Abstract
This chapter focuses on model-based inquiry in computer-supported environments, especially through the use of the Go-Lab platform (www.golabz.eu). Go-Lab is an online learning platform that offers students the opportunity to engage in inquiry-based science learning, in a structured and supportive manner, by providing environments for learning (i.e., Inquiry Learning Spaces), where virtual or remote laboratories and software scaffolds (e.g., tools for generating hypotheses and designing experiments) that support inquiry learning processes have been integrated. The purpose of this chapter is to unravel how the Go-Lab platform, especially some of its virtual laboratories, can be used for model-based learning. In so doing, we discuss core requirements for model-based inquiry in expressing, testing, and revising models. Further, we present three examples of Go-Lab virtual laboratories, with modeling and simulation affordances, to explain how they could be used by educators as means for enacting model-based inquiry.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Broadly approached, terminology on modeling would separate among different modeling pedagogies (van Joolingen et al. 2005; Campbell et al. 2013), i.e., “expressive” modeling has been largely related to elicitation of students’ initial ideas, namely, students’ initial mental models, “experimental” modeling would necessitate empirical data to validate a model, “evaluative” modeling would involve screening among rival versions of a model, “exploratory” modeling would be operationalized by means of a ready-made model (i.e., a model which was not created by students themselves), and “cyclic” modeling would include model revision.
- 2.
Close-ended simulations do not offer students the option of expressing their mental models, because the model is already there. In this case, possible relations between variables would have to be assumed/discovered. It is an issue whether this variable-by-variable approach would allow the student to grasp a complete picture of the whole phenomenon under study, as if one would have expected based on a modeling procedure, during which the whole phenomenon would be modeled and remodeled. After all, the design rationale behind any modeling tool has been to first give students the opportunity to create a model and then simulate it. It could be that we might isolate a limited number of variables to study a phenomenon. However, nonlinear thinking and system dynamics with feedback mechanisms and delay cannot be easily addressed with matching variables in pairs of two, where we mostly presuppose linear relationships between two variables at a time. Here we come across epistemological issues linking model-based inquiry to systems thinking, where the latter cannot be facilitated without the former.
- 3.
With regard to the inquiry cycle, “exploratory” modeling (i.e., students working with ready-made models) might not always equate to the exploration trajectory in the inquiry cycle as defined by Pedaste et al. (2015). For instance, the exploration trajectory is distinguished from the experimentation trajectory in the inquiry cycle in that the first incorporates research questions, while the latter presupposes hypotheses. However, “exploratory modeling” might accommodate both questions and hypotheses.
- 4.
Learning products that are created by students themselves as they go through a learning activity sequence have been characterized as “emerging learning objects (ELOs)” in the frame of the Science Created by You (SCY) project (see de Jong et al. 2010, 2012). These can include concept maps, models, questions, hypotheses, experimental designs, tables or figures with simulation data, and any other artifact that is the product of student work and can be stored and recalled upon demand for educational purposes. Learning products provide a core alignment of computer-supported learning environments with the theoretical and operational framework of constructivism.
- 5.
- 6.
All software scaffolds available at the Go-Lab platform can be found at http://www.golabz.eu/apps. For a comprehensive review of guidance provided to students in computer-supported learning environments with virtual and remote laboratories, see Zacharia et al. (2015).
- 7.
The Go-Lab platform offers online an entire array of laboratories for supporting inquiry-based learning, including virtual laboratories and remotely operated educational laboratories (http://www.golabz.eu/labs). In this contribution, we have focused on virtual laboratories.
- 8.
Inquiry Learning Spaces available in the Go-Lab platform can be found at http://www.golabz.eu/spaces
- 9.
Educators can use the Go-Lab authoring tool to select virtual laboratories and software scaffolds/applications and embed them in phases and sub-phases of the inquiry cycle in order to create an Inquiry Learning Space (de Jong et al. 2014).
