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Analyzing student thinking reflected in self-constructed cognitive maps and its influence on inquiry task performance

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Abstract

Higher-order thinking is crucial to inquiry learning. It is important to investigate how students think in inquiry contexts. Given the tacit nature of higher-order thinking, cognitive maps (e.g., concept maps, reasoning maps) have been used to externalize thinking and have shown promising effects in terms of improving inquiry task performance. However, few studies have analyzed student-constructed maps that reflect the thinking underpinning students’ inquiry task performance. This study aimed to address this gap. Sixty-nine 11th grade students worked in small groups to explain a fish die-off phenomenon in a virtual ecosystem and collaboratively constructed an integrative cognitive map to facilitate thinking during the task. The map comprised a concept map (representing conceptual thinking about relevant subject knowledge) and a reasoning map (representing the reasoning process). Regression analyses showed that the quality of the student-constructed maps, particularly the reasoning maps, was a significant predictor of inquiry task performance assessed based on students’ written explanations of the phenomenon. Although the quality of the concept maps was not a significant predictor of inquiry task performance, it did predict the quality of the reasoning maps. Student thinking reflected in concept mapping and that reflected in reasoning mapping play different roles in inquiry learning.

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Notes

  1. Previous studies of integrative cognitive mapping have generally reported fairly large effect sizes; for example, the 3DTG (Chen et al. 2018a) yielded an ES of 0.96 for group task performance, with its corresponding f2 = 0.55. To be moderate, we set f2 as 0.4 in this study.

References

  • Bell, T., Urhahne, D., Schanze, S., & Ploetzner, R. (2010). Collaborative inquiry learning: Models, tools, and challenges. International Journal of Science Education, 32(3), 349–377. https://doi.org/10.1080/09500690802582241

    Article  Google Scholar 

  • Chen, J., Wang, M., Grotzer, T. A., & Dede, C. (2018a). Using a three-dimensional thinking graph to support inquiry learning. Journal of Research in Science Teaching, 55(9), 1239–1263. https://doi.org/10.1002/tea.21450.

  • Chen, J., Wang, M., Kirschner, P. A., & Tsai, C. C. (2018b). The role of collaboration, computer use, learning environments, and supporting strategies in CSCL: A meta-analysis. Review of Educational Research, 88(6), 799–843. https://doi.org/10.3102/0034654318791584.

    Article  Google Scholar 

  • Cox, R. (1999). Representation construction, externalised cognition and individual differences. Learning and Instruction, 9(4), 343–363.

    Article  Google Scholar 

  • de Jong, T., & Ferguson-Hessler, M. (1986). Cognitive structures of good and poor novice problem solvers in physics. Journal of Educational Psychology, 78(4), 279–288.

    Article  Google Scholar 

  • de Vries, E. (2006). Students’ construction of external representations in design-based learning situations. Learning and Instruction, 16(3), 213–227. https://doi.org/10.1016/j.learninstruc.2006.03.006

    Article  Google Scholar 

  • de Vries, E., Lund, K., & Baker, M. (2002). Computer-mediated epistemic dialogue: Explanation and argumentation as vehicles for understanding scientific notions. Journal of the Learning Sciences, 11(1), 63–103. https://doi.org/10.1207/S15327809JLS1101_3

    Article  Google Scholar 

  • Echevarria, M. (2003). Anomalies as a catalyst for middle school students’ knowledge construction and scientific reasoning during science inquiry. Journal of Educational Psychology, 95(2), 357–374. https://doi.org/10.1037/0022-0663.95.2.357

    Article  Google Scholar 

  • Gadgil, S., Nokes-Malach, T. J., & Chi, M. T. H. (2012). Effectiveness of holistic mental model confrontation in driving conceptual change. Learning and Instruction, 22(1), 47–61. https://doi.org/10.1016/j.learninstruc.2011.06.002

    Article  Google Scholar 

  • Gijlers, H., & de Jong, T. (2013). Using concept maps to facilitate collaborative simulation-based inquiry learning. Journal of the Learning Sciences, 22(3), 340–374. https://doi.org/10.1080/10508406.2012.748664

    Article  Google Scholar 

  • Gijlers, H., Saab, N., van Joolingen, W. R., de Jong, T., & van Hout-Wolters, B. H. A. M. (2009). Interaction between tool and talk: How instruction and tools support consensus building in collaborative inquiry-learning environments. Journal of Computer Assisted Learning, 25(3), 252–267. https://doi.org/10.1111/j.1365-2729.2008.00302.x

