Identifying potential types of guidance for supporting student inquiry when using virtual and remote labs in science: a literature review
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The aim of this review is to identify specific types of guidance for supporting student use of online labs, that is, virtual and remote labs, in an inquiry context. To do so, we reviewed the literature on providing guidance within computer supported inquiry learning (CoSIL) environments in science education and classified all identified guidance according to a recent taxonomy of types of guidance. In addition, we classified the types of guidance in phases of inquiry. Moreover, we examined whether the types of guidance identified for each inquiry phase were found to be effective in promoting student learning, as documented in the CoSIL research. This review identifies what types of effective guidance currently exist and can be applied in developing future CoSIL environments, especially CoSIL environments with online labs. It also highlights the needs/shortcomings of these available types of guidance. Such information is crucial for the design and development of future CoSIL environments with online labs.
KeywordsGuidance Computer Supported Learning Inquiry Process constraints Performance dashboard Prompts Heuristics Scaffolds Direct presentation of information
This study was conducted in the context of the research project Global Online Science Labs for Inquiry Learning at School (Go-Lab), which is funded by the European Community under the Information and Communication Technologies (ICT) theme of the 7th Framework Programme for R&D (Grant Agreement No.: 317601).
References marked with an asterisk indicate studies which have been reviewed
- *Azevedo, R., Cromley, J. G., & Seibert, D. (2004). Does adaptive scaffolding facilitate students’ ability to regulate their learning with hypermedia? Contemporary Educational Psychology, 29, 344–370. doi: 10.1016/j.cedpsych.2003.09.002.
- *Belland, R. B. (2010). Portraits of middle school students constructing evidence-based arguments during problem-based learning: the impact of computer-based scaffolds. Educational Technology Research and Development, 58, 285–309. doi: 10.1007/s11423-009-9139-4.
- *Biesinger, K., & Crippen, K. (2010). The effects of feedback protocol on self-regulated learning in a web-based worked example learning environment. Computers & Education, 55, 1470–1482. doi: 10.1016/j.compedu.2010.06.013.
- *Butler, A. K., & Lumpe, A. (2008). Student use of scaffolding software: Relationships with motivation and conceptual understanding. Journal of Science Education and Technology, 17, 427–436. doi: 10.1007/s10956-008-9111-9.
- *Chang K. E., Chen Y. L., Lin H. Y., & Sung Y. T. (2008). Effects of learning support in simulation-based physics learning. Computers & Education, 51, 1486–1498. doi: 10.1016/j.compedu.2008.01.007.
- *Cho, Y., & Jonassen, D. (2012). Learning by self-explaining causal diagrams in high-school biology. Asia Pacific Education Review, 13, 171–184. doi: 10.1007/s12564-011-9187-4.
- *Crippen, J. K., & Earl, L. B. (2007). The impact of web-based worked example and self-explanation on performance, problem solving, and self-efficacy. Computers & Education, 49, 809–821. doi: 10.1016/j.compedu.2005.11.018.
- 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. (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., Härtel, H., Swaak, J., & van Joolingen, W. (1996). Support for simulation-based learning: The effect of assignments in learning about transmission lines. In A. Diaz de Ilarraza Sanchez & I. Fernandez de Castro (Eds.), Computer aided learning and instruction in science and engineering (pp. 9–26). Heidelberg: Springer. doi: 10.1007/BFb0022586.
- de Jong, T., & Lazonder, A. W. (2014). The guided discovery principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 371–390). Cambridge: Cambridge University Press.Google Scholar
- *de Jong, T., Martin, E., Zamaro, J. M., Esquembre, F., Swaak, J., & van Joolingen, W. R. (1999). The integration of computer simulation and learning support: An example from the physics domain of collisions. Journal of Research in Science Teaching, 36, 597–615. doi: 10.1002/(SICI)10982736(199905)36:5<597::AID-TEA6>3.0.CO;2-6.
