Abstract
Quantitative ethnographers across a range of domains study complex collaborative thinking (CCT): the processes by which members of a group or team develop shared understanding by making cognitive connections from the statements and actions of the group. CCT is difficult to model because the actions of group members are interdependent—the activity of any individual is influenced by the actions of other members of the group. Moreover, the actions of group members engaged in some collaborative tasks may need to follow a particular order. However, current techniques can account for either interdependence or order, but not both. In this paper, we present directed epistemic network analysis (dENA), an extension of epistemic network analysis (ENA), as a method that can simultaneously account for the interdependent and ordered aspects of CCT. To illustrate the method, we compare a qualitative analysis of two U.S. Navy commanders working in a simulation to ENA and dENA analyses of their performance. We find that by accounting for interdependence but not order, ENA was not able to model differences between the commanders seen in the qualitative analysis, but by accounting for both interdependence and order, dENA was able to do so.
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References
Clark, H.H.: Using Language. Cambridge University Press, Cambridge (1996)
D’Angelo, A.-L.D., Ruis, A.R., Collier, W., Shaffer, D.W., Pugh, C.M.: Evaluating how residents talk and what it means for surgical performance in the simulation lab. American Journal of Surgery. In press (2020)
DeChurch, L.A., Mesmer-Magnus, J.R.: The cognitive underpinnings of effective teamwork: a meta-analysis. J. Appl. Psychol. 95(1), 32–53 (2010)
Dillenbourg, P.: Collaborative Learning: Cognitive and Computational Approaches. Advances in Learning and Instruction Series. Elsevier Science Inc, New York (1999)
Halpin, P.F., von Davier, A.A.: Modeling collaboration using point processes. In: von Davier, A.A., Zhu, M., Kyllonen, P.C. (eds.) Innovative Assessment of Collaboration, pp. 233–247. Springer, Cham (2017)
Hutchins, E.: Cognition in the Wild. MIT Press, Cambridge (1995)
Kapur, M.: Temporality matters: advancing a method for analyzing problem- solving processes in a computer-supported collaborative environment. Int. J. Comput. Supported Collaborative Learning 6(1), 39–56 (2011)
Kim, Y., Tscholl, M.: The dynamic interaction between engagement, friendship, and collaboration in robot children triads. In: Eagan, B., Misfeldt, M., Siebert-Evenstone, A. (eds.) ICQE 2019. CCIS, vol. 1112, pp. 307–314. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33232-7_27
Marquart, C.L., Swiecki, Z., Collier, W., Eagan, B., Woodward, R., Shaffer, D. W. rENA: Epistemic Network Analysis (Version 0.1.3) (2018)
Marquart, C.L., Swiecki, Z., Eagan, B., Shaffer, D. W.: ncodeR (Version 0.1.2) (2018)
Morrison, J.G., Kelly, R.T., Moore, R.A., Hutchins, S.G.: Tactical decision making under stress (TADMUS) decision support system, vol. 13 (1996)
Nachtigall, V., Sung, H.: Students’ collaboration patterns in a productive failure setting: an epistemic network analysis of contrasting cases. In: Eagan, B., Misfeldt, M., Siebert-Evenstone, A. (eds.) ICQE 2019. CCIS, vol. 1112, pp. 165–176. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33232-7_14
Paris, C., Johnston, J.H., Reeves, D.: A schema-based approach to measuring team decision making in a Navy combat information center. The Human in Command, pp. 263–278 (2000)
Perera, D., Kay, J., Koprinska, I., Yacef, K., Zaïane, O.R.: Clustering and sequential pattern mining of online collaborative learning data. IEEE Trans. Knowl. Data Eng. 21(6), 759–772 (2009)
Reimann, P.: Time is precious: variable- and event-centred approaches to process analysis in CSCL research. Comput. Supported Learn. 4, 239–257 (2009)
Ruis, A.R., Rosser, A.A., Quandt-Walle, C., Nathwani, J.N., Shaffer, D.W., Pugh, C.M.: The hands and head of a surgeon: modeling operative competency with multimodal epistemic network analysis. Am. J. Surgery 216(5), 835–840 (2018)
Ruis, A.R., Siebert-Evenstone, A.L., Pozen, R., Eagan, B., Shaffer, D.W.: Finding common ground: a method for measuring recent temporal context in analyses of complex, collaborative thinking. In: Lund, K., Niccolai, G., Lavoué, E., Hmelo-Silver, C., Gwon, G., Baker, M. (eds.) 13th International Conference on Computer Supported Collaborative Learning (CSCL), vol. 1, pp. 136–143. International Society of the Learning Sciences, Lyon, France (2019)
Shaffer, D.W., Collier, W., Ruis, A.R.: A tutorial on epistemic network analysis: analyzing the structure of connections in cognitive, social, and interaction data. J. Learn. Analytics 3(3), 9–45 (2016)
Shaffer, D.W.: Quantitative Ethnography. Cathcart Press, Madison (2017)
Siebert-Evenstone, A., Arastoopour Irgens, G., Collier, W., Swiecki, Z., Ruis, A.R., Shaffer, D.W.: In search of conversational grain size: Modelling semantic structure using moving stanza windows. J. Learn. Anal. 4(3), 123–139 (2017)
Suthers, D.D., Desiato, C.: Exposing chat features through analysis of uptake between contributions. In: 2012 45th Hawaii International Conference on System Sciences, pp. 3368–3377. IEEE, Hawaii (2012)
Swiecki, Z., Lian, Z., Ruis, A., Shaffer, D.W.: Does order matter? Investigating sequential and cotemporal models of collaboration. In: Lund, K., Niccolai, G.P., Lavoué, E., Gweon, C.H., Baker, M. (eds.) 13th International Conference on Computer Supported Collaborative Learning (CSCL), vol. 1, pp. 112–119. International Society of the Learning Sciences, Lyon, France (2019)
Swiecki, Z., Ruis, A.R., Farrell, C., Shaffer, D.W.: Assessing individual contributions to collaborative problem solving: a network analysis approach. Comput. Hum. Behav. 104 (2020)
Acknowledgements
This work was funded in part by the National Science Foundation (DRL-1661036, DRL-1713110), the Wisconsin Alumni Research Foundation, and the Office of the Vice Chancellor for Research and Graduate Education at the University of Wisconsin-Madison. The opinions, findings, and conclusions do not reflect the views of the funding agencies, cooperating institutions, or other individuals.
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Fogel, A. et al. (2021). Directed Epistemic Network Analysis. In: Ruis, A.R., Lee, S.B. (eds) Advances in Quantitative Ethnography. ICQE 2021. Communications in Computer and Information Science, vol 1312. Springer, Cham. https://doi.org/10.1007/978-3-030-67788-6_9
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