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guidance inquiry learning prompts simulation

Enhancing Scientific Discovery Learning by Just-in-Time Prompts in a Simulation-Assisted Inquiry Environment

Shiva Hajian , Misha Jain , Arita L. Liu , Teeba Obaid , Mari Fukuda , Philip H. Winne , John C. Nesbit

We investigated the effects of just-in-time guidance at various stages of inquiry learning by novice learners. Thirteen participants, randomly assigne.

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We investigated the effects of just-in-time guidance at various stages of inquiry learning by novice learners. Thirteen participants, randomly assigned to an intervention (n = 8) or control (n = 5) group, were observed as they learned about DC electric circuits using a web-based simulation. Just-in-time instructional prompts to observe, predict, explain, systematically test, collect evidence, and generate rules were strongly associated with diagnosing and correcting misconceptions, and constructing correct scientific concepts. Students’ repeated use of predictions, systematic testing, and evidence-coordinated reasoning often led to formulating new principles, generalizing from observed patterns, verifying comprehension, and experiencing “Aha!” moments. Just-in-time prompts helped learners manage embedded cognitive challenges in inquiry tasks, achieve a comprehensive understanding of the model represented in the simulation, and show significantly higher knowledge gain. Just-in-time prompts also promoted rejection of incorrect models of inquiry and construction of robust scientific mental models. The results suggest ways of customizing guidance to promote scientific learning within simulation environments.

Keywords: Guidance, inquiry learning, prompts, simulation.

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References

Alfieri, L., Brooks, P. J., Aldrich, N. J., & Tenenbaum, H. R. (2011). Does discovery-based instruction enhance learning? Journal of Educational Psychology, 103(1), 1–18. https://doi.org/10.1037/a0021017

Alwan, A. A. (2011). Misconception of heat and temperature among physics students. Procedia - Social and Behavioral Sciences, 12, 600–614. https://doi.org/10.1016/j.sbspro.2011.02.074

Anderson, L. W., & Krathwohl, D. R. (Eds.). (2001). A taxonomy for learning, teaching and assessing: A revision of Bloom's Taxonomy of educational objectives: Complete edition. Longman.

Ausubel, D. P. (1968). Educational psychology: A cognitive view. Holt, Rinehart and Winston, Inc.

Balim, A. G. (2009). The Effects of discovery learning on students’ success and inquiry learning skills. Eurasian Journal of Educational Research, 35, 1–20.

Belcastro, S. M. (2017). Ask questions to encourage questions asked. PRIMUS, 27(2), 171-178. https://doi.org/10.1080/10511970.2016.1171813

Bisra, K., Liu, Q., Nesbit, J. C., Salimi, F., & Winne, P. H. (2018). Inducing self-explanation: A meta-analysis. Educational Psychology Review, 30(3), 703–725. https://doi.org/10.1007/s10648-018-9434-x

Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (1999). How people learn: Brain, mind, experience, and school. National Academy Press.

Bybee, R., & Landes, N. M. (1990). Science for life and living: An elementary school science program from Biological Sciences Improvement Study (BSCS). The American Biology Teacher, 52(2), 92-98. https://doi.org/10.2307/4449042

Bybee, R. W. (2006). Scientific inquiry and science teaching. In L. B. Flick & N. G. Lederman (Eds.), Scientific Inquiry and Nature of Science: Implications for Teaching, Learning, and Teacher Education (pp. 1–14). Springer. https://doi.org/10.1007/978-1-4020-5814-1_1

Cardak, O. (2009). Science students’ misconceptions of the water cycle according to their drawings. Journal of Applied Sciences, 9(5), 865-873. https://doi.org/10.3923/jas.2009.865.873

Chang, K. E., Liu, S. H., & Chen, S. W. (1998). A testing system for diagnosing misconceptions in DC electric circuits. Computers & Education, 31(2), 195-210. http://dx.doi.org/10.1016/S0360-1315(98)00030-X

Chi, M. T. H. (2005). Commonsense conceptions of emergent processes: Why some misconceptions are robust. Journal of the Learning Sciences, 14(2), 161–199. https://doi.org/10.1207/s15327809jls1402_1

Chi, M. T. H. (2008). Three types of conceptual change: Belief revision, mental model transformation, and categorical shift. In S. Vosniadou (Ed.), Handbook of research on conceptual change (pp. 61-82). Lawrence Erlbaum Associates, Inc.