- de Jong, T., van Joolingen, W. R., Scott, D., de Hoog, R., Lapied, L., & Valent, R. (1994). SMISLE: System for multimedia integrated simulation learning environments. In T. de Jong & L. Sarti (Eds.), Design and production of multimedia and simulation based learning material (pp. 133–167). Dordrecht, The Netherlands: Kluwer Academic Publishers. doi: 10.1007/978-94-011-0942-0_8.CrossRefGoogle Scholar
- *de Jong, T., van Joolingen, W. R., Swaak, J., Veermans, K., Limbach, R., King, S., & Gureghian, D. (1998). Self-directed learning in simulation-based discovery environments. Journal of Computer Assisted Learning, 14, 235–246. doi: 10.1046/j.1365-2729.1998.143060.x
- de Jong, T., Weinberger, A., Girault, I., Kluge, A., Lazonder, W. A., Pedaste, M., & Zacharia, Z. C. (2012). Using scenarios to design complex technology-enhanced learning environments. Educational Technology Research and Development, 60, 883–901. doi: 10.1007/s11423-012-9258-1.CrossRefGoogle Scholar
- *Demetriadis, N. S., Papadopoulos, M. P., Stamelos, G. I., & Fischer, F. (2008). The effect of scaffolding students’ context-generating cognitive activity in technology-enhanced case-based learning. Computer & Education, 51, 939–954. doi: 10.1016/j.compedu.2007.09.012.
- Donnelly, D. F., Linn, M. C., & Ludvigsen, S. (2014). Impacts and characteristics of computer-based science inquiry learning environments for precollege students. Review of Educational Research. Available at http://rer.sagepub.com/content/early/2014/08/14/0034654314546954.full.pdf+html. doi: 10.3102/0034654314546954.
- *Dunbar, K. (2000). How scientists think in the real world: Implications for science education. Journal of Applied Developmental Psychology, 21, 49–58. doi: 10.1016/S0193-3973(99)00050-7.
- *Eckhardt, M., Urhahne, D., Conrad, O., & Harms, U. (2013). How effective is instructional support for learning with computer simulations? Instructional Science, 41, 105–124. doi: 10.1007/s11251-012-9220-y.
- *Edelson, D. C., Gordin, D. N., & Pea, R. D. (1999). Addressing the challenges of inquiry-based learning through technology and curriculum design. Journal of the Learning Sciences, 8, 391–450. doi: 10.1080/10508406.1999.967.
- *Fretz, E. B, Wu, H. K., Zhang, B., Davis, E. A., Krajcik, J. S., & Soloway, E. (2002). An investigation of software scaffolds supporting modeling practices. Research in Science Education, 32, 567–589. doi: 10.1023/A:1022400817926.
- Gerjets, P., Scheiter, K., & Schuh, K. (2008). Information comparisons in example-based hypermedia environments: supporting learners with processing prompts and an interactive comparison tool. Educational Technology Research and Development, 56, 73–92. doi: 10.1007/s11423-007-9068-z.CrossRefGoogle Scholar
- *Gijlers, H., & de Jong, T. (2009). Sharing and confronting propositions in collaborative inquiry learning. Cognition and Instruction, 27, 239–268. doi: 10.1080/07370000903014352.
- *Graesser, A., Wiley, J., Goldman, S., O’Reilly, T., Jeon, M., & McDaniel, B. (2007). SEEK web tutor: Fostering a critical stance while exploring the causes of volcanic eruption. Metacognition and Learning, 2, 89–105. doi: 10.1007/s11409-007-9013-x.
- *Hand, B. (2004). Using a science writing heuristic to enhance learning outcomes from laboratory activities in seventh-grade science: Quantitative and qualitative aspects. International Journal of Science Education, 26, 131–149. doi: 10.1080/0950069032000070252.
