Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-745bb68f8f-grxwn Total loading time: 0 Render date: 2025-01-27T07:55:50.977Z Has data issue: false hasContentIssue false

33 - The Guided Inquiry Principle in Multimedia Learning

from Part VII - Principles Based on Generative Activity in Multimedia Learning

Published online by Cambridge University Press:  19 November 2021

By
Ton de Jong
Edited by
Richard E. Mayer  and
Logan Fiorella
Richard E. Mayer
Affiliation:
University of California, Santa Barbara
Logan Fiorella
Affiliation:
University of Georgia
Get access

Summary

Inquiry learning puts students in an active and engaged learning mode. In inquiry learning, students try to find answers to research questions by performing investigations (in the case of science learning the investigations most often involve experiments). To be successful in inquiry learning, students need adequate initial knowledge of the domain involved and they need to be supported in their inquiry processes. Experimental work and data from PISA have indicated that guided inquiry can be an effective teaching strategy. This chapter presents an overview of the types of guidance typically found in online inquiry learning environments. Specifically, this chapter suggests the need for effective combinations of online and offline activities, such as combinations of hands-on and virtual laboratory experiences, and the use of teacher dashboards that inform teachers about the status of their students’ inquiry process, so that appropriate guidance can be provided.

Keywords

inquiry learningguided inquiry principleactive learningengaged learningonline laboratories
Type
Chapter
Information
The Cambridge Handbook of Multimedia Learning , pp. 394 - 402
Publisher: Cambridge University Press
Print publication year: 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aditomo, A., & Klieme, E. (2020). Forms of inquiry-based science instruction and their relations with learning outcomes: Evidence from high and low-performing education systems. International Journal of Science Education, 42, 504–525.Google Scholar
Aditomo, A., & Köhler, C. (2020). Do student ratings provide reliable and valid information about teaching quality at the school level? Evaluating measures of science teaching in PISA 2015. Educational Assessment, Evaluation and Accountability, 32, 275–310.CrossRefGoogle Scholar
Ainsworth, S. (2014). The multiple representation principle in multimedia learning. In Mayer, R. E. (ed.), The Cambridge Handbook of Multimedia Learning (2nd ed., pp. 464–486). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Bell, R. L., Smetana, L., & Binns, I. (2005). Simplifying inquiry instruction. The Science Teacher, 72, 30–33.Google Scholar
Belland, B. R., Walker, A. E., & Kim, N. J. (2017). A Bayesian network meta-analysis to synthesize the influence of contexts of scaffolding use on cognitive outcomes in STEM education. Review of Educational Research, 87, 1042–1081.Google Scholar
Belland, B. R., Walker, A. E., Kim, N. J., & Lefler, M. (2017). Synthesizing results from empirical research on computer-based scaffolding in STEM education. Review of Educational Research, 87, 309–344.CrossRefGoogle ScholarPubMed
Brand, C., Massey-Allard, J., Perez, S., Rummel, N., & Roll, I. (2019). What inquiry with virtual labs can learn from productive failure: A theory-driven study of students’ reflections. In Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., & Luckin, R. (eds.), Artificial intelligence in Education. AIED 2019. Lecture Notes in Computer Science (Vol. 11626, pp. 30–35). Cham: Springer.Google Scholar
Bruner, J. S. (1961). The act of discovery. Harvard Educational Review, 31, 21–32.Google Scholar
Cannady, M. A., Vincent-Ruz, P., Chung, J. M., & Schunn, C. D. (2019). Scientific sensemaking supports science content learning across disciplines and instructional contexts. Contemporary Educational Psychology, 59, 101802.Google Scholar