Chi, M. T., De Leeuw, N., Chiu, M. H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18(3), 439-477. https://doi.org/10.1207/s15516709cog1803_3

Chin, C., & Brown, D. E. (2000). Learning in science: A comparison of deep and surface approaches. Journal of Research in Science Teaching, 37(2), 109–138. https://doi.org/10.1002/(SICI)1098-2736(200002)37:2<109::AID-TEA3>3.0.CO;2-7

Clement, J. (1989). Learning via model construction and criticism: Protocol evidence on sources of creativity in science. In J. A. Glover, R. R. Ronning, & C. R. Reynolds (Eds.), Perspectives on individual differences. Handbook of creativity (pp. 341–381). Plenum Press. https://doi.org/10.1007/978-1-4757-5356-1_20

Clement, J., Brown, D. E., & Zietsman, A. (1989). Not all preconceptions are misconceptions: Finding ‘anchoring conceptions’ for grounding instruction on students’ intuitions. International Journal of Science Education, 11(5), 554-565. https://doi.org/10.1080/0950069890110507

Crawford, B. A. (2000). Embracing the essence of inquiry: New roles for science teachers. Journal of Research in Science Teaching, 37(9), 916–937. https://doi.org/10.1002/1098-2736(200011)37:9<916::AID-TEA4>3.0.CO;2-2

Dalgarno, B., Kennedy, G., & Bennett, S. (2014). The impact of students' exploration strategies on discovery learning using computer-based simulations. Educational Media International, 51(4), 310–329. https://doi.org/10.1080/09523987.2014.977009

de Jong, T., Martin, E., Zamarro, J.-M., Esquembre, F., Swaak, J., & van Joolingen, W. R. (1999). The integration of computer simulation and learning support: An example from the physics domain of collisions. Journal of Research in Science Teaching, 36(5), 597–615. https://doi.org/10.1002/(SICI)1098-2736(199905)36:5<597::AID-TEA6>3.0.CO;2-6

de Jong, T., & van Joolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68(2), 179-201. https://doi.org/10.3102/00346543068002179

Duran, E., Duran, L., Haney, J., & Scheuermann, A. (2011). A learning cycle for all students: Modifying the 5E instructional model to address the needs of all learners. The Science Teacher, 78(3), 56-60.

Duran, L. B., & Duran, E. (2004). The 5E instructional model: A learning cycle approach for inquiry-based science teaching. Science Education Review, 3(2), 49-58.

Durkin, K., & Rittle-Johnson, B. (2012). The effectiveness of using incorrect examples to support learning about decimal magnitude. Learning and Instruction, 22(3), 206–214. https://doi.org/10.1016/j.learninstruc.2011.11.001

Eberbach, C., & Crowley, K. (2009). From everyday to scientific observation: How children learn to observe the biologist’s world. Review of Educational Research, 79(1), 39–68. https://doi.org/10.3102/0034654308325899

Gautreau, B. T., & Binns, I. C. (2012). Investigating student attitudes and achievements in an environmental place-based inquiry in secondary classrooms. International Journal of Environmental & Science Education, 7(2), 167-195.

Gormally, C., Brickman, P., Hallar, B., & Armstrong, N. (2009). Effects of inquiry-based learning on students' science literacy skills and confidence. International Journal for the Scholarship of Teaching and Learning, 3(2), 1-22. https://doi.org/10.20429/ijsotl.2009.030216

Hajian, S. (2018). The benefits and challenges of analogical comparison in learning and transfer: Can self-explanation scaffold analogy in the process of learning? SFU Educational Review, 11(1), 60–74. https://doi.org/10.21810/sfuer.v11i1.599

Hajian, S. (2019). Transfer of learning and teaching: A review of transfer theories and effective instructional practices. IAFOR Journal of Education, 7(1), 93 – 11. https://doi.org/10.22492/ije.7.1.06

Hajian, S., Obaid, T., Jain, M., & Nesbit, J. (2019). Inquiry learning with an interactive physics simulation: What exploratory strategies lead to success? Journal of Interactive Learning Research, 30(4), 451- 476.

Hamilton, R. (2012). Elaboration effects on learning. In N. M. Seel (Ed.), Encyclopedia of the Sciences of Learning (pp. 1103–1105). Springer. https://doi.org/10.1007/978-1-4419-1428-6_170

Hitt, A. M., & Smith, D. (2017). Filling in the gaps: An explicit protocol for scaffolding inquiry lessons. Science Educator, 25(2), 133–141. https://files.eric.ed.gov/fulltext/EJ1132102.pdf

Holyoak, K. J., & Morrison, R. G. (Eds.). (2005). The Cambridge handbook of thinking and reasoning (Vol. 137).  Cambridge University Press.

Hong, J. C., Hwang, M. Y., Liu, M. C., Ho, H. Y., & Chen, Y. L. (2014). Using a "prediction-observation-explanation" inquiry model to enhance student interest and intention to continue science learning predicted by their Internet cognitive failure. Computers and Education, 72, 110-120. https://doi.org/10.1016/j.compedu.2013.10.004

Jonassen, D. H. (2000). Toward a design theory of problem solving. Educational Technology Research and Development, 48(4), 63–85. https://doi.org/10.1007/BF02300500

Jones, G. (2003). Testing two cognitive theories of insight. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29(5), 1017–1027. https://doi.org/10.1037/0278-7393.29.5.1017

Kearsley, G., & Shneiderman, B. (1998). Engagement theory: A framework for technology-based teaching and learning. Educational Technology, 38(5), 20–23.