- Justice, C., Warry, W., Cuneo, C. L., Inglis, S., Miller, S., Rice, J., & Sammon, S. (2001). A grammar for inquiry: Linking goals and methods in a collaborative taught social sciences inquiry course. In: The Allan Blizzard Award Paper: The Award Winning Papers (Windsor: Special Publication of the Society for Teaching and Learning in Higher Education and McGraw-Hill Ryerson).Google Scholar
- *Keys, C. W. (2000). Investigating the thinking processes of eight writers during the composition of a scientific laboratory report. Journal of Research in Science Teaching, 37, 676–690. doi: 10.1002/1098-2736(200009)37:7<676::AID-TEA4>3.0.CO;2-6.
- *Keys, C. W., Hand, B., Prain, V., & Collins, S. (1999). Using the science writing heuristic as a tool for learning from laboratory investigations in secondary science. Journal of Research in Science Teaching, 36, 1065–1084. doi: 10.1002/(SICI)1098-2736(199912)36:10<1065::AID-TEA2>3.0.CO;2-I.
- *Kim, J. H., & Pedersen, S. (2011). Advancing young adolescents’ hypothesis- development performance in a computer-supported and problem-based learning environment. Computers & Education, 57, 1780–1789. doi: 10.1016/j.compedu.2011.03.014.
- *Klahr, D., Fay, A., & Dunbar, K. (1993). Heuristics for specific experimentation: A developmental study. Cognitive Psychology, 25, 111–146. doi: 10.1006/cogp.1993.1003.
- *Kyza, E., Michael, G., & Constantinou, C. (2007). The rationale, design, and implementation of a web-based inquiry learning environment. In C. Constantinou, Z. C. Zacharia, & M. Papaevripidou (Eds.), Contemporary Perspectives on New Technologies in Science and Education, Proceedings of the Eighth International Conference on Computer Based Learning in Science (pp. 531–539). Crete, Greece: E-media.Google Scholar
- *Lajoie, S. P., Lavigne, N. C., Guerrera, C., & Munsie, S. D. (2001). Constructing knowledge in the context of Bioworld. Instructional Science, 29, 155–186. doi: 10.1023/A:1003996000775.
- *Lee H. W., Lim K. Y. & Grabowski, B. (2010). Improving self-regulation, learning strategy use, and achievement with metacognitive feedback. Educational Technology Research and Development, 58, 629–648. doi: 10.1007/s11423-010-9153-6.
- *Lewis, E. L., Stern, J. L., & Linn, M. C. (1993). The effect of computer simulations on introductory thermodynamics understanding. Educational Technology, 33, 45–58.Google Scholar
- *Lin, X., & Lehman, D.J. (1999). Supporting learning of variable control in a computer-based biology environment: effects of prompting college students to reflect on their own thinking. Journal of Research in Science Teaching, 36, 837–858. doi: 10.1002/(SICI)1098-2736(199909)36:7<837::AID-TEA6>3.0.CO;2-U.
- *Löhner, S., van Joolingen, W. R., & Savelsbergh, E. R. (2003). The effect of external representation on constructing computer models of complex phenomena. Instructional Science, 31, 395–418. doi: 10.1023/A:1025746813683.
- *Luchini, K., Quintana, C., & Soloway, E. (2003). Pocket PiCoMap: A case study in designing assessing a handheld concept mapping tool for learners. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 321–328), ACM. doi: 10.1145/642611.642668.
- *MacGregor, S. K., & Lou, Y. (2004) Web-based learning: How task scaffolding and web site design support knowledge acquisition. Journal of Research on Technology in Education, 37, 161–175. doi: 10.1080/15391523.2004.10782431.
- *Manlove, S., Lazonder, W. A., & de Jong, T. (2007). Software scaffolds to promote regulation during scientific inquiry learning. Metacognition and Learning, 2, 141–155. doi: 10.1007/s11409-007-9012-y.
- *Manlove, S., Lazonder, W. A., & 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.
- *Marschner, J., Thillmann, H., Wirth, J., & Leutner, D. (2012). How can the use of strategies for experimentation be fostered? Zeitschrift Fur Erziehungswissenschaft, 15, 77–93. doi: 10.1007/s11618-012-0260-5.