Chamberlain, J. M., Lancaster, K., Parson, R., & Perkins, K. K. (2014). How guidance affects student engagement with an interactive simulation. Chemistry Education Research and Practice, 15, 628–638.Google Scholar
Chan, J. W. W., & Pow, J. W. C. (2020). The role of social annotation in facilitating collaborative inquiry-based learning. Computers & Education, 147, 103787.Google Scholar
Chen, L., Dorn, E., Krawitz, M., Lim, C., & Mourshed, M. (2017). Drivers of Student Performance Insights from Asia. McKinsey and Company.Google Scholar
Chernikova, O., Heitzmann, N., Fink, M. C., Timothy, V., Seidel, T., Fischer, F., & DFG Research group COSIMA. (2020). Facilitating diagnostic competences in higher Education – a meta-analysis in medical and teacher Education. Educational Psychology Review, 32, 157–196.CrossRefGoogle Scholar
Chi, M. T. H., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49, 219–243.Google Scholar
Chinn, C. A., & Brewer, W. F. (1993). The role of anomalous data in knowledge acquisition: A theoretical framework and implications for science instruction. Review of Educational Research, 63, 1–51.CrossRefGoogle Scholar
Clark, R. C., & Mayer, R. E. (2016). E-learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning. Hoboken, NJ: John Wiley & Sons.CrossRefGoogle Scholar
d’Angelo, C., Rutstein, D., Harris, C., Bernard, R., Borokhovski, E., & Haertel, G. (2014). Simulations for STEM Learning: Systematic Review and Meta-analysis. Menlo Park, CA: SRI International.Google Scholar
de Jong, T. (2006). Scaffolds for scientific discovery learning. In Elen, J., & Clark, R. E. (eds.), Dealing with Complexity in Learning Environments (pp. 107–128). Amsterdam: Elsevier Science Publishers.Google Scholar
de Jong, T. (2019). Moving towards engaged learning in STEM domains; there is no simple answer, but clearly a road ahead. Journal of Computer Assisted Learning, 35, 153–167.CrossRefGoogle Scholar
de Jong, T., Gillet, D., Rodríguez-Triana, M. J., Hovardas, T., Dikke, D., Doran, R., Dziabenko, O., Koslowsky, J., Korventausta, M., Law, E., Pedaste, M., Tasiopoulou, E., Vidal, G., & Zacharia, Z. C. (2021). Understanding teacher design practices for digital inquiry-based science learning: The case of Go-Lab. Educational Technology Research & Development, 69, 417–444.CrossRefGoogle ScholarPubMed
de Jong, T., & Lazonder, A. W. (2014). The guided discovery principle in multimedia learning. In Mayer, R. E. (ed.), The Cambridge Handbook of Multimedia Learning (2nd ed., pp. 371–390). Cambridge: Cambridge University Press.Google Scholar
de Jong, T., Lazonder, A. W., Pedaste, M., & Zacharia, Z. C. (2018). Simulations, games and modelling tools for learning. In Fischer, F., Hmelo-Silver, C. E., Goldman, S. R., & Reimann, P. (eds.), International Handbook of the Learning Sciences (pp. 241–268). Abingdon: Routledge.Google Scholar
Dickler, R. (2019). An intelligent tutoring system and teacher dashboard to support mathematizing during science inquiry. In Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., & Luckin, R. (eds.), Lecture Notes in Computer Science (Vol. 11626, pp. 332–338). New York: Springer International Publishing.Google Scholar
Dickler, R., Gobert, J. D., & Yasar, O. (2018). Exploring the use of eye-tracking as a method to capture student knowledge acquisition in a virtual science inquiry investigation. Hypothesis, 16, 4–5.Google Scholar
Eshuis, E. H., ter Vrugte, J., Anjewierden, A., Bollen, L., Sikken, J., & de Jong, T. (2019). Improving the quality of vocational students’ collaboration and knowledge acquisition through instruction and joint reflection. International Journal of Computer-Supported Collaborative Learning, 14, 53–76.CrossRefGoogle Scholar
Fang, S.-C., Hsu, Y.-S., & Hsu, W. H. (2016). Effects of explicit and implicit prompts on students’ inquiry practices in computer-supported learning environments in high school earth science. International Journal of Science Education, 38, 1699–1726.Google Scholar
