Skip to main content

Abstract

The basic tenet of inquiry learning is that students arrive at an understanding of the subject matter by engaging in self-directed investigations. The foundations of this mode of learning are derived from three related fields of study. Psychological research on scientific reasoning revolves around the cognitive processes involved in inducing knowledge from empirical data, and intends to give an account of the problems students encounter in performing these processes. These learning difficulties (should) serve as a starting point for educational research into the effectiveness of support or scaffolding that can be used to overcome known skill deficiencies. Research and development of software tools and environments addresses the ways in which this support can best be offered to the learner so as to enhance learning processes and outcomes. This chapter outlines recent trends and issues in these three research areas, and attempts to synthesize key findings in order to identify the latest advancements in inquiry-based learning.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

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

    Google Scholar 

  • Amsel, E., & Brock, S. (1996). The development of evidence evaluation skills. Cognitive Development, 11, 523–550. doi:10.1016/S0885-2014(96)90016-7.

    Article  Google Scholar 

  • Bernacki, M. L., Aguilar, A. C., & Byrnes, J. P. (2011). Self-regulated learning and technology-enhanced learning environments: An opportunity-propensity analysis. In D. Persico & G. Dettori (Eds.), Fostering self-regulated learning through ICT (pp. 1–26). Hershey, PA: Information Science Publishing.

    Google Scholar 

  • Burns, B. D., & Vollmeyer, R. (2002). Goal specificity effects on hypothesis testing in problem solving. The Quarterly Journal of Experimental Psychology, 55A, 241–261. doi:10.1080/02724980143000262.

    Google Scholar 

  • Chang, K. E., Chen, Y. L., Lin, H. Y., & Sung, Y. T. (2008). Effects of learning support in simulation-based physics learning. Computers in Education, 51, 1486–1498. doi:10.1016/j.compedu.2008.01.007.

    Article  Google Scholar 

  • Chi, M. T. H., Feltovich, P., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152. doi:10.1207/s15516709cog0502_2.

    Article  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, 1–49. doi:10.3102/00346543063001001.

    Article  Google Scholar 

  • Chinn, C. A., & Brewer, W. F. (1998). An empirical test of a taxonomy of responses to anomalous data in science. Journal of Research in Science Teaching, 35, 623–654. doi:10.1002/(SICI)1098-2736(199808)35:6<623::AID-TEA3>3.0.CO;2-O.

    Article  Google Scholar 

  • Chinn, C. A., & Malhotra, B. A. (2002a). Children’s responses to anomalous scientific data: How is conceptual change impeded? Journal of Educational Psychology, 94, 327–343. doi:10.1037/0022-0663.94.2.327.

    Article  Google Scholar 

  • Chinn, C. A., & Malhotra, B. A. (2002b). Epistemologically authentic inquiry in schools: A theoretical framework for evaluating inquiry tasks. Science Education, 86, 175–218. doi:10.1002/sce.10001.

    Article  Google Scholar 

  • De Jong, T. (2006a). Computer simulations: Technological advances in inquiry learning. Science, 312, 532–533. doi:10.1126/science.1127750.

    Article  Google Scholar 

  • De Jong, T. (2006b). Scaffolds for scientific discovery learning. In J. Elen & R. E. Clark (Eds.), Handling complexity in learning environments: Theory and research (pp. 107–128). London: Elsevier.

    Google Scholar 

  • De Jong, T. (2010). Instruction based on computer simulations. In R. E. Mayer & P. A. Alexander (Eds.), Handbook of research on learning and instruction (pp. 446–466). London: Routledge.

    Google Scholar 

  • De Jong, T., & Van Joolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68, 179–202. doi:10.3102/00346543068002179.

    Article  Google Scholar 

  • De Jong, T., Van Joolingen, W. R., Giezma, A., Girault, I., Hoppe, U., Kindermann, J., et al. (2010). Learning by creating and exchanging objects: The SCY experience. British Journal of Educational Technology, 41, 909–921. doi:10.1111/j.1467-8535.2010.01121.x.

    Article  Google Scholar 

  • Dean, D., & Kuhn, D. (2007). Direct instruction vs. discovery: The long view. Science Education, 91, 384–397. doi:10.1002/sce.20194.