References
Ariza, Y., Lorenzano, P., & Adúriz-Bravo, A. (2016). Meta-theoretical contributions to the constitution of a model-based didactics of science. Science & Education. https://doi.org/10.1007/s11191-016-9845-3.
Campbell, T., Oh, P. S., & Neilson, D. (2013). Reification of five types of modeling pedagogies with model-based inquiry (MBI) modules for high school science classrooms. In M. S. Khine & I. M. Saleh (Eds.), Approaches and strategies in next generation science learning (pp. 106–126). Hershey: IGI Global.
Campbell, T., Oh, P. S., Maughn, M., Kiriazis, N., & Zuwallack, R. (2015). A review of modeling pedagogies: Pedagogical functions, discursive acts, and technology in modeling instruction. Eurasia Journal of Mathematics, Science & Technology Education, 11, 159–176.
Clarke, T., Ayres, P., & Sweller, J. (2005). The impact of sequencing and prior knowledge on learning mathematics through spreadsheet applications. Educational Technology Research & Development, 53, 15–24.
Clement, J. (2000). Model based learning as a key research area for science education. International Journal of Science Education, 22, 1041–1053.
Cobb, P., Confrey, J., diSessa, A., Lehrer, R., & Schauble, L. (2003). Design experiment in educational research. Educational Researcher, 32, 9–13.
Coll, R. K., & Lajium, D. (2011). Modeling and the future of science learning. In M. S. Khine & I. M. Saleh (Eds.), Models and modeling cognitive tools for scientific enquiry (pp. 3–21). New York: Springer.
De Boer, G. E., Quellmalz, E. S., Davenport, J. L., Timms, M. J., Herrmann-Abell, C. F., Buckley, B. C., Jordan, K. A., Huang, C.-W., & Flanagan, J. C. (2014). Comparing three online testing modalities: Using static, active, and interactive online testing modalities to access middle school students’ understanding of fundamental ideas and use of inquiry skills related to ecosystems. Journal of Research in Science Teaching, 51, 523–554.
de Jong, T. (2015). Go-Lab classroom scenarios handbook. Deliverable D1.4. The Go-Lab Project: Global Online Science Labs for Inquiry Learning at School.
de Jong, T., & van Joolingen, W. R. (2007). Model-facilitated learning. In J. M. Spector, M. D. Merrill, J. J. G. van Merriënboer, & M. P. Driscoll (Eds.), Handbook of research on educational communication and technology (3rd ed., pp. 457–468). New York: Lawrence Erlbaum.
de Jong, T., van Joolingen, W. R., Giemza, A., Girault, I., Hoppe, U., Kindermann, J., Kluge, A., Lazonder, A. W., Vold, V., Weinberger, A., Weinbrenner, S., Wichmann, A., Anjewierden, A., Bodin, M., Bollen, L., D’Ham, C., Dolonen, J., Engler, J., Geraedts, C., Grosskreutz, H., Hovardas, T., Julien, R., Lechner, J., Ludvigsen, S., Matteman, Y., Meistadt, Ø., Næss, B., Ney, M., Pedaste, M., Perritano, A., Rinket, M., von Schlanbusch, H., Sarapuu, T., Schulz, F., Sikken, J., Slotta, J., Toussaint, J., Verkade, A., Wajeman, C., Wasson, B., Zacharia, Z. C., & van der Zanden, M. (2010). Learning by creating and exchanging objects: The SCY experience. British Journal of Educational Technology, 41, 909–921.
de Jong, T., Weinberger, A., van Joolingen, W. R., Ludvigsen, S., Ney, M., Girault, I., Kluge, A., Lazonder, A., Pedaste, M., Wasson, B., Wichmann, A., Geraedts, C., Hovardas, T., Julien, R., Lejeune, A., Manoli, C., Matteman, Y., Sarapuu, T., Vold, V., Wanders, B., & Zacharia, Z. (2012). Using scenarios to design complex technology-enhanced learning environments. Educational Technology Research & Development, 60, 883–901.
de Jong, T., Linn, M. C., & Zacharia, Z. C. (2013). Physical and virtual laboratories in science and engineering education. Science, 340, 305–308.
de Jong, T., Sotiriou, S., & Gillet, D. (2014). Innovations in STEM education: The Go-Lab federation of online labs. Smart Learning Environments, 1, 1–16.
