Abstract
Many important forms of collaborative learning are co-creative in nature. AI systems to support co-creativity in learning are highly underinvestigated, and very little is known about the dialogue mechanisms that support learning during collaborative co-creativity. To address this need, we analyzed the structure of collaborative dialogue between pairs of high school students who co-created music by writing code. We used hidden Markov models to analyze 68 co-creative dialogues consisting of 3,305 total utterances. The results distinguish seven hidden states: three of the hidden states are characterized by conversation, such as social, aesthetic, or technical dialogue. The remaining four hidden states are characterized by task actions including code editing, accessing the curriculum, running the code successfully, and receiving an error when running the code. The model reveals that immediately after the pairs ran their code successfully, they often transitioned into the aesthetic or technical dialogue state. However, when facing code errors, learners were unlikely to transition into a conversation state. In the few cases where they did transition to a conversation state, this transition was almost always to the technical dialogue state. These findings reveal processes of human co-creativity and can inform the design of intelligent co-creative agents that support human collaboration and learning.
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
References
Akaike, H.: A new look at the statistical model identification. IEEE Trans. Autom. Control 19(6), 716–723 (1974)
Arroyo, I., Wixon, N., Allessio, D., Woolf, B., Muldner, K., Burleson, W.: Collaboration improves student interest in online tutoring. In: André, E., Baker, R., Hu, X., Rodrigo, M.M.T., du Boulay, B. (eds.) AIED 2017. LNCS (LNAI), vol. 10331, pp. 28–39. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61425-0_3
Carpenter, D., et al.: Detecting off-task behavior from student dialogue in game-based collaborative learning. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12163, pp. 55–66. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52237-7_5
Chng, E., Seyam, M.R., Yao, W., Schneider, B.: Using motion sensors to understand collaborative interactions in digital fabrication labs. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12163, pp. 118–128. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52237-7_10
Dich, Y., Reilly, J., Schneider, B.: Using physiological synchrony as an indicator of collaboration quality, task performance and learning. In: Penstein Rosé, C., et al. (eds.) AIED 2018. LNCS (LNAI), vol. 10947, pp. 98–110. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93843-1_8
Dyke, G., Adamson, D., Howley, I., Rose, C.P.: Enhancing scientific reasoning and discussion with conversational agents. IEEE Trans. Learn. Technol. 6(3), 240–247 (2013)
Freeman, J., Magerko, B., Verdin, R.: EarSketch: a web-based environment for teaching introductory computer science through music remixing. In: The 46th ACM Technical Symposium on Computer Science Education, SIGCSE 2015, p. 5. Association for Computing Machinery, New York (2015)
Gokhale, A.A.: Collaborative learning enhances critical thinking 7(1), 22–30 (1995)
Goodman, B.A., Linton, F.N., Gaimari, R.D., Hitzeman, J.M., Ross, H.J., Zarrella, G.: Using dialogue features to predict trouble during collaborative learning. User Model. User-Adapt. Interact. 15(1), 85–134 (2005). https://doi.org/10.1007/s11257-004-5269-x
Howard, C., Jordan, P., Di Eugenio, B., Katz, S.: Shifting the load: a peer dialogue agent that encourages its human collaborator to contribute more to problem solving. Int. J. Artif. Intell. Educ. 27(1), 101–129 (2017). https://doi.org/10.1007/s40593-015-0071-y
Kantosalo, A., Toivanen, J., Xiao, P., Toivonen, H.: From isolation to involvement: adapting machine creativity software to support human-computer co-creation. In: The Fifth International Conference on Computational Creativity, vol. 2014, pp. 1–7 (2014)
Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33(1), 159–174 (1977)
Magerko, B., et al.: EarSketch: a steam-based approach for underrepresented populations in high school computer science education. ACM Trans. Comput. Educ. (TOCE) 16(4), 1–25 (2016)
Morales-Urrutia, E.K., Ocaña Ch., J.M., Pérez-MarÃn, D., Pizarro-Romero, C.: Promoting learning and satisfaction of children when interacting with an emotional companion to program. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12164, pp. 220–223. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52240-7_40
Ogan, A., Finkelstein, S., Walker, E., Carlson, R., Cassell, J.: Rudeness and rapport: insults and learning gains in peer tutoring. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 11–21. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-30950-2_2
Parde, N., Nielsen, R.D.: AI meets Austen: towards human-robot discussions of literary metaphor. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds.) AIED 2019. LNCS (LNAI), vol. 11626, pp. 213–219. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23207-8_40
Rabiner, L., Juang, B.: An introduction to hidden Markov models. IEEE ASSP Mag. 3(1), 4–16 (1986)
Radu, I., Tu, E., Schneider, B.: Relationships between body postures and collaborative learning states in an augmented reality study. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12164, pp. 257–262. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52240-7_47
RodrÃguez, F.J., Boyer, K.E.: Discovering individual and collaborative problem-solving modes with hidden Markov models. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M.F. (eds.) AIED 2015. LNCS (LNAI), vol. 9112, pp. 408–418. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19773-9_41
Rosen, Y.: Computer-based assessment of collaborative problem solving: exploring the feasibility of human-to-agent approach. Int. J. Artif. Intell. Educ. 25(3), 380–406 (2015). https://doi.org/10.1007/s40593-015-0042-3
Samoilescu, R.-F., Dascalu, M., Sirbu, M.-D., Trausan-Matu, S., Crossley, S.A.: Modeling collaboration in online conversations using time series analysis and dialogism. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds.) AIED 2019. LNCS (LNAI), vol. 11625, pp. 458–468. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23204-7_38
Schneider, B., Pea, R.: Toward collaboration sensing. Int. J. Comput.-Supp. Collab. Learn. 9(4), 371–395 (2014). https://doi.org/10.1007/s11412-014-9202-y
Snyder, C., Hutchins, N.M., Biswas, G., Emara, M., Yett, B., Mishra, S.: Understanding collaborative question posing during computational modeling in science. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12164, pp. 296–300. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52240-7_54
Viswanathan, S.A., VanLehn, K.: Using the tablet gestures and speech of pairs of students to classify their collaboration. IEEE Trans. Learn. Technol. 11(2), 230–242 (2018)
Acknowledgements
This work is supported by the National Science Foundation through the grants DRL-1814083 and DRL-1813740. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Griffith, A.E. et al. (2021). Discovering Co-creative Dialogue States During Collaborative Learning. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12748. Springer, Cham. https://doi.org/10.1007/978-3-030-78292-4_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-78292-4_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78291-7
Online ISBN: 978-3-030-78292-4
eBook Packages: Computer ScienceComputer Science (R0)