Skip to main content
Springer Nature Link
Log in

Automatic Coding of Collective Creativity Dialogues in Collaborative Problem Solving Based on Deep Learning Models

  • Conference paper
  • First Online:
Blended Learning: Engaging Students in the New Normal Era (ICBL 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13357))

Included in the following conference series:

Abstract

Creativity and collaboration are considered core competencies of contemporary students in different education levels and disciplines. Existing research mainly focuses on the theoretical framework for computer-supported collaborative learning, and the dialogic content analysis is mainly based on expert annotating. Consequently, there is a vacuum in the direction of AI-based discourse analysis, which prevents researchers from progressing further towards automatic monitoring and assessing collective creativity in problem-solving activities. Hence, this paper aims to fill such a gap by setting a preliminary benchmark for deep learning models in dialogue coding. More concretely, we target identifying metacognition and cognition indicators in a collaborative problem-solving process based on a collective creativity coding framework. Moreover, our work goes beyond the conventional computer-mediated and dyad (one-on-one) settings and focuses on an interactive problem-oriented activity involving multiple participants. We employ deep learning models on the full transcripts collected during the activity to validate the affordance of AI-based coding models in a real teaching and learning scenario. To the best of our knowledge, it is the first attempt to introduce AI techniques into dialogue analysis in collaborative learning.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://github.com/Embedding/Chinese-Word-Vectors.

References

  1. Beghetto, R.A., Kaufman, J.C.: Toward a broader conception of creativity: a case for “mini-c” creativity. Psychol. Aesthet. Creat. Arts 1(2), 73 (2007)

    Google Scholar 

  2. Care, E., Scoular, C., Griffin, P.: Assessment of collaborative problem solving in education environments. Appl. Measure. Educ. 29(4), 250–264 (2016)

    Article  Google Scholar 

  3. Chen, X., Xie, H., Zou, D., Hwang, G.J.: Application and theory gaps during the rise of artificial intelligence in education. Comput. Educ. Artif. Intell. 1, 100002 (2020)

    Article  Google Scholar 

  4. Cheng, H.H., Yang, H.L.: Student team projects in information systems development: measuring collective creative efficacy. Australasian J. Educ. Technol. 27(6), 1 (2011)

    Article  Google Scholar 

  5. Cropley, A., Cropley, D.: Resolving the paradoxes of creativity: an extended phase model. Cambridge J. Educ. 38(3), 355–373 (2008)

    Article  Google Scholar 

  6. von Davier, A.A., Halpin, P.F.: Collaborative problem solving and the assessment of cognitive skills: Psychometric considerations. ETS Research Report Series 2013(2), i–36 (2013)

    Article  Google Scholar 

  7. Ferreira, D.J., Santos, G.L.: Scaffolding online discourse in collaborative ill-structured problem-solving for innovation. Inform. Educ. 8(2), 173–190 (2009)

    Google Scholar 

  8. Graesser, A., Kuo, B.C., Liao, C.H.: Complex problem solving in assessments of collaborative problem solving. J. Intell. 5(2), 10 (2017)

    Article  Google Scholar 

  9. Graves, A., Jaitly, N., Mohamed, A.: Hybrid speech recognition with deep bidirectional LSTM. In: 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 273–278 (2013)

    Google Scholar 

  10. Griffin, P., Care, E.: Assessment and Teaching of 21st Century Skills: Methods and Approach. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-65368-6

    Book  Google Scholar 

  11. Herborn, K., Stadler, M., Mustafić, M., Greiff, S.: The assessment of collaborative problem solving in PISA 2015: can computer agents replace humans? Comput. Human Behav. 104, 105624 (2020)

    Article  Google Scholar 

  12. Huang, X., Zou, D., Cheng, G., Chen, X., Xie, H.: Trends, research issues and applications of artificial intelligence in language education. Educ. Technol. Soc. J. Int. Forum Educ. Technol. Soc. IEEE Learn. Technol. Task Force 24(3), 238 (2021)

    Google Scholar 

  13. Johnson, R., Zhang, T.: Deep pyramid convolutional neural networks for text categorization. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, vol. 1, Long Papers, pp. 562–570. Association for Computational Linguistics, July 2017

    Google Scholar 

  14. Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751. Association for Computational Linguistics, October 2014

    Google Scholar 

  15. Klausen, S.H.: The notion of creativity revisited: a philosophical perspective on creativity research. Creat. Res. J. 22(4), 347–360 (2010)

    Article  Google Scholar 

  16. Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 2267–2273. AAAI 2015, AAAI Press (2015)

    Google Scholar 

  17. Li, S., Zhao, Z., Hu, R., Li, W., Liu, T., Du, X.: Analogical reasoning on Chinese morphological and semantic relations. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, vol. 2, Short Papers, pp. 138–143. Association for Computational Linguistics (2018)

    Google Scholar 

  18. Li, X., Li, Z., Xie, H., Li, Q.: Merging statistical feature via adaptive gate for improved text classification. Proc. AAAI Conf. Artif. Intell. 35(15), 13288–13296 (2021)