Küçüközer, H., & Kocakülah, S. (2007). Secondary school students’ misconceptions about simple electric circuits. Journal of Turkish Science Education, 4(1), 101-115. https://doi.org/10.1.1.494.309

Kuhn, D., Schauble, L., & Garcia-Mila, M. (1992). Cross-domain development of scientific reasoning. Cognition and Instruction, 9(4), 285–327. https://doi.org/10.1207/s1532690xci0904_1

Langley, P. (2019). Scientific discovery, causal explanation, and process model induction. Mind & Society, 18(1), 43–56. https://doi.org/10.1007/s11299-019-00216-1

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

Lim, K. H., Buendia, G., Kim, O. K., Cordero, F., & Kasmer, L. (2010). The role of prediction in the teaching and learning of mathematics. International Journal of Mathematical Education in Science and Technology, 41(5), 595-608. https://doi.org/10.1080/00207391003605239

Lodge, J. M., Kennedy, G., Lockyer, L., Arguel, A., & Pachman, M. (2018). Understanding difficulties and resulting confusion in learning: An integrative review. Frontiers in Education, 3(49). https://doi.org/10.3389/feduc.2018.00049

Metcalf, S. J., Reilly, J. 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 physical concept mapping. Computers in Human Behavior, 87, 459–469. https://doi.org/10.1016/j.chb.2018.03.018

Morley, D., Bettles, S., & Derham, C. (2019). The exploration of students’ learning gain following immersive simulation–the impact of feedback. Higher Education Pedagogies, 4(1), 368-384. https://doi.org/10.1080/23752696.2019.1642123

Neidorf, T., Arora, A., Erberber, E., Tsokodayi, Y., & Mai, T. (2020). Review of research into misconceptions and misunderstandings in physics and mathematics. In T. Neidorf, A. Arora, E. Erberber, Y. Tsokodayi, & T. Mai (Eds.), Student Misconceptions and Errors in Physics and Mathematics: Exploring Data from TIMSS and TIMSS Advanced. 11–20. Springer International Publishing. https://doi.org/10.1007/978-3-030-30188-0_2

Newman, F. M. (1990). Higher order thinking in teaching social studies: A rationale for the assessment of classroom thoughtfulness. Journal of Curriculum Studies, 22, 41-56. https://doi.org/10.1080/0022027900220103

Nokes, T. J., Hausmann, R. G. M., VanLehn, K., & Gershman, S. (2011). Testing the instructional fit hypothesis: The case of self-explanation prompts. Instructional Science, 39(5), 645–666. https://doi.org/10.1007/s11251-010-9151-4

Norris, S. P. (1985). The philosophical basis of observation in science and science education. Journal of Research in Science Teaching, 22(9), 817-833. https://doi.org/10.1002/tea.3660220905

Orgill, M., & Thomas, M. (2007). Analogies and the 5E model. The Science Teacher, 74(1), 40-45.

Pedaste, M., Mäeots, M., Siiman, L. A., de Jong, T., van Riesen, S. A. N., Kamp, E. T., Manoli, C. C., Zacharia, Z. C., & Tsourlidaki, E. (2015). Phases of inquiry-based learning: Definitions and the inquiry cycle. Educational Research Review, 14, 47–61. https://doi.org/10.1016/j.edurev.2015.02.003

Peng, W., & Gero, J. S. (2010). Concept formation in scientific knowledge discovery from a constructivist view. In M. M. Gaber (Ed.), Scientific data mining and knowledge discovery: Principles and foundations (pp. 91–109). Springer. https://doi.org/10.1007/978-3-642-02788-8_5

Peterson, N. S., & Jungck, J. R. (1988). Problem-posing, problem-solving and persuasion in biology education. Academic Computing, 2(6), 14-17. https://doi.org/10.1016/0307-4412(89)90047-2

Piyayodilokchai, H., Panjaburee, P., Laosinchai, P., Ketpichainarong, W., & Ruenwongsa, P. (2013). A 5E learning cycle approach–based, multimedia-supplemented instructional unit for structured query language. Journal of Educational Technology & Society; Palmerston North, 16(4), 146–159.