- *McNeill, K. L., Lizotte, D. J., Krajcik, J., & Marx, R. W. (2006). Supporting students’ construction of scientific explanations by fading scaffolds in instructional materials. Journal of the Learning Sciences, 15, 153–191. doi: 10.1207/s15327809jls1502_1.
- *Molenaar, I., van Boxtel, A. M. C., & Sleegers, J. C. P. (2010). The effects of scaffolding metacognitive activities in small groups. Computers in Human Behavior, 26, 1727–1738. doi: 10.1016/j.chb.2010.06.022.
- *Moos, D. C., & Azevedo, R. (2008). Exploring the fluctuation of motivation and use of self-regulatory processes during learning with hypermedia. Instructional Science 36, 203–231. doi: 10.1007/s11251-007-9028-3.
- *Moreno, R., Mayer, R., Spires, H., & Lester, J. (2001). The case for social agency in computer-based teaching: do students learn more deeply when they interact with animated pedagogical agents? Cognition and Instruction, 19, 177–213. doi: 10.1207/S1532690XCI1902_02.
- *Oshima, J., Oshima, R., Murayama, I., Inagaki, S., Takenaka, M., Yamamoto, T., Nakayama, H. (2006). Knowledge-building activity structures in Japanese elementary science pedagogy. Computer-Supported Collaborative Learning, 1, 229–246. doi: 10.1007/s11412-006-8995-8.
- Pedaste, M., Mäeots, M., Siiman, L. A., de Jong, T., van Riesen, S. A. N., Kamp, E. T., et al. (in press). Phases of inquiry-based learning: Definitions and the inquiry cycle. Educational Research Review.Google Scholar
- Plass, J. L., & Schwartz, R. N. (2014). Multimedia learning with simulations and microworlds. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 729–761). Cambridge: Cambridge University Press.Google Scholar
- Polya, G. (1945). How to solve it. Princeton, NJ: Princeton University Press.Google Scholar
- *Puntambekar, S., & Kolodner, L. J. (2005). Toward implementing distributed scaffolding: Helping students learn science from design. Journal of Research in Science Teaching, 42, 185–217. doi: 10.1002/tea.20048.
- *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. doi: 10.1080/08886504.1994.10782117.
- *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 Associates.Google Scholar
- *Sao Pedro, M. A., de Baker, R. S., Gobert, J. D., Montalvo, O., & Nakama, A. (2013). Leveraging machine-learned detectors of systematic inquiry behavior to estimate and predict transfer of inquiry skill. User Modeling and User-Adapted Interaction, 23, 1–39. doi: 10.1007/s11257-011-9101-0.
- Scanlon, E., Anastopoulou, S., Kerawalla, L., & Mulholland, P. (2011). How technology resources can be used to represent personal inquiry and support students’ understanding of it across contexts. Journal of Computer Assisted learning, 27, 516–529. doi: 10.1111/j.1365-2729.2011.00414.x.CrossRefGoogle Scholar
- *Schauble, L., Glaser, R., Raghavan, K., & Reiner, M. (1991). Causal models and experimentation strategies in scientific reasoning. Journal of the Learning Sciences, 1, 201–238. doi: 10.1207/s15327809jls0102_3.
- Schoenfeld, A. (1979). Can heuristics be taught? In J. Lochhead & J. Clement (Eds.), Cognitive process instruction (pp. 315–338). Philadelphia: Franklin Institute Press.Google Scholar
- Schoenfeld, A. (1985). Mathematical problem solving. New York: Academies Press.Google Scholar
- *Schunn, C. D., & Anderson, J. R. (1999). The generality/specificity of expertise in scientific reasoning. Cognitive Science, 23, 337–370. doi: 10.1207/s15516709cog2303_3.
- Shute, V. J., & Glaser, R. (1990). A large-scale evaluation of an intelligent discovery world: Smithtown. Interactive Learning Environments, 1, 51–77. doi: 10.1080/1049482900010104.