    Article  Google Scholar 

  • Dunbar, K. (1993). Concept discovery in a scientific domain. Cognitive Science, 17, 397–434. doi:10.1207/s15516709cog1703_3.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Eysink, T. H. S., De Jong, T., Berthold, K., Kolloffel, B., Opfermann, M., & Wouters, P. (2009). Learner performance in multimedia learning arrangements: An analysis across instructional approaches. American Educational Research Journal, 46, 1107–1149. doi:10.3102/0002831209340235.

    Article  Google Scholar 

  • Fund, Z. (2007). The effects of scaffolded computerized science problem-solving on achievement outcomes: A comparative study of support programs. Journal of Computer Assisted Learning, 23, 410–424. doi:10.1111/j.1365-2729.2007.00226.x.

    Article  Google Scholar 

  • Gijlers, H., & De Jong, T. (2005). The relation between prior knowledge and students’ collaborative discovery learning processes. Journal of Research in Science Teaching, 42, 264–282. doi:10.1002/tea.20056.

    Article  Google Scholar 

  • Gijlers, H., & De Jong, T. (2009). Sharing and confronting propositions in collaborative scientific discovery learning. Cognition and Instruction, 27, 239–268. doi:10.1080/07370000903014352.

    Article  Google Scholar 

  • Hmelo-Silver, C. E., Golan Dunca, R., & Chinn, C. A. (2007). Scaffolding and achievement in problem-based and inquiry learning: A response to Kirschner, Sweller, and Clark (2006). Educational Psychologist, 42, 99–107. doi:10.1080/00461520701263368.

    Article  Google Scholar 

  • Horwitz, P., Neumann, E., & Schwartz, J. (1996). Teaching science at multiple levels: The GenScope program. Communications of the ACM, 39, 127–131.

    Article  Google Scholar 

  • Horwitz, P., O’Dwyer, L. M., & Rosca, C. V. (2010, April). Teaching and assessing “Evolution Readiness” to fourth graders using games. Paper presented at the annual meeting of the American Educational Research Association, Denver, CO.

    Google Scholar 

  • Hulshof, C. D., & De Jong, T. (2006). Using just-in-time information to support discovery learning about geometrical optics in a computer-based simulation. Interactive Learning Environments, 14, 79–94. doi:10.1080/10494820600769171.

    Article  Google Scholar 

  • Inhelder, B., & Piaget, J. (1958). The growth of logical thinking from childhood to adolescence. New York, NY: Basic Books.

    Book  Google Scholar 

  • Kali, Y., & Linn, M. C. (2008). Technology-enhanced support strategies for inquiry learning. In J. M. Spector, M. D. Merrill, J. J. G. Van Merrienboer, & M. Driscoll (Eds.), Handbook of research on educational communications and technology (3rd ed., pp. 145–161). New York, NY: Lawrence Erlbaum.

    Google Scholar 

  • Keselman, A. (2003). Supporting inquiry learning by promoting normative understanding of multivariable causality. Journal of Research in Science Teaching, 40, 898–921. doi:10.1002/tea.10115.

    Article  Google Scholar 

  • Kirschner, P. A., Sweller, J., & Clark, R. E. (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, 75–86. doi:10.1207/s15326985ep4102_1.

    Article  Google Scholar 

  • Klahr, D. (2000). Exploring science: The cognition and development of discovery processes. Cambridge, MA: MIT Press.

    Google Scholar 

  • Klahr, D. (2005). Early science instruction: Addressing fundamental issues. Psychological Science, 16, 871–872. doi:10.1111/j.1467-9280.2005.01629.x.

    Article  Google Scholar 

  • Klahr, D., & Dunbar, K. (1988). Dual space search during scientific reasoning. Cognitive Science, 12, 1–48. doi:10.1207/s15516709cog1201_1.

    Article  Google Scholar 

  • Klahr, D., Fay, A. L., & Dunbar, K. (1993). Heuristics for scientific experimentation: A developmental study. Cognitive Psychology, 25, 111–146. doi:10.1006/cogp.1993.1003.

    Article  Google Scholar 

  • Klahr, D., & Li, J. (2005). Cognitive research and elementary science instruction: From the laboratory, to the classroom, and back. Journal of Science Education and Technology, 14, 217–238. doi:10.1007/s10956-005-4423-5.