Gobert, J. D., Moussavi, R., Li, H., Sao Pedro, M. A., & Dickler, R. (2018). Real time scaffolding of students’ online data interpretation during inquiry with Inq-ITS using educational datamining. In Azad, A. K. M., Auer, M., Edwards, A., & de Jong, T. (eds.), Cyber-Physical Laboratories in Engineering and Science Education (pp. 191–219). Berlin: Springer.Google Scholar
Hattie, J. A. C., & Donoghue, G. M. (2016). Learning strategies: A synthesis and conceptual model [Review Article]. npj Science of Learning, 1, 16013.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, 1–13.Google Scholar
Kapici, H. O., Akcay, H., & de Jong, T. (2019). Using hands-on and virtual laboratories alone or together – Which works better for acquiring knowledge and skills? Journal of Science Education and Technology, 28, 231–250.CrossRefGoogle Scholar
Kapur, M., & Bielaczyc, K. (2012). Designing for productive failure. Journal of the Learning Sciences, 21, 45–83.Google Scholar
Kroeze, K., van den Berg, S., Veldkamp, B., Lazonder, A. W., & de Jong, T. (2019). Automated feedback can improve hypothesis quality. Frontiers in Education, 3, 116.CrossRefGoogle Scholar
Kuang, X., Eysink, T. H. S., & de Jong, T. (2020). Effects of providing partial hypotheses as a support for simulation-based inquiry learning. Journal of Computer Assisted Learning, 36, 487–501.Google Scholar
Lau, K., & Lam, T. Y. (2017). Instructional practices and science performance of 10 top-performing regions in PISA 2015. International Journal of Science Education, 39, 2128–2149.Google Scholar
Lazonder, A. W., & Harmsen, R. (2016). Meta-analysis of inquiry-based learning: Effects of guidance. Review of Educational Research, 86, 681–718.Google Scholar
Li, H., Gobert, J. D., & Dickler, R. (2019). Scaffolding during science inquiry. In Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale (pp. 1–10). ACM.Google Scholar
Mavrikis, M., Geraniou, E., Gutierrez Santos, S., & Poulovassilis, A. (in press). Intelligent analysis and data visualisation for teacher assistance tools: The case of exploratory learning. British Journal of Educational Technology, 50, 2920–2942.Google Scholar
Mayer, R. E. (2004). Should there be a three-strikes rule against pure discovery learning? American Psychologist, 59, 14–19.Google Scholar
National Academies of Sciences Engineering Medicine. (2019). Science and Engineering for Grades 6–12: Investigation and Design at the Center. Washington, DC: National Academies Press.Google Scholar
National Research Council. (2012). Education for Life and Work: Developing Transferable Knowledge and Skills in the 21st Century. Washington, DC: National Academies Press.Google Scholar
OECD. (2016). PISA 2015 Results (Volume II): Policies and Practices for Successful Schools. Paris: PISA, OECD Publishing.Google Scholar
Oliver, M., McConney, A., & Woods-McConney, A. (in press). The efficacy of inquiry-based instruction in science: A comparative analysis of six countries using PISA 2015. Research in Science Education. https://doi.org/10.1007/s11165–019-09901-0CrossRefGoogle Scholar
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 inquiry cycle. Educational Research Review, 14, 47–61.Google Scholar
Plass, J. L., & Pawar, S. (2020). Toward a taxonomy of adaptivity for learning. Journal of Research on Technology in Education, 52, 275–300.Google Scholar
Rau, M. A. (2020). Comparing multiple theories about learning with physical and virtual representations: Conflicting or complementary effects? Educational Psychology Review, 32, 297–325.Google Scholar
Rinehart, R., Duncan, R., Chinn, C., Atkins, T., & DiBenedetti, J. (2016). Critical design decisions for successful model-based inquiry in Science classrooms. International Journal of Designs for Learning, 7, 17–40.Google Scholar