    Google Scholar 

  19. Lin, T.J., Duh, H.B.L., Li, N., Wang, H.Y., Tsai, C.C.: An investigation of learners’ collaborative knowledge construction performances and behavior patterns in an augmented reality simulation system. Comput. Educ. 68, 314–321 (2013)

    Article  Google Scholar 

  20. Liu, L., Hao, J., von Davier, A.A., Kyllonen, P., Zapata-Rivera, J.D.: A tough nut to crack: measuring collaborative problem solving. In: Handbook of research on technology tools for real-world skill development, pp. 344–359. IGI Global (2016)

    Google Scholar 

  21. Mikolov, T., Grave, E., Bojanowski, P., Puhrsch, C., Joulin, A.: Advances in pre-training distributed word representations. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), pp. 52–55. European Language Resources Association (ELRA) (2018)

    Google Scholar 

  22. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, vol. 2, pp. 3111–3119. NIPS 2013, Curran Associates Inc. (2013)

    Google Scholar 

  23. OECD: PISA 2015: Draft collaborative problem solving framework (2015)

    Google Scholar 

  24. OECD: PISA 2017 Results (Volume V) (2017)

    Google Scholar 

  25. Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)

    Google Scholar 

  26. Peters, M.E., et al.: Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1, (Long Papers), pp. 2227–2237. Association for Computational Linguistics, New Orleans, Louisiana, June 2018

    Google Scholar 

  27. Sannino, A., Ellis, V.: Learning and Collective Creativity. Routledge, Boston (2015)

    Google Scholar 

  28. Sawyer, R.K.: The dialogue of creativity: teaching the creative process by animating student work as a collaborating creative agent. Cogn. Instr. 1–29 (2021)

    Google Scholar 

  29. Saye, J.W., Brush, T.: Scaffolding critical reasoning about history and social issues in multimedia-supported learning environments. Educ. Technol. Res. Dev. 50(3), 77–96 (2002)

    Article  Google Scholar 

  30. Schoenfeld, A.H.: Looking toward the 21st century: challenges of educational theory and practice. Educ. Res. 28, 14–4 (1999)

    Article  Google Scholar 

  31. Shin, N., Jonassen, D.H., McGee, S.: Predictors of well-structured and ill-structured problem solving in an astronomy simulation. J. Res. Sci. Teach. 40(1), 6–33 (2003)

    Article  Google Scholar 

  32. Socher, R., et al.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1631–1642 (2013)

    Google Scholar 

  33. Tan, J.P.L., Caleon, I.S., Jonathan, C.R., Koh, E.: A dialogic framework for assessing collective creativity in computer-supported collaborative problem-solving tasks. Res. Pract. Technol. Enhanced Learn. 9(3), 1–7 (2014)

    Google Scholar 

  34. Treffinger, D.J., Isaksen, S.G.: Creative problem solving: the history, development, and implications for gifted education and talent development. Gifted Child Q. 49(4), 342–353 (2005)

    Article  Google Scholar 

  35. Yu, L., Nickerson, J.V., Sakamoto, Y.: Collective creativity: Where we are and where we might go. Proceedings of collective intelligence (2012). https://ssrn.com/abstract=2037908

Download references

Acknowledgement

The research described in this paper has been supported by Eastern Scholar Chair Professorship Fund from Shanghai Municipal Education Commission of China (No. JZ2017005) and National Natural Science Foundation of China (No. 61977023).

Author information

Authors and Affiliations

  1. School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China

    Zongxi Li

  2. Department of Computing and Decision Sciences, Lingnan University, Hong Kong, China

    Haoran Xie

  3. Faculty of Education, The University of Hong Kong, Hong Kong, China

    Minhong Wang

  4. Department of Educational Information Technology, East China Normal University, Shanghai, China

    Bian Wu & Yiling Hu

Authors
  1. Zongxi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haoran Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Minhong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bian Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yiling Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haoran Xie .

Editor information

Editors and Affiliations

  1. The Hong Kong Polytechnic University, Hong Kong, China

    Richard Chen Li

  2. Hong Kong Metropolitan University, Hong Kong, China

    Simon K. S. Cheung

  3. The Hong Kong Polytechnic University, Hong Kong, China

    Peter H. F. Ng

  4. Tung Wah College, Hong Kong, China

    Leung-Pun Wong

  5. Hong Kong Metropolitan University, Hong Kong, China

    Fu Lee Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, Z., Xie, H., Wang, M., Wu, B., Hu, Y. (2022). Automatic Coding of Collective Creativity Dialogues in Collaborative Problem Solving Based on Deep Learning Models. In: Li, R.C., Cheung, S.K.S., Ng, P.H.F., Wong, LP., Wang, F.L. (eds) Blended Learning: Engaging Students in the New Normal Era. ICBL 2022. Lecture Notes in Computer Science, vol 13357. Springer, Cham. https://doi.org/10.1007/978-3-031-08939-8_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-08939-8_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08938-1

  • Online ISBN: 978-3-031-08939-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics