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The Development of Multivariable Causality Strategy: Instruction or Simulation First?

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Artificial Intelligence in Education (AIED 2023)

Abstract

Understanding phenomena by exploring complex interactions between variables is a challenging task for students of all ages. While the use of simulations to support exploratory learning of complex phenomena is common, students still struggle to make sense of interactive relationships between factors. Here we study the applicability of Problem Solving before Instruction (PS-I) approach to this context. In PS-I, learners are given complex tasks that help them make sense of the domain, prior to receiving instruction on the target concepts. While PS-I has been shown to be effective to teach complex topics, it is yet to show benefits for learning general inquiry skills. Thus, we tested the effect of exploring with simulations before instruction (as opposed to afterward) on the development of a multivariable causality strategy (MVC-strategy). Undergraduate students (N = 71) completed two exploration tasks using simulation about virus transmission. Students completed Task1 either before (Exploration-first condition) or after (Instruction-first condition) instruction related to multivariable causality and completed Task2 at the end of the intervention. Following, they completed transfer Task3 with a simulation on the topic of Predator-Prey relationships. Results showed that Instruction-first improved students’ Efficiency of MVC-strategy on Task1. However, these gaps were gone by Task2. Interestingly, Exploration-first had higher efficiency of MVC-strategy on transfer Task3. These results show that while Exploration-first did not promote performance on the learning activity, it has in fact improved learning on the transfer task, consistent with the PS-I literature. This is the first time that PS-I is found effective in teaching students better exploration strategies.

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References

  1. Roll, I., Yee, N., Briseno, A.: Students’ adaptation and transfer of strategies across levels of scaffolding in an exploratory environment. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds.) ITS 2014. LNCS, vol. 8474, pp. 348–353. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07221-0_43

    Chapter  Google Scholar 

  2. Roll, I., Baker, R.S.J.D., Aleven, V., Koedinger, K.R.: On the benefits of seeking (and avoiding) help in online problem solving environment. J. Learn. Sci. 23(4), 537–560 (2014). https://doi.org/10.1080/10508406.2014.883977

    Article  Google Scholar 

  3. 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). https://doi.org/10.1007/s10648-016-9379-x

    Article  Google Scholar 

  4. Roll, I., et al.: Understanding the impact of guiding inquiry: the relationship between directive support, student attributes, and transfer of knowledge, attitudes, and behaviors in inquiry learning. Instr. Sci. 46(1), 77–104 (2018)

    Article  Google Scholar 

  5. Saba, J., Hel-Or, H., Levy, S.T.:.Much.Matter.in.Motion: Learning by modeling systems in chemistry and physics with a universal programming platform. Interactive Learn. Env. 29, 1–20 (2021)

    Google Scholar 

  6. Sinha, T., Kapur, M.: When problem solving followed by instruction works: evidence for productive failure. Rev. Educ. Res. 91(5), 761–798 (2021)

    Article  Google Scholar 

  7. Ashman, G., Kalyuga, S., Sweller, J.: Problem-solving or explicit instruction: Which should go first when element interactivity is high? Educ. Psychol. Rev. 32, 229–247 (2020). https://doi.org/10.1007/s10648-019-09500-5

    Article  Google Scholar 

  8. Blake, C., Scanlon, E.: Reconsidering simulations in science education at a distance: features of effective use. J. Comput. Assist. Learn. 23(6), 491–502 (2007)

    Article  Google Scholar 

  9. Bransford, J.D., Schwartz, D.L.: Rethinking Transfer: A Simple Proposal with Multiple Implications, vol. 24. American Educational Research Association, Washington DC (1999)

    Google Scholar 

  10. Chase, C.C., Klahr, D.: Invention versus direct instruction: for some content, it’sa tie. J. Sci. Educ. Technol. 26(6), 582–596 (2017)

    Article  Google Scholar 

  11. Darabi, A., Arrington, T.L., Sayilir, E.: Learning from failure: a meta-analysis of the empirical studies. Educ. Tech. Res. Dev. 66(5), 1101–1118 (2018). https://doi.org/10.1007/s11423-018-9579-9

    Article  Google Scholar 

  12. DeCaro, M.S., McClellan, D.K., Powe, A., Franco, D., Chastain, R.J., Hieb, J.L., Fuselier, L.: Exploring an online simulation before lecture improves undergraduate chemistry learning. International Society of the Learning Sciences (2022)

    Google Scholar 

  13. de Jong, T.: Learning and instruction with computer simulations. Educ. Comput. 6, 217–229 (1991)