Develaki, M. (2007). The model-based view of scientific theories and the structuring of school science programmes. Science & Education, 16, 725–749.
Duschl, R., Maeng, S., & Sezen, A. (2011). Learning progressions and teaching sequences: A review and analysis. Studies in Science Education, 47, 123–182.
Forbes, C. T., Zangori, L., & Schwarz, C. V. (2015). Empirical validation of integrated learning performances for hydrologic phenomena: 3rd-grade students’ model-driven explanation-construction. Journal of Research in Science Teaching, 52, 895–921.
Giere, R. N. (1991). Understanding scientific reasoning (3rd ed.). New York: Harcourt Brace Jovanovich College Publishers.
Gobert, G. D., & Buckley, B. C. (2000). Introduction to model-based teaching and learning in science education. International Journal of Science Education, 22, 891–894.
Grünkorn, J., Upmeier zu Belzen, A., & Krüger, D. (2014). Assessing students’ understandings of biological models and their use in science to evaluate a theoretical framework. International Journal of Science Education, 36, 1651–1684.
Halloun, I. (2007). Mediated modeling in science education. Science & Education, 16, 653–697.
Hmelo-Silver, C. E., & Azevedo, R. (2006). Understanding complex systems: Some core challenges. The Journal of the Learning Sciences, 15, 53–61.
Hovardas, T. (2016). A learning progression should address regression: Insights from developing non-linear reasoning in ecology. Journal of Research in Science Teaching, 53, 1447–1470.
Hovardas, T., & Korfiatis, K. J. (2011). Towards a critical re-appraisal of ecology education: Scheduling an educational intervention to revisit the ‘Balance of Nature’ metaphor. Science & Education, 20, 1039–1053.
Hovardas, T., Xenofontos, N., & Zacharia, Z. (2017). Using virtual labs in an inquiry context: The effect of a hypothesis formulation tool and an experiment design tool on students’ learning. In Levin, I., & Tsybulsky, D. (Eds.), Digital tools and solutions for inquiry based STEM learning (pp. 58–83). Hershey: IGI Global.
Jacobson, M. J., & Wilensky, U. (2006). Complex systems in education: Scientific and educational importance and implications for the learning sciences. The Journal of the Learning Sciences, 15, 11–34.
Justi, R. S., & Gilbert, J. K. (2003). Teachers’ views on the nature of models. International Journal of Science Education, 25, 1369–1386.
Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19, 509–539.
Kolodner, J. L., Camp, P. J., Crismond, D., Fasse, B., Gray, J., Holbrook, J., Puntambekar, S., & Ryan, N. (2003). Problem-based learning meets case-based reasoning in the middle-school science classroom: Putting learning by design (TM) into practice. The Journal of the Learning Sciences, 12, 495–547.
Lefkaditou, A., Korfiatis, K., & Hovardas, T. (2014). Contextualizing the teaching and learning of ecology: Historical and philosophical considerations. In M. Matthews (Ed.), International handbook of research in history, philosophy and science teaching (pp. 523–550). Dordrecht: Springer.
Lehrer, R., & Schauble, L. (2006). Scientific thinking and scientific literacy: Supporting development in learning in context. In W. Damon, R. M. Lerner, K. A. Renninger, & I. E. Sigel (Eds.), Handbook of child psychology (Vol. 4, 6th ed., pp. 153–196). Hoboken: Wiley.
Lehrer, R., & Schauble, L. (2015). Learning progressions: The whole world is NOT a stage. Science Education, 99, 432–437.
