Revision as an essential step in modeling to support predicting, observing, and explaining cellular respiration system dynamics

References

• Abrahamson, D., Blikstein, P., & Wilensky, U. (2007). Classroom model, model classroom: Computer-supported methodology for investigating collaborative-learning pedagogy. In C. Chinn, G. Erkens, & S. Puntambekar (Eds.), Proceedings of the computer supported collaborative learning (CSCL) conference (Vol. 8, Part 1, pp. 46–55). Rutgers University.
   Google Scholar
• 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
   Web of Science ®Google Scholar
• American Association for the Advancement of Science (AAAS). (2011). Vision and change in undergraduate biology education. http://visionandchange.org/files/2011/03/Revised-Vision-and-Change-Final-Report.pdf.
   Google Scholar
• Baker, R., Walonoski, J., Heffernan, N., Roll, I., Corbett, A., & Koedinger, K. (2008). Why students engage in “gaming the system” behavior in interactive learning environments. Journal of Interactive Learning Research, 19(2), 185–224. https://www.learntechlib.org/primary/p/24328/
   Google Scholar
• Bartley, J., Riedel, M., Salo, T., Boeving, E., Bottenhorn, K., Bravo, E., Odean, R., Nazareth, A., Laird, R., Sutherland, M., Pruden, S., Brewe, E., & Laird, A. (2019). Brain activity links performance in science reasoning with conceptual approach. npj Science of Learning, 4(1), 20. https://doi.org/10.1038/s41539-019-0059-8
   PubMedGoogle Scholar
• Bartocci, E., & Lió, P. (2016). Computational modeling, formal analysis, and tools for systems biology. PLoS Computational Biology, 12(1), e1004591. https://doi.org/10.1371/journal.pcbi.1004591
   PubMed Web of Science ®Google Scholar
• Bergan-Roller, H. E., Galt, N. J., Chizinski, C. J., Helikar, T., & Dauer, J. T. (2018). Simulated computational model lesson improves foundational systems thinking skills and conceptual knowledge in biology students. BioScience, 68(8), 612–621. https://doi.org/10.1093/biosci/biy054
   Web of Science ®Google Scholar
• Bergan-Roller, H. E., Galt, N. J., Dauer, J. T., & Helikar, T. (2017). Discovering cellular respiration with computational modeling and simulations. CourseSource. https://doi.org/10.24918/cs.2017.10
   Google Scholar
• Bergan-Roller, H. E., Galt, N. J., Helikar, T., & Dauer, J. T. (2020). Using concept maps to characterise cellular respiration knowledge in undergraduate students. Journal of Biological Education, 54(1), 33–46. https://doi.org/10.1080/00219266.2018.1541001
   Web of Science ®Google Scholar
• Booth, C. S., Song, C., Howell, M. E., Rasquinha, A., Saska, A., Helikar, R., Sikich, S. M., Couch, B. A., van Dijk, K., Roston, R. L., & Helikar, T. (2021). Teaching metabolism in upper-division undergraduate biochemistry courses using online computational systems and dynamical models improves student performance. CBE-Life Sciences Education, 20(1), ar13 1–16. https://doi.org/10.1187/cbe.20-05-0105
   Web of Science ®Google Scholar
• Brodland, G. W. (2015). How computational models can help unlock biological systems. Seminars in Cell & Developmental Biology, 47-48, 62–73. https://doi.org/10.1016/j.semcdb.2015.07.001
   PubMed Web of Science ®Google Scholar
• Brookman-Byrne, A., Mareschal, D., Tolmie, A. K., & Dumontheil, I. (2018). Inhibitory control and counterintuitive science and maths reasoning in adolescence. PLoS ONE, 13(6), e0198973. https://doi.org/10.1371/journal.pone.0198973
   PubMed Web of Science ®Google Scholar
• Brown, D. (2003). High school biology: A group approach to concept mapping. The American Biology Teacher, 65(3), 192–197. https://doi.org/10.2307/4451473
   Web of Science ®Google Scholar
• Buckley, B. C., Gobert, J. D., Kindfield, A. C. H., Horwitz, P., Tinker, R. F., Gerlits, B., Wilensky, U., Dede, C., & Willett, J. (2004). Model-based teaching and learning with BioLogica™: what do they learn? How do they learn? How do we know? Journal of Science Education and Technology, 13(1), 23–41. https://doi.org/10.1023/B:JOST.0000019636.06814.e3