    Article  Google Scholar 

  14. de Jong, T.: Technological advances in inquiry learning. Science 312(5773), 532–533 (2006)

    Article  Google Scholar 

  15. Esquembre, F.: Computers in physics education. Comput. Phys. Commun. 147(1–2), 13–18 (2002)

    Article  Google Scholar 

  16. Finkelstein, N.D., Adams, W.K., Keller, C.J., Kohl, P.B., Perkins, K.K., Podolefsky, N.S., et al.: When learning about the real world is better done virtually: a study of substituting computer simulations for laboratory equipment. Phys. Rev. Spec. Topics-Phys. Educ. Res. 1(1), 10103 (2005)

    Article  Google Scholar 

  17. Horn, M., Baker, J., Wilensky, U.: NetTango Web 1.0alpha. [Computer Software]. Evanston, IL. Center for Connected Learning and Computer Based Modeling, Northwestern University. http://ccl.northwestern.edu/nettangoweb/ (2020)

  18. Hsu, C.-Y., Kalyuga, S., Sweller, J.: When should guidance be presented during physics instruction? Arch. Sci. Psychol. 3(1), 37–53 (2015)

    Google Scholar 

  19. Kirschner, P., Sweller, J., Clark, R.E.: Why unguided learning does not work: an analysis of the failure of discovery learning, problem-based learning, experiential learning and inquiry-based learning. Educ. Psychol. 41(2), 75–86 (2006)

    Article  Google Scholar 

  20. Kuhn, D., Ramsey, S., Arvidsson, T.S.: Developing multivariable thinkers. Cogn. Dev. 35, 92–110 (2015)

    Article  Google Scholar 

  21. Matlen, B.J., Klahr, D.: Sequential effects of high and low instructional guidance on children’s acquisition of experimentation skills: is it all in the timing? Instr. Sci. 41(3), 621–634 (2013)

    Article  Google Scholar 

  22. Moser, S., Zumbach, J., Deibl, I.: The effect of metacognitive training and prompting on learning success in simulation-based physics learning. Sci. Educ. 101(6), 944–967 (2017)

    Article  Google Scholar 

  23. Nathan, M.J.: Knowledge and situational feedback in a learning environment for algebra story problem solving. Interact. Learn. Environ. 5(1), 135–159 (1998)

    Article  Google Scholar 

  24. Schwartz, D.L., Bransford, J.D.: A time for telling. Cogn. Instr. 16(4), 475–522 (1998)

    Article  Google Scholar 

  25. Schwartz, D.L., Martin, T.: Inventing to prepare for future learning: The hidden efficacy of encouraging original student production in statistics instruction. Cogn. Instr. 22, 129–184 (2004)

    Article  Google Scholar 

  26. Stockard, J., Wood, T.W., Coughlin, C., Rasplica Khoury, C.: The effectiveness of direct instruction curricula: a meta-analysis of a half century of research. Rev. Educ. Res. 88(4), 479–507 (2018). https://doi.org/10.3102/0034654317751919

    Article  Google Scholar 

  27. Waldmann, M.R.: Combining versus analyzing multiple causes: how domain assumptions and task context affect integration rules. Cogn. Sci. 31(2), 233–256 (2007)

    Article  Google Scholar 

  28. Wieman, C.E., Adams, W.K., Perkins, K.K.: PhET: simulations that enhance learning. Science 322(5902), 682–683 (2008)

    Article  Google Scholar 

  29. Wilensky, U.: NetLogo models library [Computer software]. In Center for connected learning and computer-based modeling. Northwestern University (1999). http://cclnorthwestern.edu/netlogo/models/

  30. Wu, H.K., Wu, P.H., Zhang, W.X., Hsu, Y.S.: Investigating college and graduate students’ multivariable reasoning in computational modeling. Sci. Educ. 97(3), 337–366 (2013)

    Article  Google Scholar 

  31. Zohar, A.: Reasoning about interactions between variables. J. Res. Sci. Teach. 32(10), 1039 (1995)

    Article  Google Scholar 

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Correspondence to Janan Saba .

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Saba, J., Kapur, M., Roll, I. (2023). The Development of Multivariable Causality Strategy: Instruction or Simulation First?. In: Wang, N., Rebolledo-Mendez, G., Matsuda, N., Santos, O.C., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2023. Lecture Notes in Computer Science(), vol 13916. Springer, Cham. https://doi.org/10.1007/978-3-031-36272-9_4

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  • DOI: https://doi.org/10.1007/978-3-031-36272-9_4

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