Prastyaningrum, I., & Pratama, H. (2019). Student conception of Ohm’s law. Journal of Physics: Conference Series, 1321, 022-028. https://doi.org/10.1088/1742-6596/1321/2/022028

Putra, F., Nur Kholifah, I. Y., Subali, B., & Rusilowati, A. (2018). 5E-Learning cycle strategy: Increasing conceptual understanding and learning motivation. Al-Biruni Journal of Physics Education, 7(2), 171 - 181. https://doi.org/10.24042/jipfalbiruni.v7i2.2898

Rawson, K. A., & Dunlosky, J. (2007). Improving students’ self-evaluation of learning for key concepts in textbook materials. European Journal of Cognitive Psychology, 19(4–5), 559–579. https://doi.org/10.1080/09541440701326022

Reid, D. J., Zhang, J., & Chen, Q. (2003). Supporting scientific discovery learning in a simulation environment. Journal of Computer Assisted Learning, 19(1), 9–20. https://doi.org/10.1046/j.0266-4909.2003.00002.x

Resnick, L. B. (1987). Education and learning to think. National Academy Press.

Rittle-Johnson, B., & Loehr, A. M. (2017). Eliciting explanations: Constraints on when self-explanation aids learning. Psychonomic bulletin & review24(5), 1501-1510. http://dx.doi.org/10.3758/s13423-016-1079-5

Rittle-Johnson, B., & Star, J. R. (2011). Chapter Seven - The power of comparison in learning and Instruction: Learning outcomes supported by different types of comparisons. In J. P. Mestre & B. H. Ross (Eds.), Psychology of Learning and Motivation (Vol. 55, pp. 199–225). Academic Press. https://doi.org/10.1016/B978-0-12-387691-1.00007-7

Roy, M., & Chi, M. T. (2005). The self-explanation principle in multimedia learning. The Cambridge Handbook of Multimedia Learning, 271-286. https://doi.org/10.1017/CBO9781139547369.021

Sandoval, W., & Reiser, B. (2004). Explanation-driven inquiry: Integrating conceptual and epistemic scaffolds for scientific inquiry. Science Education, 88 (3), 345–372. https://doi.org/10.1002/sce.10130

SAS Institute. (2019). JMP (Version 14) [Computer Software]. https://www.jmp.com/en_ca/software/data-analysis-software.html

Skemp, R. R. (1976). Relational understanding and instrumental understanding. Mathematics Teaching, 77, 20-26.

Smith, B. K., & Reiser, B. J. (2005). Explaining behavior through observational investigation and theory articulation. The Journal of the Learning Sciences, 14(3), 315-360. https://doi.org/10.1207/s15327809jls1403_1

Srisawasdi, N., & Panjaburee, P. (2015). Exploring effectiveness of simulation-based inquiry learning in science with integration of formative assessment. Journal of Computers in Education, 2(3), 323–352. https://doi.org/10.1007/s40692-015-0037-y

Tatar, E., & Oktay, M. (2007). Students' misunderstandings about the energy conservation principle: A general view to studies in literature. International Journal of Environmental and Science Education, 2(3), 79-81.

Turgut, Ü., Gürbüz, F., & Turgut, G. (2011). An investigation of 10th grade students’ misconceptions about electric current. Procedia - Social and Behavioral Sciences, 15, 1965–1971. https://doi.org/10.1016/j.sbspro.2011.04.036

van der Valk, T., & de Jong, O. (2009). Scaffolding science teachers in open‐inquiry teaching. International Journal of Science Education, 31(6), 829-850. https://doi.org/10.1080/09500690802287155

van Joolingen, W., & de Jong, T. (1997). An extended dual search space model of scientific discovery learning. Instructional Science, 25(5), 307-346. https://doi.org/10.1023/A:1002993406499

VanLehn, K., Jones, R. M., & Chi, M. T. (1992). A model of the self-explanation effect. The Journal of the Learning Sciences, 2(1), 1-59. https://doi: 10.1207/s15327809jls0201_1

Veermans, M., Lallimo, J., & Hakkarainen, K. (2005). Patterns of guidance in inquiry learning. Journal of Interactive Learning Research, 16(2), 179-194.

White, B. Y., & Frederiksen, J. R. (1998). Inquiry, modeling, and metacognition: Making science accessible to all students. Cognition and Instruction, 16 (1), 3–118. https://doi.org/10.1207/s1532690xci1601_2

Williams, J. J., & Lombrozo, T. (2010). The Role of explanation in discovery and generalization: Evidence from category learning. Cognitive Science, 34(5), 776–806. https://doi.org/10.1111/j.1551-6709.2010.01113.x

Wrenn, J., & Wrenn, B. (2009). Enhancing learning by integrating theory and practice. International Journal of Teaching and Learning in Higher Education, 21(2), 258–265.

Zhang, T., Chen, A., & Ennis, C. (2019). Elementary school students’ naïve conceptions and misconceptions about energy in physical education context. Sport, Education and Society, 24(1), 25–37. https://doi.org/10.1080/13573322.2017.1292234

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