   Google Scholar
• Çakir, Ö, Geban, Ö, & Yürük, N. (2002). Effectiveness of conceptual change text-oriented instruction on students’ understanding of cellular respiration concepts. Biochemistry and Molecular Biology Education, 30(4), 239–243. https://doi.org/10.1002/bmb.2002.494030040095
   Web of Science ®Google Scholar
• Cinici, A., & Demir, Y. (2013). Teaching through cooperative POE tasks: A path to conceptual change. The Clearing House: A Journal of Educational Strategies, Issues and Ideas, 86(1), 1–10. https://doi.org/10.1080/00098655.2012.712557
   Google Scholar
• Clement, J. (2000). Model based learning as a key research area for science education. International Journal of Science Education, 22(9), 1041–1053. https://doi.org/10.1080/095006900416901
   Web of Science ®Google Scholar
• Coştu, B., Ayas, A., & Niaz, M. (2012). Investigating the effectiveness of a POE-based teaching activity on students’ understanding of condensation. Instructional Science, 40(1), 47–67. https://doi.org/10.1007/s11251-011-9169-2
   Web of Science ®Google Scholar
• Dam, M., Ottenhof, K., Van Boxtel, C., & Janssen, F. (2019). Understanding cellular respiration through simulation using lego® as a concrete dynamic model. Education Sciences, 9(2), 72. https://doi.org/10.3390/educsci9020072
   Web of Science ®Google Scholar
• Dauer, J. T., & Long, T. M. (2015). Long-term conceptual retrieval by college biology majors following model-based instruction. Journal of Research in Science Teaching, 52(8), 1188–1206. https://doi.org/10.1002/tea.21258
   Web of Science ®Google Scholar
• Dauer, J. T., Momsen, J. L., Bray Speth, E., Makohon-Moore, S. C., & Long, T. M. (2013). Analyzing change in students’ gene-to-evolution models in college-level introductory biology. Journal of Research in Science Teaching, 50(6), 639–659. https://doi.org/10.1002/tea.21094
   Web of Science ®Google Scholar
• de Jong, T. (2006). Technological advances in inquiry learning. Science, 312(5773), 532–533. https://doi.org/10.1126/science.1127750
   PubMed Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Doerr, H. M. (1997). Experiment, simulation and analysis: An integrated instructional approach to the concept of force. International Journal of Science Education, 19(3), 265–282. https://doi.org/10.1080/0950069970190302
   Web of Science ®Google Scholar
• Driver, R., Squires, A., Rushworth, P., & Wood-Robinson, V. (2014). Making sense of secondary science: Research into children’s ideas 2nd edition. Routledge.
   Google Scholar
• Flores, F., Tovar, M. E., & Gallegos, L. (2003). Representation of the cell and its processes in high school students: An integrated view. International Journal of Science Education, 25(2), 269–286. https://doi.org/10.1080/09500690210126793
   Web of Science ®Google Scholar
• Fugelsang, J., & Dunbar, K. (2005). Brain-based mechanisms underlying complex causal thinking. Neuropsychologia, 43(8), 1204–1213. https://doi.org/10.1016/j.neuropsychologia.2004.10.012
   PubMed Web of Science ®Google Scholar
• Fuhrmann, T., Schneider, B., & Blikstein, P. (2018). Should students design or interact with models? Using the bifocal modelling framework to investigate model construction in high school science. International Journal of Science Education, 40(8), 867–893. https://doi.org/10.1080/09500693.2018.1453175
   Web of Science ®Google Scholar
• Gilbert, S. W. (1991). Model building and a definition of science. Journal of Research in Science Teaching, 28(1), 73–79. https://doi.org/10.1002/tea.3660280107
   Web of Science ®Google Scholar
• Haysom, J., & Bowen, M. (2010). Predict, observe, explain: Activities enhancing scientific understanding. NSTA Press.
