Abstract
Research in the field of collaboration shows that students do not spontaneously collaborate with each other. A system that can measure collaboration in real time could be useful by, for example, helping the teacher locate a group requiring guidance. To address this challenge, my research focuses on building and comparing collaboration detectors for different types of classroom problem solving activities, such as card sorting and hand writing. I am also studying transfer: how collaboration detectors for one task can be used with a new task. Finally, we attempt to build a teachers dashboard that can describe reasoning behind the triggered alerts thereby helping the teachers with insights to aid the collaborative activity. Data for building such detectors were collected in the form of verbal interaction and user action logs from students’ tablets. Three qualitative levels of interactivity was distinguished: Collaboration, Cooperation and Asymmetric Contribution. Machine learning was used to induce a classifier that can assign a code for every episode based on the set of features. Our preliminary results indicate that machine learned classifiers were reliable.
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References
Chen, J., Wang, M., Kirschner, P.A., Tsai, C.-C.: The role of collaboration, computer use, learning environments, and supporting strategies in CSCL: a meta-analysis. Rev. Educ. Res. 88(6), 799–843 (2018)
Vogel, F., Wecker, C., Kollar, I., Fischer, F.: Socio-cognitive scaffolding with computer-supported collaboration scripts: a meta-analysis. Educ. Psychol. Rev. 29(3), 477–511 (2017)
Stahl, G.: Theories of cognition in collaborative learning. In: Hmelo-Silver, C., O’Donnell, A., Chan, C., Chinn, C. (eds.) The International Handbook of Collaborative Learning, pp. 74–90. Taylor & Francis, New York (2013)
Hartmann, C., Angersbach, J., Rummel, N.: Social Interaction, Constructivism and their Application within (CS) CL Theories. International Society of the Learning Sciences, Inc. [ISLS] (2015)
Chi, M.T., Wylie, R.: ICAP: a hypothesis of differentiated learning effectiveness for four modes of engagement activities. Educ. Psychol. 49(4), 219–243 (2014)
Kahrimanis, G., et al.: Assessing collaboration quality in synchronous CSCL problem-solving activities: adaptation and empirical evaluation of a rating scheme. In: Cress, U., Dimitrova, V., Specht, M. (eds.) EC-TEL 2009. LNCS, vol. 5794, pp. 267–272. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04636-0_25
Meier, A., Spada, H., Rummel, N.: A rating scheme for assessing the quality of computer-supported collaboration processes. Int. J. Comput. Support. Collab. Learn. 2(1), 63 (2007)
Berkowitz, M.W., Gibbs, J.C.: Measuring the developmental features of moral discussion (1982)
Schwartz, D.L.: The productive agency that drives collaborative learning. In: Dillenbourg, P. (ed.) Collaborative Learning: Cognitive and Computational Approaches, pp. 197–218. Elsevier Science, New York (1999)
Soller, A., Martinez, A., Jermann, P., Muehlenbrock, M.: From mirroring to guiding: a review of state of the art technology for supporting collaborative learning. Int. J. Artif. Intell. Educ. 15, 261–290 (2005)
VanLehn, K.: Regulative loops, step loops and task loops. Int. J. Artif. Intell. Educ. 26(1), 107–112 (2016)
Martinez-Maldonado, R., Kay, J., Yacef, K.: An automatic approach for mining patterns of collaboration around an interactive tabletop. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS (LNAI), vol. 7926, pp. 101–110. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39112-5_11
Gweon, G., Agarawal, P., Raj, B., Rose, C.P.: The automatic assessment of knowledge integration processes in project teams. In: Proceedings of Computer Supported Collaborative Learning (2011)
Martinez-Maldonado, R., Clayphan, A., Yacef, K., Kay, J.: MTFeedback: providing notifications to enhance teacher awareness of small group work in the classroom. IEEE Trans. Learn. Technol. 8, 187–200 (2014)
Tedesco, P.A.: MArCo: building an artificial conflict mediator to support group planning interactions. Int. J. Artif. Intell. Educ. 13(1), 117–155 (2003)
de los Angeles Constantino-Gonzalez, M., Suthers, D.D., de los Santos, J.G.E.: Coaching web-based collaborative learning based on problem solution differences and participation. Int. J. Artif. Intell. Educ. 13(2–4), 263–299 (2003)
Baghaei, N., Mitrovic, A., Irwin, W.: Supporting collaborative learning and problem-solving in a constraint-based CSCL environment for UML class diagrams. Comput. Support. Collab. Learn. 2, 159–190 (2007)
Tsovaltzi, D., Rummel, N., McLaren, B., Pinkwart, N., Scheuer, O., Harrer, A., Braun, I.: Extending a virtual chemistry laboratory with a collaboration script to promote conceptual learning. Int. J. Technol. Enhanc. Learn. 2(1/2), 91–110 (2010)
Dragon, T., Floryan, M., Woolf, B., Murray, T.: Recognizing dialogue content in student collaborative conversation. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010. LNCS, vol. 6095, pp. 113–122. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13437-1_12
Martinez-Maldonado, R., Yacef, K., Kay, J.: TSCL: a conceptual model to inform understanding of collaborative learning processes at interactive tabletops. Int. J. Hum Comput Stud. 83, 62–82 (2015)
Walker, E., Rummel, N., Koedinger, K.R.: Adaptive intelligent support to improve peer tutoring in algebra. Int. J. Artif. Intell. Educ. 24(1), 33–61 (2014)
Gweon, G., Jain, M., McDonough, J., Raj, B., Rose, C.P.: Measuring prevalence of other-oriented transactive contributions using an automated measure of speech style accommodation. Int. J. Comput. Support. Collab. Learn. 8(2), 245–265 (2013)
Bassiou, N., et al.: Privacy-preserving speech analytics for automatic assessment of student collaboration. In: Privacy-Preserving Speech Analytics for Automatic Assessment of Student Collaboration, pp. 888–892 (2016)
Viswanathan, S.A., VanLehn, K.: Using the tablet gestures and speech of pairs of students to classify their collaboration. IEEE Trans. Learn. Technol. 11, 230–242 (2018)
Acknowledgements
This research was funded by the Diane and Gary Tooker chair for effective education in Science Technology Engineering and Math, by NSF grant IIS-1628782, and by the Bill and Melinda Gates Foundation under Grant OP1061281.
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Viswanathan, S.A., Vanlehn, K. (2019). Detection of Collaboration: Relationship Between Log and Speech-Based Classification. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds) Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science(), vol 11626. Springer, Cham. https://doi.org/10.1007/978-3-030-23207-8_60
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