Louca, L., & Zacharia, Z. (2008). The use of computer-based programming environments as computer modeling tools in early science education: The cases of textual and graphical program languages. International Journal of Science Education, 30, 287–323.
Louca, T. L., & Zacharia, C. Z. (2012). Modeling-based learning in science education: A review. Educational Review, 64, 471–492.
Louca, L. T., & Zacharia, Z. C. (2015). Examining learning through modeling in K-6 science education. Journal of Science Education and Technology, 24(2–3), 192–215.
Matthews, M. R. (2005). Idealization and Galileo’s pendulum discoveries: Historical, philosophical and pedagogical considerations. In M. R. Matthews, C. F. Gauld, & A. Stinner (Eds.), The pendulum: Scientific, historical, philosophical & educational perspectives (pp. 209–235). Dordrecht: Springer.
McComas, W. F. (2002). The nature of science in science education. Dordrecht: Kluwer Academic Publishers.
McNeill, K. L., Lizotte, D. J., Krajcik, J., & Marx, R. W. (2006). Supporting students’ construction of scientific explanations by fading scaffolds in instructional materials. The Journal of the Learning Sciences, 15, 153–191.
Mellar, H., & Bliss, J. (1994). Introduction: Modeling and education. In H. Mellar, J. Bliss, R. Boohan, J. Ogborn, & C. Tompsett (Eds.), Learning with artificial worlds: Computer based modeling in the curriculum (pp. 1–7). London: The Falmer Press.
Nersessian, N. (2002). The cognitive basis of model-based reasoning in science. In P. Carruthers, S. Stich, & M. Siegal (Eds.), The cognitive basis of science (pp. 17–34). Cambridge: Cambridge University Press.
Nersessian, N. (2005). Interpreting scientific and engineering practices: Integrating the cognitive, social, and cultural dimensions. In M. Gorman, R. D. Tweney, D. Gooding, & A. Kincannon (Eds.), Scientific and technological thinking. Hillsdale: Lawrence Erlbaum Associates.
Oh, P. S., & Oh, S. J. (2011). What teachers of science need to know about models: An overview. International Journal of Science Education, 33, 1109–1130.
Olympiou, G., Zacharia, Z. C., & de Jong, T. (2013). Making the invisible visible: Enhancing students’ conceptual understanding by introducing representations of abstract objects in a simulation. Instructional Science, 41, 575–587.
Pea, R. D. (2004). The social and technological dimensions of scaffolding and related theoretical concepts for learning, education, and human activity. The Journal of the Learning Sciences, 13, 423–451.
Pedaste, M., & Sarapuu, T. (2006a). Developing an effective support system for inquiry learning in a Web-based environment. Journal of Computer Assisted Learning, 22, 47–62.
Pedaste, M., & Sarapuu, T. (2006b). The factors influencing the outcome of solving story-problems in a Web-based learning environment. Interactive Learning Environments, 14, 153–176.
Pedaste, M., Mäeots, M., Siiman, L. A., de Jong, T., van Riesen, S. A., 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.
Petridou, E., Psillos, D., Hatzikraniotis, E., & Kallery, M. (2013). A study on the exploratory use of microscopic models as investigative tools: The case of electrostatic polarization. In G. Tsaparlis & H. Sevian (Eds.), Concepts of matter in science education (pp. 199–212). Dordrecht: Springer.
Pollock, E., Chandler, P., & Sweller, J. (2002). Assimilating complex information. Learning and Instruction, 12, 61–86.
Reiser, B. J. (2004). Scaffolding complex learning: The mechanisms of structuring and problematizing student work. The Journal of the Learning Sciences, 13, 273–304.
Schwarz, C. V., & White, B. Y. (2005). Metamodeling knowledge: Developing students’ understanding of scientific modeling. Cognition and Instruction, 23, 165–205.
Schwarz, C. V., Reiser, B. J., Davis, E. A., Kenyon, L., Archer, A., Fortus, D., & 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, 632–654.