    Article  Google Scholar 

  • Hmelo-Silver, C. E., Duncan, R. G., & Chinn, C. A. (2007). Scaffolding and achievement in problem-based and inquiry learning: A response to Kirschner, Sweller, and Clark (2006). Educational Psychologist, 42(2), 99–107. https://doi.org/10.1080/00461520701263368

    Article  Google Scholar 

  • Hsu, C. C., Chiu, C. H., Lin, C. H., & Wang, T. I. (2015). Enhancing skill in constructing scientific explanations using a structured argumentation scaffold in scientific inquiry. Computers and Education, 91, 46–59. https://doi.org/10.1016/j.compedu.2015.09.009

    Article  Google Scholar 

  • Janssen, J., Erkens, G., Kirschner, P. A., & Kanselaar, G. (2010). Effects of representational guidance during computer-supported collaborative learning. Instructional Science, 38(1), 59–88. https://doi.org/10.1007/s11251-008-9078-1

    Article  Google Scholar 

  • Jonassen, D. H. (1997). Instructional design models for well-structured and III-structured problem-solving learning outcomes. Educational Technology Research and Development, 45(1), 65–94. https://doi.org/10.1007/BF02299613.

    Article  Google Scholar 

  • Jonassen, D. (2003). Using cognitive tools to represent problems. Journal of Research on Technology in Education, 35(3), 362–381. https://doi.org/10.1080/15391523.2003.10782391

    Article  Google Scholar 

  • Jonassen, D. H. (2005). Tools for representing problems and the knowledge required to solve them. In S. O. Tergan & T. Keller (Eds.), Knowledge and information visualization (pp. 82–94). Springer.

    Chapter  Google Scholar 

  • Kim, M. C., & Hannafin, M. J. (2011). Scaffolding 6th graders’ problem solving in technology-enhanced science classrooms: A qualitative case study. Instructional Science, 39(3), 255–282. https://doi.org/10.1007/s11251-010-9127-4

    Article  Google Scholar 

  • Kinchin, I. M. (2000). Using concept maps to reveal understanding: A two-tier analysis. School Science Review, 81(296), 41–46.

    Google Scholar 

  • Klahr, D., & Dunbar, K. (1988). Dual space search during scientific reasoning. Cognitive Science, 12(1), 1–48. https://doi.org/10.1016/0364-0213(88)90007-9

    Article  Google Scholar 

  • Kolloffel, B., Eysink, T. H. S., & de Jong, T. (2011). Comparing the effects of representational tools in collaborative and individual inquiry learning. International Journal of Computer-Supported Collaborative Learning, 6(2), 223–251. https://doi.org/10.1007/s11412-011-9110-3

    Article  Google Scholar 

  • Kuhn, D., Amsel, E., & O’Loughlin, M. (1988). The development of scientific thinking skills. Academic Press.

    Google Scholar 

  • Kuhn, D., Black, J., Keselman, A., & Kaplan, D. (2000). The development of cognitive skills to support inquiry learning. Cognition and Instruction, 18(4), 495–523.

    Article  Google Scholar 

  • Kuhn, L., & Reiser, B. (2005). Students constructing and defending evidence-based scientific explanations. Paper presented at the Annual Meeting of the National Association for Research in Science Teaching, Dallas, TX.

  • Kyza, E. A. (2009). Middle-school students’ reasoning about alternative hypotheses in a scaffolded, software-based inquiry investigation. Cognition and Instruction, 27(4), 277–311. https://doi.org/10.1080/07370000903221718

    Article  Google Scholar 

  • Lazonder, A. W., & Harmsen, R. (2016). Meta-analysis of inquiry-based learning: Effects of guidance. Review of Educational Research, 86(3), 681–718. https://doi.org/10.3102/0034654315627366

    Article  Google Scholar 

  • Löhner, S., Van Joolingen, W. R., & Savelsbergh, E. R. (2003). The effect of external representation on constructing computer models of complex phenomena. Instructional Science, 31(6), 395–418. https://doi.org/10.1023/A:1025746813683

    Article  Google Scholar 

  • Lou, Y., Abrami, P. C., Spence, J. C., Poulsen, C., Chambers, B., & D’Apollonia, S. (1996). Within-class grouping: A meta-analysis. Review of Educational Research, 66(4), 423–458.

    Article  Google Scholar 

  • Markham, K. M., Mintzes, J. J., & Jones, M. G. (1994). The concept map as a research and evaluation tool: Further evidence of validity. Journal of Research in Science Teaching, 31(1), 91–101. https://doi.org/10.1002/tea.3660310109

    Article  Google Scholar 

  • McClure, J. R., Sonak, B., & Suen, H. K. (1999). Concept map assessment of classroom learning: Reliability, validity, and logistical practicality. Journal of Research in Science Teaching, 36(4), 475–492.

