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.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
*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.
Amsel, E., & Brock, S. (1996). The development of evidence evaluation skills. Cognitive Development, 11, 523–550. doi:10.1016/S0885-2014(96)90016-7.
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.
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.
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.
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.
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.
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.
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.
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.
De Jong, T. (2006a). Computer simulations: Technological advances in inquiry learning. Science, 312, 532–533. doi:10.1126/science.1127750.
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.
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.
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.
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.
Dean, D., & Kuhn, D. (2007). Direct instruction vs. discovery: The long view. Science Education, 91, 384–397. doi:10.1002/sce.20194.
Dunbar, K. (1993). Concept discovery in a scientific domain. Cognitive Science, 17, 397–434. doi:10.1207/s15516709cog1703_3.
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.
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.
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.
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.
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.
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.
Horwitz, P., Neumann, E., & Schwartz, J. (1996). Teaching science at multiple levels: The GenScope program. Communications of the ACM, 39, 127–131.
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.
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.
Inhelder, B., & Piaget, J. (1958). The growth of logical thinking from childhood to adolescence. New York, NY: Basic Books.
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.
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.
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.
Klahr, D. (2000). Exploring science: The cognition and development of discovery processes. Cambridge, MA: MIT Press.
Klahr, D. (2005). Early science instruction: Addressing fundamental issues. Psychological Science, 16, 871–872. doi:10.1111/j.1467-9280.2005.01629.x.
Klahr, D., & Dunbar, K. (1988). Dual space search during scientific reasoning. Cognitive Science, 12, 1–48. doi:10.1207/s15516709cog1201_1.
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.
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.
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.
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.
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.
Kuhn, D., Amsel, E., & O’Loughlin, M. (1988). The development of scientific thinking skills. Orlando, FL: Academic.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Ross, J. A. (1988). Controlling variables: A meta-analysis of studies. Review of Educational Research, 58, 405–437. doi:10.3102/0034654305800440.
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.
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.
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.
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.
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.
Tschirgi, J. E. (1980). Sensible reasoning: A hypothesis about hypotheses. Child Development, 51, 1–10. doi:10.1111/1467-8624.ep12325377.
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.
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.
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.
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.
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.
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.
White, B. Y. (1993). ThinkerTools: Causal models, conceptual change, and science education. Cognition and Instruction, 10, 1–100. doi:10.1207/s1532690xci1001_1.
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.
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.
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.
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights 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
DOI: https://doi.org/10.1007/978-1-4614-3185-5_36
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-3184-8
Online ISBN: 978-1-4614-3185-5
eBook Packages: Humanities, Social Sciences and LawEducation (R0)