    Article  Google Scholar 

  • Klahr, D., & Nigam, M. (2004). The equivalence of learning paths in early science instruction: Effects of direct instruction and discovery learning. Psychological Science, 15, 661–667. doi:10.1111/j.0956-7976.2004.00737.x.

    Article  Google Scholar 

  • Koslowski, B., Marasia, J., Chelenza, M., & Dublin, R. (2008). Information becomes evidence when an explanation can incorporate it into a causal framework. Cognitive Development, 23, 472–487. doi:10.1016/j.cogdev.2008.09.007.

    Article  Google Scholar 

  • Krajcik, J., Blumenfeld, P. C., Marx, R. W., Bass, K. M., Fredricks, J., & Soloway, E. (1998). Inquiry in project-based science classrooms: Initial attempts by middle school students. The Journal of the Learning Sciences, 7, 313–350. doi:10.1207/s15327809jls0703%26;4_3.

    Google Scholar 

  • Kuhn, D., Amsel, E., & O’Loughlin, M. (1988). The development of scientific thinking skills. Orlando, FL: Academic.

    Google Scholar 

  • Kuhn, D., Black, J., Keselman, A., & Kaplan, D. (2000). The development of cognitive skills to support inquiry learning. Cognition and Instruction, 18, 495–523. doi:10.1207/S1532690XCI1804_3.

    Article  Google Scholar 

  • Kuhn, D., & Dean, D. (2005). Is developing scientific thinking all about learning to control variables? Psychological Science, 16, 866–870. doi:10.1111/j.1467-9280.2005.01628.x.

    Article  Google Scholar 

  • Kuhn, D., Iordanou, K., Pease, M., & Wirkala, C. (2008). Beyond control of variables: What needs to develop to achieve skilled scientific thinking? Cognitive Development, 23, 435–451. doi:10.1016/j.cogdev.2008.09.006.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Lazonder, A. W., Hagemans, M. G., & De Jong, T. (2010). Offering and discovering domain information in simulation-based inquiry learning. Learning and Instruction, 20, 511–520. doi:10.1016/j.learninstruc.2009.08.001.

    Article  Google Scholar 

  • Lazonder, A. W., Wilhelm, P., & Hagemans, M. G. (2008). The influence of domain knowledge on strategy use during simulation-based inquiry learning. Learning and Instruction, 18, 580–592. doi:10.1016/j.learninstruc.2007.12.001.

    Article  Google Scholar 

  • Lazonder, A. W., Wilhelm, P., & Van Lieburg, E. (2009). Unraveling the influence of domain knowledge during simulation-based inquiry learning. Instructional Science, 37, 437–451. doi:10.1007/s11251-008-9055-8.

    Article  Google Scholar 

  • Lin, J. (2007). Responses to anomalous data obtained from repeatable experiments in the laboratory. Journal of Research in Science Teaching, 44, 506–528. doi:10.1002/tea.20125.

    Article  Google Scholar 

  • Linn, M. C. (1995). Designing computer learning environments for engineering and computer science: The scaffolded knowledge integration framework. Journal of Science Education and Technology, 4, 103–126. doi:10.1007/BF02214052.

    Article  Google Scholar 

  • Lorch, R. F., Lorch, E. P., Calderhead, W. J., Dunlap, E. E., Hodell, E. C., & Freer, B. D. (2010). Learning the control of variables strategy in higher and lower achieving classrooms: Contributions of explicit instruction and experimentation. Journal of Educational Psychology, 102, 90–101. doi:10.1037/a0017972.

    Article  Google Scholar 

  • Manlove, S., Lazonder, A. W., & De Jong, T. (2009). Trends and issues of regulative support use during inquiry learning: Patterns from three studies. Computers in Human Behavior, 25, 795–803. doi:10.1016/j.chb.2008.07.010.

    Article  Google Scholar 

  • Masnick, A. M., & Morris, B. J. (2008). Investigating the development of data evaluation: The role of data characteristics. Child Development, 79, 1032–1048. doi:10.1111/j.1467-8624.2008.01174.x.

    Article  Google Scholar 

  • Minner, D. D., Levy, A. J., & Century, J. (2010). Inquiry-based science instruction—What is it and does it matter? Results from a research synthesis years 1984 to 2002. Journal of Research in Science Teaching, 47, 474–496. doi:10.1002/tea.20347.