   Google Scholar
• Helikar, T., Cutucache, C. E., Dahlquist, L. M., Herek, T., Larson, J., & Rogers, J. A. (2015). Integrating interactive computational modeling in biology curricula. PLoS Computational Biology, 11(3), e1004131. https://doi.org/10.1371/journal.pcbi.1004131
   PubMed Web of Science ®Google Scholar
• Helikar, T., Kowal, B., McClenathan, S., Bruckner, M., Rowley, T., Madrahimov, A., Wicks, B., Shrestha, M., Limbu, K., & Rogers, J. A. (2012). The cell collective: Toward an open and collaborative approach to systems biology. BMC Systems Biology, 6(1), 96. https://doi.org/10.1186/1752-0509-6-96
   PubMedGoogle Scholar
• Hester, S., Nadler, M., Katcher, J., Elfring, L., Dykstra, E., Rezende, L., & Bolger, M. (2018). Authentic inquiry through modeling in biology (AIM-Bio): An introductory laboratory curriculum that increases undergraduates; scientific agency and skills. CBE-Life Sciences Education, 17(4), ar63 1–23. https://doi.org/10.1187/cbe.18-06-0090
   Web of Science ®Google Scholar
• Hewson, P. W., & Hewson, M. G. A. (1984). The role of conceptual conflict in conceptual change and the design of science instruction. Instructional Science, 13(1), 1–13. https://doi.org/10.1007/BF00051837
   Web of Science ®Google Scholar
• Hmelo-Silver, C. E., Marathe, S., & Liu, L. (2007). Fish swim, rocks sit, and lungs breathe: Expert-novice understanding of complex systems. Journal of the Learning Sciences, 16(3), 307–331. https://doi.org/10.1080/10508400701413401
   Web of Science ®Google Scholar
• Kallick, B., & Zmuda, A. (2017). Students at the center: Personalized learning with habits of the mind. ASCD.
   Google Scholar
• Ke, L., Sadler, T. D., Zangori, L., & Friedrichsen, P. J. (2021). Developing and using multiple models to promote scientific literacy in the context of socio-scientific issues. Science & Education, 30, 589–607. https://doi.org/10.1007/s11191-021-00206-1
   Web of Science ®Google Scholar
• King, G. P., Bergan-Roller, H., Galt, N., Helikar, T., & Dauer, J. T. (2019). Modelling activities integrating construction and simulation supported explanatory and evaluative reasoning. International Journal of Science Education, 41(13), 1764–1786. https://doi.org/10.1080/09500693.2019.1640914
   Web of Science ®Google Scholar
• Kirschner, P., Sweller, J., & Clark, R. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75–86. https://doi.org/10.1207/s15326985ep4102_1
   Web of Science ®Google Scholar
• Klahr, D. (2000). Exploring science: The cognition and development of discovery processes. The MIT Press.
   Google Scholar
• Krajcik, J., Berger, C. F., & Czerniak, C. M. (2002). Teaching science in elementary and middle school classrooms: A project-based approach (2nd ed.). McGraw Hill.
   Google Scholar
• Krajcik, J., Blumenfeld, P. C., Marx, R. W., Bass, K. M., & Fredricks, J. (1998). Inquiry in project-based science classrooms: Initial attempts by middle school students. Journal of the Learning Sciences, 7(3&4), 313–350. https://doi.org/10.1080/10508406.1998.9672057
   Web of Science ®Google Scholar
• Krajcik, J., & Merritt, J. (2012). Engaging students in scientific practices: What does constructing and revising models look like in the science classroom? The Science Teacher, 79(3), 38–41. https://www.jstor.org/stable/43557386
   Google Scholar
• Krajcik, J., & Mun, K. (2014). Promises and challenges of using learning technologies to promote student learning of science. In N. G. Lederman, & S. K. Abell (Eds.), Handbook of research on science education: Volume II (pp. 337–360). Routledge.
   Google Scholar
• Krippendorff, K. (2011). Computing krippendorff’s alpha-reliability. Annenberg School for Communication Departmental Papers.
   Google Scholar
• Lazonder, A. W., & Harmsen, R. (2016). Meta-Analysis of inquiry-based learning. Review of Educational Research, 86(3), 681–718. https://doi.org/10.3102/0034654315627366
   Web of Science ®Google Scholar
• Lehrer, R., & Schauble, L. (2000). Developing model-based reasoning in mathematics and science. Journal of Applied Developmental Psychology, 21(1), 39–48. https://doi.org/10.1016/S0193-3973(99)00049-0
   Web of Science ®Google Scholar
• Lesh, R., & Doerr, H. M. (2000). Symbolizing, communicating, and mathematizing: Key components of models and modeling. In P. Cobb, E. Yackel, & K. McClain (Eds.), Symbolizing and communicating in mathematics classrooms. Perspectives on discourse, tools, and instructional design (pp. 361–383). Erlbaum.
   Google Scholar
• Liew, C., & Treagust, D. (1998, April 13-17). The effectiveness of predict-observe-explain tasks in diagnosing students’ understanding of science and in identifying their levels of achievement. Annual Meeting of the American Educational Research Association, San Diego, CA. https://files.eric.ed.gov/fulltext/ED420715.pdf.
   Google Scholar
• Löhner, S., van Joolingen, W. R., Savelsbergh, E. R., & van Hout-Wolters, B. (2005). Students’ reasoning during modeling in an inquiry learning environment. Computers in Human Behavior, 21(3), 441–461. https://doi.org/10.1016/j.chb.2004.10.037
   Web of Science ®Google Scholar
• Louca, L., & Zacharia, Z. (2012). Modeling-based learning in science education: Cognitive, metacognitive, social, material and epistemological contributions. Educational Review, 64(4), 471–492. https://doi.org/10.1080/00131911.2011.628748
   Web of Science ®Google Scholar
• Louca, L., Zacharia, Z., Michael, M., & Constantinou, C. P. (2011). Objects, entities, behaviors and interactions: A typology of student-constructed computer-based models of physical phenomena. Journal of Educational Computing Research, 44(2), 173–201. https://doi.org/10.2190/EC.44.2.c
   Web of Science ®Google Scholar
• Machamer, P., Darden, L., & Craver, C. (2000). Thinking about mechanisms. Philosophy of Science, 67(1), 1–25. https://doi.org/10.1086/392759
   Web of Science ®Google Scholar
• MacLeod, M., & Nersessian, N. J. (2018). Modeling complexity: Cognitive constraints and computational model-building in integrative systems biology. History and Philosophy of the Life Sciences, 40(1), 17. https://doi.org/10.1007/s40656-017-0183-9
   PubMed Web of Science ®Google Scholar
• Mayes, R., Forrester, J., Christus, J. S., Peterson, F., & Walker, R. (2014). Quantitative reasoning learning progression: The matrix. Numeracy, 7(2), 5. https://doi.org/10.5038/1936-4660.7.2.5
   Google Scholar
• McComas, W. F., & Kampourakis, K. (2015). Using the history of biology, chemistry, geology and physics to teach aspects of the nature of science. Review of Science, Mathematics and ICT Education, 9(1), 47–76. https://doi.org/10.26220/REV.2240
   Google Scholar
• Mulder, Y. G., Bollen, L., de Jong, T., & Lazonder, A. W. (2016). Scaffolding learning by modelling: The effects of partially worked-out models. Journal of Research in Science Teaching, 53(3), 502–523. https://doi.org/10.1002/tea.21260
   Web of Science ®Google Scholar
• National Institute of Biomedical Imaging and Bioengineering (NIBIB). (2018). Computational modeling. https://www.nibib.nih.gov/science-education/science-topics/computational-modeling.
   Google Scholar
• National Research Council (NRC). (2000a). How people learn: Brain, mind, experience, and school. The National Academies Press.
   Google Scholar
• National Research Council (NRC). (2000b). Inquiry and the national science education standards: A guide for teaching and learning. The National Academies Press.
   Google Scholar
• National Research Council (NRC). (2003). BIO 2010: Transforming undergraduate education for future research biologists. National Academies Press.
   Google Scholar
• Nenciovici, L., Allaire-Duquette, G., & Masson, S. (2019). Brain activations associated with scientific reasoning: A literature review. Cognitive Processing, 20(2), 139–161. https://doi.org/10.1007/s10339-018-0896-z
   PubMed Web of Science ®Google Scholar
• Nersessian, N. J. (2009). How Do engineering scientists think? Model-based simulation in biomedical engineering research laboratories. Topics in Cognitive Science, 1(4), 730–757. https://doi.org/10.1111/j.1756-8765.2009.01032.x
   PubMed Web of Science ®Google Scholar
• Papaevripidou, M., & Zacharia, Z. C. (2015). Examining how students’ knowledge of the subject domain affects their process of modeling in a computer programming environment. Journal of Computers in Education, 2(3), 251–282. https://doi.org/10.1007/s40692-015-0034-1
   Web of Science ®Google Scholar
• Passmore, C., Stewart, J., & Cartier, J. (2009). Model-based inquiry and school science: Creating connections. School Science and Mathematics, 109(7), 394–402. https://doi.org/10.1111/j.1949-8594.2009.tb17870.x
   Google Scholar
• Penner, D. E. (2000). Chapter 1: Cognition, computers, and synthetic science: Building knowledge and meaning through modeling. Review of Research in Education, 25(1), 1–35. https://doi.org/10.3102/0091732X025001001
   Google Scholar
• Pennington, D., Bammer, G., Danielson, A., Gosselin, D., Gouvea, J., Habron, G., Hawthorne, D., Parnell, R., Thompson, K., Vincent, S., & Wei, C. (2016). The EMBeRS project: Employing model-based reasoning in socio-environmental synthesis. Journal of Environmental Studies and Sciences, 6(2), 278–286. https://doi.org/10.1007/s13412-015-0335-8
   Google Scholar
• Pennycook, G., Fugelsang, J. A., & Koehler, D. J. (2015). What makes US think? A three-stage dual-process model of analytic engagement. Cognitive Psychology, 80, 34–72. https://doi.org/10.1016/j.cogpsych.2015.05.001
   PubMed Web of Science ®Google Scholar
• Perkins, K., Adams, W., Dubsson, M., Finkelstein, N., Reid, S., & Wieman, C. (2006). PhET: Interactive simulations for teaching and learning physics. The Physics Teacher, 44(1), 18–23. https://doi.org/10.1119/1.2150754
   Google Scholar
• Piaget, J. (1985). The equilibration of cognitive structures: The central problem of intellectual development. University of Chicago Press.
   Google Scholar
• Posner, G., Strike, K., Hewson, P., & Gertzog, W. (1982). Accommodation of a scientific conception: Toward a theory of conceptual change. Science Education, 66(2), 211–227. https://doi.org/10.1002/sce.3730660207
   Google Scholar
• Qudrat-Ullah, H. (2010). Perceptions of the effectiveness of system dynamics-based interactive learning environments: An empirical study. Computers & Education, 55(3), 1277–1286. https://doi.org/10.1016/j.compedu.2010.05.025
   Web of Science ®Google Scholar
• Quellmalz, E., Silberglitt, M., Buckley, B., Loveland, M., & Brenner, D. (2016). Simulations for supporting and assessing science literacy. In Y. Rosen, S. Ferrera, & M. Mosharraf (Eds.), Handbook of research on technology tools for real-world skill development (pp. 191–229). IGI Global.
   Google Scholar
• Quintana, C., Reiser, B., Davis, E., Krajcik, J., Fretz, E., Duncan, R. G., Kyza, E., Edelson, D., & Soloway, E. (2004). A scaffolding design framework for software to support science inquiry. Journal of the Learning Sciences, 13(3), 337–386. https://doi.org/10.1207/s15327809jls1303_4
   Web of Science ®Google Scholar
• R Core Team. (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing.
   Google Scholar
• Rogaten, J., Rientes, B., Sharpe, R., Cross, S., Whitelock, D., Lygo-Baker, S., & Littlejohn, A. (2019). Reviewing affective, behavioural and cognitive learning gains in higher education. Assessment & Evaluation in Higher Education, 44(3), 321–337. https://doi.org/10.1080/02602938.2018.1504277
   Web of Science ®Google Scholar
• Rutten, N., van Joolingen, W. R., & van der Veen, J. T. (2012). The learning effects of computer simulations in science education. Computers & Education, 58(1), 136–153. https://doi.org/10.1016/j.compedu.2011.07.017
   Web of Science ®Google Scholar
• Sarma, G., & Faundez, V. (2017). Integrative biological simulation praxis: Considerations from physics, philosophy, and data/model curation practices. Cellular Logistics, 7(4), e1392400. https://doi.org/10.1080/21592799.2017.1392400
   PubMedGoogle Scholar
• Savander-Ranne, C., & Kolari, S. (2003). Promoting the conceptual understanding of engineering students through visualization. Global Journal of Engineering Education, 7(2), 189–199. http://www.wiete.com.au/journals/GJEE/Publish/vol7no2/SavRanneKolari.pdf
   Google Scholar
• Schwarz, C. V., Reiser, B. J., Davis, E. A., Kenyon, L., Achér, A., Fortus, D., Shwartz, Y., Hug, B., & Krajcik, J. (2009). Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching, 46(6), 632–654. https://doi.org/10.1002/tea.20311
   Web of Science ®Google Scholar
• Seel, N. M. (2017). Model-based learning: A synthesis of theory and research. Educational Technology Research and Development, 65(4), 931–966. https://doi.org/10.1007/s11423-016-9507-9
   Web of Science ®Google Scholar
• Seymour, E., & Hewitt, N. (2000). Talking about leaving: Why undergraduates leave the sciences. Westview Press.