- Smith, B. K., & Reiser, B. J. (1997). What should a wildebeest say? Interactive nature films for high school classrooms. Paper presented at the ACM Multimedia, Seattle. doi: 10.1145/266180.266365.
- *Stahl, E., & Bromme, R. (2009). Not everybody needs help to seek help: surprising effects of metacognitive instructions to foster help-seeking in an online-learning environment. Computers & Education, 53, 1020–1028. doi: 10.1016/j.compedu.2008.10.004.
- *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: 10.1016/S0959-4752(98)00018-8.
- *Thillmann, H., Kunsting, J., Wirth, J., & Leutner, D. (2009). Is it merely a question of ‘what’ to prompt or also ‘when’ to prompt? The role of point of presentation time of prompts in self-regulated learning. Zeitschrift Fur Padagogische Psychologie, 23, 105–115. doi: 10.1024/1010-06220.127.116.11.
- *Tschirgi, J. E. (1980). Sensible reasoning: A hypothesis about hypotheses. Child Development, 51, 1–10. doi: 10.2307/1129583.
- *van Joolingen, W. R., & de Jong, T. (2003). SimQuest: authoring educational simulations. In T. Murray, S. Blessing, & S. Ainsworth (Eds.), Authoring tools for advanced technology educational software: Toward cost-effective production of adaptive, interactive, and intelligent educational software (pp. 1–31). Dordrecht: Kluwer Academic Publishers.Google Scholar
- *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.Google Scholar
- *Veermans, K. H. (2003). Intelligent support for discovery learning. Ph.D. thesis, University of Twente.Google Scholar
- *Veermans, K. H., van Joolingen, W., & de Jong, T. (2006). Use of heuristics to facilitate scientific discovery learning in a simulation learning environment in a physics domain. International Journal of Science Education, 28, 341–361. doi: 10.1080/09500690500277615.
- White, B. Y., Shimoda, T. A., & Frederiksen, J. R. (1999). Enabling students to construct theories of collaborative inquiry and reflective learning: Computer support for metacognitive development. International Journal of Artificial Intelligence in Education, 10, 151–182.Google Scholar
- *Wichmann, A., & Leutner, D. (2009). Inquiry learning multilevel support with respect to inquiry, explanations and regulation during an inquiry cycle. Zeitschrift Fur Padagogische Psychologie, 23, 117–127. doi: 10.1024/1010-0618.104.22.168.
- *Wirth, J., Künsting, J., & Leutner, D. (2009). The impact of goal specificity and goal type on learning outcome and cognitive load. Computers in Human Behavior, 25, 299–305. doi: 10.1016/j.chb.2008.12.004.
- *Woolf, B., Reid, J., Stillings, N., Bruno, M., Murray, D., Reese, P., Peterfreund, A., & Rath, K. (2002). A general platform for inquiry learning. Proceedings of the International Conference on Intelligent Tutoring Systems, Biarritz, France. doi: 10.1007/3-540-47987-2_69. Retrieved 24 Jan, 2012 from http://link.springer.com/chapter/10.1007%2F3-540-47987-2_69?LI=true.
- *Wu, H. K. (2010). Modeling a complex system: using novice-expert analysis for developing an effective technology-enhanced learning environment. International Journal of Science Education, 32, 195–219. doi: 10.1080/09500690802478077.
- *Yaman, M., Nerdel, C., & Bayrhuber, H. (2008). The effects of instructional support and learner interests when learning using computer simulations. Computer & Education, 51, 1784–1794. doi: 10.1016/j.compedu.2008.05.009.
- *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, B. J. (2001). Theories of self-regulated learning and academic achievement: An overview and analysis. In B. J. Zimmerman & D. H. Schunk (Eds.), Self-regulated learning and academic achievement—theoretical perspectives (pp. 1–37). Mahwah, NJ: Erlbaum.Google Scholar
- *Zumbach, J. (2009). The role of graphical and text based argumentation tools in hypermedia learning. Computers & Education, 25, 811–817. doi: 10.1016/j.chb.2008.07.005.