Roll, I., Butler, D., Yee, N., Welsh, A., Perez, S., Briseno, A., Perkins, K., & Bonn, D. (2018). Understanding the impact of guiding inquiry: The relationship between directive support, student attributes, and transfer of knowledge, attitudes, and behaviours in inquiry learning. Instructional Science, 46, 77–104.CrossRefGoogle 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, 136–153.CrossRefGoogle Scholar
Schwaighofer, M., Bühner, M., & Fischer, F. (2017). Executive functions in the context of complex learning: Malleable moderators? Frontline Learning Research, 5, 58–75.Google Scholar
Sergis, S., Sampson, D. G., Rodríguez-Triana, M. J., Gillet, D., Pelliccione, L., & de Jong, T. (2019). Using educational data from teaching and learning to inform teachers’ reflective educational design in inquiry-based STEM education. Computers in Human Behavior, 92, 724–738.Google Scholar
Smetana, L. K., & Bell, R. L. (2012). Computer simulations to support science instruction and learning: A critical review of the literature. International Journal of Science Education, 34, 1337–1370.Google Scholar
Teig, N., Scherer, R., & Nilsen, T. (2018). More isn’t always better: The curvilinear relationship between inquiry-based teaching and student achievement in science. Learning and Instruction, 56, 20–29.CrossRefGoogle Scholar
van der Graaf, J., Segers, E., & de Jong, T. (2020). Fostering integration of informational texts and virtual labs during inquiry-based learning. Contemporary Educational Psychology, 62, 101890.Google Scholar
van der Meij, J., & de Jong, T. (2006). Supporting students’ learning with multiple representations in a dynamic simulation-based learning environment. Learning and Instruction, 16, 199–212.Google Scholar
van Riesen, S. A. N., Gijlers, H., Anjewierden, A., & de Jong, T. (2018). The influence of prior knowledge on the effectiveness of guided experiment design. International Journal of Science Education, 11, 1327–1344.Google Scholar
Veermans, K. H., & Jaakkola, T. (2016). Reflections from research: Some considerations for the design of educational simulations (and games). In Proceedings of the 3rd Asia–Europe symposium on simulation & serious gaming (pp. 173–176). New York: Association for Computing Machinery.Google Scholar
Verbert, K., Govaerts, S., Duval, E., Santos, J. L., Van Assche, F., Parra, G., & Klerkx, J. (2014). Learning dashboards: An overview and future research opportunities. Personal and Ubiquitous Computing, 18, 1499–1514.Google Scholar
Vuorikari, R., Punie, Y., Gomez, S. C., & van Den Brande, G. (2016). Digcomp 2.0: The Digital Competence Framework for Citizens. Update Phase 1: The Conceptual Reference Model. Luxembourg: Publications Office of the European Union.Google Scholar
Wecker, C., Rachel, A., Heran-Dörr, E., Waltner, C., Wiesner, H., & Fischer, F. (2013). Presenting theoretical ideas prior to inquiry activities fosters theory-level knowledge. Journal of Research in Science Teaching, 50, 1180–1206.Google Scholar
Wen, C., Liu, C., Chang, H., Chang, C., Chang, M., Fan Chiang, S., Yang, C., & Hwang, F. (2020). Students’ guided inquiry with simulation and its relation to school science achievement and scientific literacy. Computers & Education, 149, 103830.Google Scholar
Yannier, N., Hudson, S. E., & Koedinger, K. R. (2020). Active learning is about more than hands-on: A mixed-reality AI system to support STEM Education. International Journal of Artificial Intelligence in Education, 30, 74–96.Google Scholar
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, 101–158.Google Scholar
Zacharia, Z. C., Manoli, C., Xenofontos, N., de Jong, T., Pedaste, M., van Riesen, S. A. N., Kamp, E., Mäeots, M., Siiman, L. A., & Tsourlidaki, E. (2015). Identifying potential types of guidance for supporting student inquiry in using virtual and remote labs: A literature review. Educational Technology Research & Development, 63, 257–302.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×