprocal effects. Child Dev. 88(5), 1653–1670 (2017)

    Article  Google Scholar 

  • Philip, D. N.: Networks and the spread of ideas in knowledge building environments, Doctoral dissertation, University of Toronto (2009)

  • Polo, C., Lund, K., Plantin, C., Niccolai, G.P.: Group emotions: the social and cognitive functions of emotions in argumentation. Int. J. Comput. Support. Collab. Learn. 11(2), 123–156 (2016)

    Article  Google Scholar 

  • Putwain, D.W., Becker, S., Symes, W., Pekrun, R.: Reciprocal relations between students’ academic enjoyment, boredom, and achievement over time. Learn. Instruct. 54, 73–81 (2018)

    Article  Google Scholar 

  • Popper, K.R.: Objective Knowledge: An Evolutionary Approach. Clarendon Press, Oxford (1972)

    Google Scholar 

  • Ranganathan, H., Chakraborty, S., Panchanathan, S.: Multimodal emotion recognition using deep learning architectures. In: Proceedings of Applications of Computer Vision (WACV2016), pp. 1–9 (2016)

  • Ray, A., Chakrabarti, A.: Design and implementation of technology enabled affective learning using fusion of bio-physical and facial expression. Educ. Technol. Soc. 19(4), 112–125 (2016)

    Google Scholar 

  • Rantala, T., Määttä, K.: Ten theses of the joy of learning at primary schools. Early Child Dev. Care 182(1), 87–105 (2012)

    Article  Google Scholar 

  • Reeve, R., Messina, R., Scardamalia, M.: Wisdom in elementary school. In: Ferrari, M., Potworowski, G. (eds.) Teaching for Wisdom: Cross-cultural Perspectives on Fostering Wisdom, pp. 79–92. Springer, New York (2008)

    Google Scholar 

  • Resendes, M., Scardamalia, M., Bereiter, C., Chen, B., Halewood, C.: Group-level formative feedback and metadiscourse. Int. J. Comput. Support. Collab. Learn. 10(3), 309–336 (2015)

    Article  Google Scholar 

  • Sakr, M., Jewitt, C., Price, S.: Mobile experiences of historical place: a multimodal analysis of emotional engagement. J. Learn. Sci. 25(1), 51–92 (2016)

    Article  Google Scholar 

  • Scardamalia, M., Bereiter, C.: Computer support for knowledge-building communities. J. Learn. Sci. 3(3), 265–283 (1994)

    Article  Google Scholar 

  • Scardamalia, M.: Collective cognitive responsibility for the advancement of knowledge. In: Jones, B. (ed.) Liberal Education in a Knowledge Society, pp. 67–98. Open Court, Chicago (2002)

    Google Scholar 

  • Scardamalia, M.: CSILE/Knowledge Forum®. In: Kovalchick, A., Dawson, K. (eds.) Education and Technology: An Encyclopedia, pp. 183–192. ABC-CLIO, Santa Barbara, CA (2004)

    Google Scholar 

  • Scardamalia, M., Bereiter, C.: Knowledge building: theory, pedagogy, and technology. In: Sawyer, K. (ed.) Cambridge Handbook of the Learning Sciences, pp. 97–118. Cambridge University Press, New York (2006)

    Google Scholar 

  • Scardamalia, M., Bereiter, C.: Knowledge building and knowledge creation: theory, pedagogy, and technology. In: Sawyer, K. (ed.) Cambridge Handbook of the Learning Sciences, 2nd edn, pp. 397–417. Cambridge University Press, New York (2014)

    Chapter  Google Scholar 

  • Schutz, P.A., Hong, J.Y., Cross, D.I., Osbon, J.N.: Reflections on investigating emotion in educational activity settings. Educ. Psychol. Rev. 18(4), 343–360 (2006)

    Article  Google Scholar 

  • Sinha, S., Rogat, T.K., Adams-Wiggins, K.R., Hmelo-Silver, C.E.: Collaborative group engagement in a computer-supported inquiry learning environment. Int. J. Comput. Support. Collab. Learn. 10(3), 273–307 (2015)

    Article  Google Scholar 

  • Thagard, P.: Coherence, truth and the development of scientific knowledge. Philos. Sci. 74, 28–47 (2007)

    Article  Google Scholar 

  • Worsley, M., Blikstein, P.: Using learning analytics to study cognitive disequilibrium in a complex learning environment. In: Proceedings of the Fifth International Conference on Learning Analytics and Knowledge (LAK2015), pp. 426–427 (2015)

  • Yang, Y., van Aalst, J., Chan, C.K., Tian, W.: Reflective assessment in knowledge building by students with low academic achievement. Int. J. Comput. Support. Collab. Learn. 11(3), 281–311 (2016)

    Article  Google Scholar 

  • Zhang, J., Scardamalia, M., Lamon, M., Messina, R., Reeve, R.: Socio-cognitive dynamics of knowledge building in the work of 9- and 10-year-olds. Educ. Technol. Res. Dev. 55(2), 117–145 (2007)

    Article  Google Scholar 

  • Zhang, J., Scardamalia, M., Reeve, R., Messina, R.: Designs for collective cognitive responsibility in knowledge-building communities. J. Learn. Sci. 18(1), 7–44 (2009)

    Article  Google Scholar 

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Acknowledgements

This work is supported by a Social Sciences and Humanities Research Council of Canada grant (SSHRC #496730). We would like to thank the children, teachers, parents, principal, and vice-principal who made this research possible. We also want to thank the editor and anonymous reviewers who have greatly helped improve this article with their insightful comments and suggestions.

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Authors and Affiliations

  1. Ontario Institute for Studies in Education, University of Toronto, Toronto, ON, M5S 1V6, Canada

    Gaoxia Zhu, Stacy Costa & Marlene Scardamalia

  2. Department of Educational Psychology and Leadership, Texas Tech University, Lubbock, TX, 79409, USA

    Wanli Xing & Bo Pei

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  1. Gaoxia Zhu
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Correspondence to Gaoxia Zhu.

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Zhu, G., Xing, W., Costa, S. et al. Exploring emotional and cognitive dynamics of Knowledge Building in grades 1 and 2. User Model User-Adap Inter 29, 789–820 (2019). https://doi.org/10.1007/s11257-019-09241-8

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