    Article  Google Scholar 

  • McNeill, K. L., & Krajcik, J. (2012). Supporting grade 5–8 students in constructing explanations in science: The claim, evidence and reasoning framework for talk and writing. New York: Pearson Allyn & Bacon.

    Google Scholar 

  • Metcalf, S., Kamarainen, A., Tutwiler, M. S., Grotzer, T. A., & Dede, C. (2011). Ecosystem science learning via multi-user virtual environments. International Journal of Gaming and Computer-Mediated Simulations, 3(1), 86–90. https://doi.org/10.4018/jgcms.2011010107.

  • Metcalf, S. M., Kamarainen, A. M., King, J., Grotzer, T. A., & Dede, C. (2018). Supports for deeper learning of inquiry-based ecosystem science in virtual environments: Comparing virtual and physicalconcept mapping. Computers in Human Behavior, 87, 459–469. https://doi.org/10.1016/j.chb.2018.03.018.

    Article  Google Scholar 

  • Metz, K. E. (2004). Children’s understanding of scientific inquiry: Their conceptualization of uncertainty in investigations of their own design. Cognition and Instruction, 22(2), 219–290. https://doi.org/10.1207/s1532690xci2202_3

    Article  Google Scholar 

  • Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, N.J.: Prentice Hall.

    Google Scholar 

  • NRC, National Research Committee. (1996). National science education standards. Washington: National Academy Press.

    Google Scholar 

  • Novak, J. D., Bob Gowin, D., & Johansen, G. T. (1983). The use of concept mapping and knowledge vee mapping with junior high school science students. Science Education, 67(5), 625–645. https://doi.org/10.1002/sce.3730670511

    Article  Google Scholar 

  • Novak, J. D., & Cañas, A. J. (2008). The theory underlying concept maps and how to construct and use them. Retrieved from http://cmap.ihmc.us/Publications/ResearchPapers/TheoryUnderlyingConceptMaps.pdf

  • Novak, J. D., & Gowin, D. B. (1984). Learning how to learn. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Pedaste, M., de Jong, T., Sarapuu, T., Piksööt, J., van Joolingen, W. R., & Giemza, A. (2013). Investigating ecosystems as a blended learning experience. Science, 340(6140), 1537–1538.

    Article  Google Scholar 

  • Penner, D. E. (2000). Explaining systems: Investigating middle school students’ understanding of emergent phenomena. Journal of Research in Science Teaching, 37(8), 784–806.

    Article  Google Scholar 

  • Roessger, K. M., Daley, B. J., & Hafez, D. A. (2018). Effects of teaching concept mapping using practice, feedback, and relational framing. Learning and Instruction, 54, 11–21. https://doi.org/10.1016/j.learninstruc.2018.01.011

    Article  Google Scholar 

  • Ruiz-Primo, M. A., Schultz, S. E., Li, M., & Shavelson, R. J. (2001). Comparison of the reliability and validity of scores from two concept-mapping techniques. Journal of Research in Science Teaching, 38(2), 260–278.

    Article  Google Scholar 

  • Ruiz-Primo, M. A., & Shavelson, R. J. (1996). Problems and issues in the use of concept maps in science assessment. Journal of Research in Science Teaching, 33(6), 569–600.

    Article  Google Scholar 

  • Saab, N., van Joolingen, W. R., & van Hout-Wolters, B. H. A. M. (2007). Supporting communication in a collaborative discovery learning environment. Instructional Science, 35(1), 73–98.

    Article  Google Scholar 

  • Schwendimann, B. A., & Linn, M. C. (2016). Comparing two forms of concept map critique activities to facilitate knowledge integration processes in evolution education. Journal of Research in Science Teaching, 53(1), 70–94. https://doi.org/10.1002/tea.21244

    Article  Google Scholar 

  • Slof, B., Erkens, G., Kirschner, P. A., & Helms-Lorenz, M. (2013). The effects of inspecting and constructing part-task-specific visualizations on team and individual learning. Computers and Education, 60(1), 221–233. https://doi.org/10.1016/j.compedu.2012.07.019

    Article  Google Scholar 

  • Slof, B., Erkens, G., Kirschner, P. A., Janssen, J., & Jaspers, J. G. M. (2012). Successfully carrying out complex learning-tasks through guiding teams’ qualitative and quantitative reasoning. Instructional Science, 40(3), 623–643. https://doi.org/10.1007/s11251-011-9185-2

    Article  Google Scholar 

  • Suthers, D. D., & Hundhausen, C. D. (2003). An experimental study of the effects of representational guidance on collaborative learning processes. Journal of the Learning Sciences, 12(2), 183–218.

