User Modeling and User-Adapted Interaction

, Volume 29, Issue 4, pp 789–820 | Cite as

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

  • Gaoxia ZhuEmail author
  • Wanli Xing
  • Stacy Costa
  • Marlene Scardamalia
  • Bo Pei


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.


Idea improvement Multimodal learning analytics Speech emotion analysis Knowledge Building Discourse analysis Learning community Online and offline discourse 



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.


  1. Abdi, H., Williams, L.J.: Wires. Comput. Stat. 2(4), 433–459 (2010)CrossRefGoogle Scholar
  2. 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
  3. 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)CrossRefGoogle Scholar
  4. Baker, M., Järvelä, S., Andriessen, J. (eds.): Affective learning together: social and emotional dimensions of collaborative learning. Routledge, New York (2013)Google Scholar
  5. Bereiter, C.: Education and Mind in the Knowledge Age. Lawrence Erlbaum Associates, Mahwah (2002)Google Scholar
  6. 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
  7. 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)CrossRefGoogle Scholar
  8. Broadbent, S., Gallotti, M.: Collective intelligence, how does it emerge (2015). Accessed 29 Mar 2017
  9. 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
  10. 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
  11. 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)CrossRefGoogle Scholar
  12. Creswell, J.W., Clark, V.L.P.: Designing and Conducting Mixed Methods Research. Sage publications, Boston (2017)Google Scholar
  13. Csikszentmihalyi, M.: Flow: The Psychology of Optimal Performance. Cambridge University Press, New York (1990)Google Scholar
  14. Csikszentmihalyi, M., Hunter, J.: Happiness in everyday life: the uses of experience sampling. J. Happiness Stud. 4(2), 185–199 (2003)CrossRefGoogle Scholar
  15. D’Mello, S., Lehman, B., Pekrun, R., Graesser, A.: Confusion can be beneficial for learning. Learn. Instr. 29, 153–170 (2014)CrossRefGoogle Scholar
  16. Ellsworth, P.C., Smith, C.A.: Shades of joy: patterns of appraisal differentiating pleasant emotions. Cognit. Emot. 2, 301–331 (1988)CrossRefGoogle Scholar
  17. Glaser, B.G., Strauss, A.L.: Discovery of Grounded Theory: Strategies for Qualitative Research. Routledge, New York (2017)CrossRefGoogle Scholar
  18. 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)Google Scholar
  19. 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)Google Scholar
  20. 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)CrossRefGoogle Scholar
  21. Goetz, J.L., Keltner, D., Simon-Thomas, E.: Compassion: an evolutionary analysis and empirical review. Psychol. Bull. 136(3), 351–374 (2010)CrossRefGoogle Scholar
  22. Hmelo-Silver, C.E., Barrows, H.S.: Facilitating collaborative knowledge building. Cognit. Instruct. 26(1), 48–94 (2008)CrossRefGoogle Scholar
  23. Izard, C.E.: Human Emotions. Plenum Press, New York (1977)CrossRefGoogle Scholar
  24. Lazarus, R.S.: Emotion and Adaptation. Oxford University Press, New York (1991)Google Scholar
  25. 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
  26. 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)Google Scholar
  27. Kirschner, P.A., Erkens, G.: Toward a framework for CSCL research. Educ. Psychol. 48(1), 1–8 (2013)CrossRefGoogle Scholar
  28. Koolagudi, S.G., Rao, K.S.: Emotion recognition from speech: a review. Int. J. Speech. Technol. 15(2), 99–117 (2012)CrossRefGoogle Scholar
  29. 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)CrossRefGoogle Scholar
  30. 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. 269–282CrossRefGoogle Scholar
  31. Lampert, M.: Teaching Problems and the Problems of Teaching. Yale University Press, New Haven (2001)Google Scholar
  32. 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)CrossRefGoogle Scholar
  33. 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)CrossRefGoogle Scholar
  34. Lutz, C.A., Abu-Lughod, L.E.: Language and the Politics of Emotion. Cambridge University Press, New York (1990)Google Scholar
  35. 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)CrossRefGoogle Scholar
  36. 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
  37. 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)Google Scholar
  38. 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
  39. 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)CrossRefGoogle Scholar
  40. Philip, D. N.: Networks and the spread of ideas in knowledge building environments, Doctoral dissertation, University of Toronto (2009)Google Scholar
  41. 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)CrossRefGoogle Scholar
  42. 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)CrossRefGoogle Scholar
  43. Popper, K.R.: Objective Knowledge: An Evolutionary Approach. Clarendon Press, Oxford (1972)Google Scholar
  44. 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)Google Scholar
  45. 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
  46. Rantala, T., Määttä, K.: Ten theses of the joy of learning at primary schools. Early Child Dev. Care 182(1), 87–105 (2012)CrossRefGoogle Scholar
  47. 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
  48. 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)CrossRefGoogle Scholar
  49. 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)CrossRefGoogle Scholar
  50. Scardamalia, M., Bereiter, C.: Computer support for knowledge-building communities. J. Learn. Sci. 3(3), 265–283 (1994)CrossRefGoogle Scholar
  51. 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
  52. 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
  53. 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
  54. 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)CrossRefGoogle Scholar
  55. 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)CrossRefGoogle Scholar
  56. 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)CrossRefGoogle Scholar
  57. Thagard, P.: Coherence, truth and the development of scientific knowledge. Philos. Sci. 74, 28–47 (2007)CrossRefGoogle Scholar
  58. 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)Google Scholar
  59. 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)CrossRefGoogle Scholar
  60. 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)CrossRefGoogle Scholar
  61. 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)CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Gaoxia Zhu
    • 1
    Email author
  • Wanli Xing
    • 2
  • Stacy Costa
    • 1
  • Marlene Scardamalia
    • 1
  • Bo Pei
    • 2
  1. 1.Ontario Institute for Studies in EducationUniversity of TorontoTorontoCanada
  2. 2.Department of Educational Psychology and LeadershipTexas Tech UniversityLubbockUSA

Personalised recommendations