    Article  Google Scholar 

  • Mulder, Y. G., Lazonder, A. W., & De Jong, T. (2010). Finding out how they find it out: An empirical analysis of inquiry learners’ need for support. International Journal of Science Education, 32, 2033–2053. doi:10.1080/09500690903289993.

    Article  Google Scholar 

  • Njoo, M., & De Jong, T. (1991). Learning processes of students working with a computer simulation on mechanical engineering. In M. Carretero, M. Pope, R.-J. Simons, & J. I. Pozo (Eds.), Learning and instruction: European research in an international context (Vol. 3, pp. 483–495). Oxford: Pergamon.

    Google Scholar 

  • Njoo, M., & De Jong, T. (1993a). Exploratory learning with a computer simulation for control theory: Learning processes and instructional support. Journal of Research in Science Teaching, 30, 821–844. doi:10.1002/tea.3660300803.

    Article  Google Scholar 

  • Njoo, M., & De Jong, T. (1993b). Supporting exploratory learning by offering structured overviews of hypotheses. In D. Towne, T. De Jong, & H. Spada (Eds.), Simulation-based experiential learning (pp. 207–225). Berlin: Springer.

    Chapter  Google Scholar 

  • Penner, D. E., & Klahr, D. (1996). The interaction of domain-specific knowledge and domain-general discovery strategies: A study with sinking objects. Child Development, 67, 2709–2727. doi:10.1111/1467-8624.ep9706244829.

    Article  Google Scholar 

  • Quinn, J., & Alessi, S. (1994). The effects of simulation complexity and hypothesis-generation strategy on learning. Journal of Research on Computing in Education, 27, 75–91.

    Google Scholar 

  • Quintana, C., Reiser, B. J., Davis, E. A., Krajcik, J., Fretz, E., Duncan, R. G., et al. (2004). A scaffolding design framework for software to support science inquiry. The Journal of the Learning Sciences, 13, 337–386. doi:10.1207/s15327809jls1303_4.

    Article  Google Scholar 

  • Reid, D. J., Zhang, J., & Chen, Q. (2003). Supporting scientific discovery learning in a simulation environment. Journal of Computer Assisted Learning, 19, 9–20. doi:10.1111/j.1365-2729.2006.00162.x.

    Article  Google Scholar 

  • Reiser, B. J. (2004). Scaffolding complex learning: The mechanisms of structuring and problematizing student work. The Journal of the Learning Sciences, 13, 273–304. doi:10.1207/s15327809jls1303_2.

    Article  Google Scholar 

  • Reiser, B. J., Tabak, I., Sandoval, W. A., Smith, B., Steinmuller, F., & Leone, T. J. (2001). BGuILE: Strategic and conceptual scaffolds for scientific inquiry in biology classrooms. In S. M. Carver & D. Klahr (Eds.), Cognition and instruction: Twenty five years of progress (pp. 263–305). Mahwah, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Rieber, L. P., Tzeng, S., & Tribble, K. (2004). Discovery learning, representation, and explanation within a computer-based simulation: Finding the right mix. Learning and Instruction, 14, 307–323. doi:10.1016/j.learninstruc.2004.06.008.

    Article  Google Scholar 

  • Ross, J. A. (1988). Controlling variables: A meta-analysis of studies. Review of Educational Research, 58, 405–437. doi:10.3102/0034654305800440.

    Article  Google Scholar 

  • Schauble, L., Glaser, R., Duschl, R. A., Schulze, S., & John, J. (1995). Students’ understanding of the objectives and procedures of experimentation in the science classroom. The Journal of the Learning Sciences, 4, 131–166. doi:10.1207/s15327809jls0402_1.

    Article  Google Scholar 

  • Schunn, C. D., & Klahr, D. (1993). Self vs. other-generated hypotheses in scientific discovery. In Proceedings of the Fifteenth Annual Conference of the Cognitive Science Society (pp. 900–905). Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Strand-Cary, M., & Klahr, D. (2008). Developing elementary science skills: Instructional effectiveness and path independence. Cognitive Development, 23, 488–511. doi:10.1016/j.cogdev.2008.09.005.