   Google Scholar
• Sins, P. H. M., Savelsbergh, E. R., & van Joolingen, W. R. (2005). The difficult process of scientific modelling: An analysis of novices' reasoning during computer-based modelling. International Journal of Science Education, 27(14), 1695–1721. https://doi.org/10.1080/09500690500206408
   Web of Science ®Google Scholar
• Soderberg, P., & Price, F. (2003). An examination of problem-based teaching and learning in population genetics and evolution using EVOLVE, a computer simulation. International Journal of Science Education, 25(1), 35–55. https://doi.org/10.1080/09500690110095285
   Web of Science ®Google Scholar
• Songer, C., & Mintzes, J. (1994). Understanding cellular respiration: An analysis of conceptual change in college biology. Journal of Research in Science Teaching, 31(6), 621–637. https://doi.org/10.1002/tea.3660310605
   Web of Science ®Google Scholar
• Southard, K., Wince, T., Meddleton, S., & Bolger, M. S. (2016). Features of knowledge building in biology: Understanding undergraduate students’ ideas about molecular mechanisms. CBE—Life Sciences Education, 15(1), ar7 1–16. https://doi.org/10.1187/cbe.15-05-0114.
   Web of Science ®Google Scholar
• Svoboda, J., & Passmore, C. (2013). The strategies of modeling in biology education. Science & Education, 22(1), 119–142. https://doi.org/10.1007/s11191-011-9425-5
   Web of Science ®Google Scholar
• Sweeney, L. B., & Sterman, J. (2007). Thinking about systems: Student and teacher conceptions of natural and social systems. System Dynamics Review, 23(2/3), 285–311. https://doi.org/10.1002/sdr.366
   Web of Science ®Google Scholar
• Usher, D. C., Driscoll, T. A., Dhurjati, P., Pelesko, J. A., Rossi, L. F., Schleiniger, G., Pusecker, K., & White, H. B. (2010). A transformative model for undergraduate quantitative biology education. CBE—Life Sciences Education, 9(3), 181–188. https://doi.org/10.1187/cbe.10-03-0029
   PubMed Web of Science ®Google Scholar
• van Joolingen, W., de Jong, T., Lazonder, A. W., Savelsbergh, E. R., & Manlove, S. (2005). Co-Lab: Research and development of an online learning environment for collaborative scientific discovery learning. Computers in Human Behavior, 21(4), 671–688. https://doi.org/10.1016/j.chb.2004.10.039
   Web of Science ®Google Scholar
• van Mil, M., Boerwinkel, D. J., & Waarlo, A. J. (2013). Modelling molecular mechanisms: A framework of scientific reasoning to construct molecular-level explanations for cellular behaviour. Science & Education, 22(1), 93–118. https://doi.org/10.1007/s11191-011-9379-7
   Web of Science ®Google Scholar
• White, R. T., & Gunstone, R. F. (1992). Probing understanding. The Falmer Press.
   Google Scholar
• Wijnen, F. M., Mulder, Y. G., Alessi, S. M., & Bollen, L. (2015). The potential of learning from erroneous models: Comparing three types of model instruction. System Dynamics Review, 31(4), 250–270. https://doi.org/10.1002/sdr.1546
   Web of Science ®Google Scholar
• Wilensky, U., & Rand, W. (2015). An introduction to agent-based modeling: Modeling natural, social, and engineered complex systems with NetLogo. Cambridge, massachusetts. The MIT Press.
   Google Scholar
• Wilkerson, M., Shareff, B., Gravel, B., Shaban, Y., & Laina, V. (2017). Exploring computational modeling environments as tools to structure classroom-level knowledge building. In B. K. Smith, M. Borge, E. Mercier, & K. Y. Lim (Eds.), Making a difference: Prioritizing equity and access in CSCL, 12th international conference on computer supported collaborative learning (CSCL), volume 1 (pp. 447–454). International Society of the Learning Sciences.
   Google Scholar
• Windschitl, M., Thompson, J., & Braaten, M. (2008). Beyond the scientific method: Model-based inquiry as a new paradigm of preference for school science investigations. Science Education, 92(5), 941–967. https://doi.org/10.1002/sce.20259
   Web of Science ®Google Scholar
• Xenofontos, N. A., Hovardas, T., Zacharia, Z. C., & de Jong, T. (2019). Inquiry-based learning and retrospective action: Problematizing student work in a computer-supported learning environment. Journal of Computer Assisted Learning, 36(1), 12–28. https://doi.org/10.1111/jcal.12384
   Web of Science ®Google Scholar
• Zacharia, Z. C. (2005). The impact of interactive computer simulations on the nature and quality of postgraduate science teachers’ explanations in physics. International Journal of Science Education, 27(14), 1741–1767. https://doi.org/10.1080/09500690500239664
   Web of Science ®Google Scholar