Abstract
In this paper, we aim at uncovering collaborative problem-solving patterns associated with students’ successful learning of social sciences research methods in a Productive Failure (PF) setting. We report an epistemic network analysis (ENA) of PF students’ conversations. Conversations are compared between PF groups that generated high quality solution ideas (HQ groups) and groups that developed low quality solution ideas (LQ groups). The ENA results demonstrate significantly different patterns. The collaborative problem solving of four HQ triads in a PF setting is characterized by debates and elaborations related to canonical contents of the targeted learning concept. The collaborative problem solving of four LQ triads is featured by task-pursuance actions and elaborations related to the instructions and contents stated in the worksheet. We also compared the eight groups based on their learning outcome (i.e., performance on a knowledge test). The comparison of four groups with a high learning outcome and of four groups with a low learning outcome revealed similar ENA results as the comparison of the HQ and LQ groups. These findings offer empirical evidence for the often hypothesized but rarely supported notion of certain collaborative problem-solving processes being important for the effectiveness of PF. The potential relevance of the collaborative problem-solving patterns of HQ groups for learning in a PF setting is discussed in light of mechanisms hypothesized to underlie the PF effect.
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References
Kapur, M.: Learning from productive failure. Learn. Res. Pract. 1(1), 51–65 (2015)
Loibl, K., Roll, I., Rummel, N.: Towards a theory of when and how problem solving followed by instruction supports learning. Educ. Psychol. Rev. 29(4), 693–715 (2017)
Kapur, M.: Examining productive failure, productive success, unproductive failure, and unproductive success in learning. Educ. Psychol. 51(2), 289–299 (2016)
Kapur, M., Bielaczyc, K.: Designing for productive failure. J. Learn. Sci. 21(1), 45–83 (2012)
Weaver, J.P., Chastain, R.J., DeCaro, D.A., DeCaro, M.S.: Reverse the routine: problem solving before instruction improves conceptual knowledge in undergraduate physics. Contemp. Educ. Psychol. 52, 36–47 (2018)
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. Anal. 3(3), 9–45 (2016)
Mazziotti, C., Rummel, N., Deiglmayr, A., Loibl, K.: Probing boundary conditions of Productive Failure and analyzing the role of young students’ collaboration. npj Sci. Learn. 4(2), 1–9 (2019)
Dillenbourg, P., Baker, M., Blaye, A., O’Malley, C.: The evolution of research on collaborative learning. In: Spada, H., Reiman, P. (eds.) Learning in Humans and Machine: Towards an Interdisciplinary Learning Science, pp. 189–211. Elsevier, Oxford (1996)
Hartmann, C., Rummel, N., Loibl, K.: Communication patterns and their role for conceptual knowledge acquisition from productive failure. In: Looi, C.K., Polman, J., Cress, U., Reimann, P. (eds.) Proceedings of the 12th International Conference of the Learning Sciences, vol. I, pp. 530–537. International Society of the Learning Sciences, Singapore (2016)
Nachtigall, V., Rummel, N., Serova, K.: Authentisch ist nicht gleich authentisch–Wie Schülerinnen und Schüler die Authentizität von Lernaktivitäten im Schülerlabor einschätzen [Authentic Does not Equal Authentic – How Students Evaluate the Authenticity of Learning Activities in an Out-of-School Lab]. Unterrichtswissenschaft 46(3), 299–319 (2018)
Loibl, K., Rummel, N.: The impact of guidance during problem solving prior to instruction on students’ inventions and learning outcomes. Instr. Sci. 42(3), 305–326 (2014)
Nachtigall, V., Serova, K., Rummel, N.: When Failure Fails to be Productive – Probing the Effectiveness of Productive Failure for Learning Beyond STEM Domains (submitted)
Marquart, C.L., Swiecki, Z, Eagan, B., Shaffer, D.W.: ncodeR: techniques for automated classifiers. R package version 0.1.2 (2018). https://CRAN.R-project.org/package=ncodeR. Accessed 24 July 2019
Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33(1), 159–174 (1977)
Shaffer, D.W.: Quantitative Ethnography. Cathcart Press, Madison (2017)
Acknowledgements
The data analyzed in this paper are part of a project that the first author conducted in cooperation with Prof. Dr. Nikol Rummel and Dr. Katja Serova at the Institute of Educational Research at Ruhr-University Bochum (RUB). We want to acknowledge their input and support with respect to, for instance, the study design. We also want to thank the Research School at RUB for funding a research stay at the Educational Psychology Department at University Wisconsin-Madison. The research stay allowed the first author to visit the lab of Prof. Dr. David W. Shaffer and made this joint publication possible. 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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Nachtigall, V., Sung, H. (2019). Students’ Collaboration Patterns in a Productive Failure Setting: An Epistemic Network Analysis of Contrasting Cases. In: Eagan, B., Misfeldt, M., Siebert-Evenstone, A. (eds) Advances in Quantitative Ethnography. ICQE 2019. Communications in Computer and Information Science, vol 1112. Springer, Cham. https://doi.org/10.1007/978-3-030-33232-7_14
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DOI: https://doi.org/10.1007/978-3-030-33232-7_14
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