    Article  Google Scholar 

  • Swaak, J., Van Joolingen, W. R., & De Jong, T. (1998). Supporting simulation-based learning; the effects of model progression and assignments on definitional and intuitive knowledge. Learning and Instruction, 8, 235–252. doi:10.1037/a0021017.

    Article  Google Scholar 

  • Tomkins, S. P., & Tunnicliffe, S. D. (2001). Looking for ideas: Observation, interpretation and hypothesis-making by 12-year-old pupils undertaking science investigations. International Journal of Science Education, 23, 791–813. doi:10.1080/09500690119322.

    Google Scholar 

  • Tschirgi, J. E. (1980). Sensible reasoning: A hypothesis about hypotheses. Child Development, 51, 1–10. doi:10.1111/1467-8624.ep12325377.

    Article  Google Scholar 

  • Van Joolingen, W. R., & De Jong, T. (1993). Exploring a domain with a computer simulation: Traversing variable and relation space with the help of a hypothesis scratchpad. In D. Towne, T. De Jong, & H. Spada (Eds.), Simulation-based experiential learning (pp. 191–206). Berlin: Springer.

    Chapter  Google Scholar 

  • Veenman, M. V. J., Wilhelm, P., & Beishuizen, J. J. (2004). The relation between intellectual and metacognitive skills from a developmental perspective. Learning and Instruction, 14, 89–109. doi:10.1016/j.learninstruc.2003.10.004.

    Article  Google Scholar 

  • Veermans, K., Van Joolingen, W. R., & De Jong, T. (2000). Promoting self-directed learning in simulation-based discovery learning environments through intelligent support. Interactive Learning Environments, 8, 229–255. doi:10.1076/1049-4820(200012)8:3;1-D;FT229.

    Article  Google Scholar 

  • Veermans, K., Van Joolingen, W. R., & De Jong, T. (2006). Use of heuristics to facilitate scientific discovery learning in a simulation learning environment in a physics domain. Interactive Learning Environments, 28, 341–361. doi:10.1080/09500690500277615.

    Google Scholar 

  • Vollmeyer, R., & Burns, B. D. (1996). Hypotheseninstruktion und Zielspezifität: Bedingungen, die das Erlernen und Kontrollieren eines komplexen Systems beeinflussen [Hypothesis instruction and goal-specificity: Factors that influence the learning and controlling of a complex system]. Zeitschrift für Experimentelle Psychologie, 43, 657–683.

    Google Scholar 

  • Weinbrenner, S., Engler, J., Wichmann, A., & Hoppe, U. (2010). Monitoring and analysing students’ systematic behaviour—The SCY pedagogical agent framework. Lecture Notes in Computer Science, 6383, 602–607. doi:10.1007/978-3-642-16020-2_61.

    Article  Google Scholar 

  • White, B. Y. (1993). ThinkerTools: Causal models, conceptual change, and science education. Cognition and Instruction, 10, 1–100. doi:10.1207/s1532690xci1001_1.

    Article  Google Scholar 

  • Wilhelm, P., & Beishuizen, J. J. (2003). Content effects in self-directed inductive learning. Learning and Instruction, 13, 381–402. doi:10.1016/S0959-4752(02)00013-0.

    Article  Google Scholar 

  • Zhang, J., Chen, Q., Sun, Y., & Reid, D. J. (2004). Triple scheme of learning support design for scientific discovery learning based on computer simulation: Experimental research. Journal of Computer Assisted Learning, 20, 269–282. doi:10.1111/j.1365-2729.2004.00062.x.

    Article  Google Scholar 

  • Zimmerman, C. (2007). The development of scientific thinking skills in elementary and middle school. Developmental Review, 27, 172–223. doi:10.1016/j.dr.2006.12.001.

    Article  Google Scholar 

  • Zohar, A., & Peled, B. (2008). The effects of explicit teaching of metastrategic knowledge on low- and high-achieving students. Learning and Instruction, 18, 337–353. doi:10.1016/j.learninstruc.2007.07.001.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ard W. Lazonder .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this chapter

Cite this chapter

Lazonder, A.W. (2014). Inquiry Learning. In: Spector, J., Merrill, M., Elen, J., Bishop, M. (eds) Handbook of Research on Educational Communications and Technology. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3185-5_36

Download citation

Publish with us

Policies and ethics