    Article  Google Scholar 

  • Suthers, D. D., Vatrapu, R., Medina, R., Joseph, S., & Dwyer, N. (2008). Beyond threaded discussion: Representational guidance in asynchronous collaborative learning environments. Computers and Education, 50(4), 1103–1127. https://doi.org/10.1016/j.compedu.2006.10.007

    Article  Google Scholar 

  • Ting, C., Sam, Y., & Wong, C. (2013). Model of conceptual change for INQPRO: A Bayesian network approach. Computers and Education, 65, 77–91. https://doi.org/10.1016/j.compedu.2013.01.013

    Article  Google Scholar 

  • Toth, E. E., Suthers, D. D., & Lesgold, A. M. (2002). Mapping to know: The effects of representational guidance and reflective assessment on scientific inquiry. Science Education, 86(2), 264–286. https://doi.org/10.1002/sce.10004

    Article  Google Scholar 

  • Tytler, R., & Peterson, S. (2004). From “try it and see” to strategic exploration: Characterizing young children’s scientific reasoning. Journal of Research in Science Teaching, 41(1), 94–118. https://doi.org/10.1002/tea.10126

    Article  Google Scholar 

  • Van Drie, J., Van Boxtel, C., Jaspers, J., & Kanselaar, G. (2005). Effects of representational guidance on domain specific reasoning in CSCL. Computers in Human Behavior, 21(4), 575–602. https://doi.org/10.1016/j.chb.2004.10.024

    Article  Google Scholar 

  • Van Joolingen, W. R., & de Jong, T. (1997). An extended dual search space model of scientific discovery learning. Instructional Science, 25(5), 307–346.

    Article  Google Scholar 

  • Wang, M., Cheng, B., Chen, J., Mercer, N., & Kirschner, P. A. (2017). The use of web-based collaborative concept mapping to support group learning and interaction in an online environment. Internet and Higher Education, 34, 28–40. https://doi.org/10.1016/j.iheduc.2017.04.003.

  • Wang, M., Wu, B., Kirschner, P. A., & Spector, J. M. (2018). Using cognitive mapping to foster deeper learning with complex problems in a computer-based environment. Computers in Human Behavior, 87, 450–458. https://doi.org/10.1016/j.chb.2018.01.024.

  • Weinberger, A., & Fischer, F. (2006). A framework to analyze argumentative knowledge construction in computer-supported collaborative learning. Computers & Education, 46(1), 71–95.

    Article  Google Scholar 

  • Wu, B., Wang, M., Grotzer, T. A., Liu, J., & Johnson, J. M. (2016). Visualizing complex processes using a cognitive-mapping tool to support the learning of clinical reasoning. BMC Medical Education, 16, 216. https://doi.org/10.1186/s12909-016-0734-x.

    Article  Google Scholar 

  • Ziegler, R., & Weger, U. (2018). Exploring conceptual thinking and pure concepts from a first person perspective. Phenomenology and the Cognitive Sciences, 18(5), 947–972. https://doi.org/10.1007/s11097-018-9593-8

    Article  Google Scholar 

  • Zimmerman, C. (2007). The development of scientific thinking skills in elementary and middle school. Developmental Review, 27(2), 172–223. https://doi.org/10.1016/j.dr.2006.12.001

    Article  Google Scholar 

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Acknowledgements

This project is supported by the National Natural Science Foundation of China (Project No. 61977023), Eastern Scholar Chair Professorship Fund (No. JZ2017005) from the Shanghai Municipal Education Commission of China, and Fundamental Research Funds for the Central Universities from Zhejiang University. EcoMUVE was supported by the Institute of Education Sciences, U.S. Department of Education, R305A080514 to Chris Dede and Tina Grotzer. All opinions, findings, conclusions or recommendations expressed here are those of the authors and do not necessarily reflect the views of the Institute for Education Sciences. The authors would thank Prof. Haijing Jiang for his valuable support for this study.

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Authors and Affiliations

  1. College of Education, Zhejiang University, Zhejiang, China

    Juanjuan Chen

  2. KM&EL Lab, Faculty of Education, The University of Hong Kong, Pokfulam Road, Hong Kong, China

    Juanjuan Chen & Minhong Wang

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

    Minhong Wang

  4. Graduate School of Education, Harvard University, Cambridge, USA

    Chris Dede & Tina A. Grotzer

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  1. Juanjuan Chen
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Chen, J., Wang, M., Dede, C. et al. Analyzing student thinking reflected in self-constructed cognitive maps and its influence on inquiry task performance. Instr Sci 49, 287–312 (2021). https://doi.org/10.1007/s11251-021-09543-8

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