Shea, N. A., & Duncan, R. G. (2013). From theory to data: The process of refining learning progressions. The Journal of the Learning Sciences, 22, 7–32.
Soulios, I., & Psillos, D. (2016). Enhancing student teachers’ epistemological beliefs about models and conceptual understanding through a model-based inquiry process. International Journal of Science Education, 38, 1212–1233.
Sweller, J., van Merriënboer, J. J. G., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10, 251–296.
Tasiopoulou, E., & de Jong, T. (2016). Integrated validation and evaluation report and recommendations. Deliverable D8.4 (Complete version). The Go-Lab Project: Global Online Science Labs for Inquiry Learning at School.
van Joolingen, W. R., & Zacharia, Z. C. (2009). Developments in inquiry learning. In N. Balacheff, S. Ludvigsen, T. de Jong, & S. Barnes (Eds.), Technology-enhanced learning (pp. 21–37). Dordrecht: Springer.
van Joolingen, W. R., de Jong, T., Lazonder, A. W., Savelsberg, 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, 671–688.
van Joolingen, W. R., Giemza, A., Bollen, L., Bodin, M., Manske, S., Engler, J., & Halik, K. (2011). SCY cognitive scaffolds and tools (DII.2). Twente: SCY Consortium.
Verhoeff, R. P., Waarlo, A. J., & Boersma, K. T. (2008). Systems modelling and the development of coherent understanding of cell biology. International Journal of Science Education, 30, 543–568.
Weinberger, A., Dolonen, J., Hovardas, A., Pedaste, M., Kluge, A., Ludvigsen, S., et al. (2009). SCY scenario handbook and pedagogical plans, version 1. Enschede: University of Twente.
Windschitl, M., Thompson, J., & Braaten, M. (2008a). Beyond the scientific method: Model-based inquiry as a new paradigm of preference for school science investigations. Science Education, 92, 941–967.
Windschitl, M., Thompson, J., & Braaten, M. (2008b). How novice science teachers appropriate epistemic discourses around model-based inquiry for use in classrooms. Cognition and Instruction, 26, 310–378.
Zacharia, Z. C., & de Jong, T. (2014). The effects on students’ conceptual understanding of electric circuits of introducing virtual manipulatives within a physical manipulatives-oriented curriculum. Cognition and Instruction, 32(2), 101–158.
Zacharia, Z. C., Manoli, C., Xenofontos, N., de Jong, T., Pedaste, M., van Riesen, S. A. N., Kamp, E. T., Mäeots, M., Siiman, L., & Tsourlidaki, E. (2015). Identifying potential types of guidance for supporting student inquiry when using virtual and remote labs in science: A literature review. Educational Technology Research and Development, 63, 257–302.
Zangori, L., & Forbes, C. T. (2015). Exploring third-grade student model-based explanations about plant relationships within an ecosystem. International Journal of Science Education, 37, 2942–2964.
Zangori, L., Forbes, C. T., & Schwarz, C. V. (2015). Exploring the effect of embedded scaffolding within curricular tasks on third-grade students’ model-based explanations about hydrologic cycling. Science & Education, 24, 957–981.
Acknowledgments
This book chapter reports on work undertaken within the frame of the Go-Lab project (Project title: Global Online Science Labs for Inquiry Learning at School; Call identifier: FP7-ICT-2011-8; Project number: 317601) funded by the European Union. The present document does not represent the opinion of the European Union, and the European Union should not be held responsible for any use that might be made of its content.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Hovardas, T., Pedaste, M., Zacharia, Z., de Jong, T. (2018). Model-Based Inquiry in Computer-Supported Learning Environments: The Case of Go-Lab. In: Auer, M., Azad, A., Edwards, A., de Jong, T. (eds) Cyber-Physical Laboratories in Engineering and Science Education. Springer, Cham. https://doi.org/10.1007/978-3-319-76935-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-76935-6_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-76934-9
Online ISBN: 978-3-319-76935-6
eBook Packages: EducationEducation (R0)