Students should learn science by doing science. They should select their own question, design and execute a study, draw conclusions based on their data, and communicate their findings. InquirySpace provides ideas, approaches, and technologies to make this approach to learning easier for classroom teachers to offer—and more effective.
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Science should be learned in the way scientists learn, using inquiry-based learning or "extended inquiry." But finding projects for students that are feasible and interesting is a major problem that teachers face when taking this advice seriously. Projects cannot be too complex or too long; they cannot demand expensive equipment or require unusual skills. And projects must address the course learning goals. These constraints limit the range of feasible student projects. InquirySpace gives students tools, guidance, and ideas that greatly expand the range and sophistication of meaningful open-ended science investigations.
InquirySpace uses three proven technologies—the versatile modeling environments of NetLogo and the Molecular Workbench, real-time data collection from probes and sensors, and the powerful visual data exploration capabilities of CODAP. These tools will be integrated into a coherent, Web-based environment enabling rich, collaborative scientific inquiry.
The ability to produce and conduct collaborative inquiry activities can make inquiry far more effective and widespread in introductory science instruction. InquirySpace can be used across many grades and in diverse schools, not only in colleges and high-performing schools, but in under-resourced schools in which students often are performing below grade level and for whom text-based instruction is decontextualized and difficult.
This material is based upon work supported by the National Science Foundation under Grant No. IIS-1147621. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
The Concord Consortium (n.d.) InquirySpace. Retrieved 2015, May 30 from http://concord.org/projects/inquiryspace
Disclaimer: The Concord Consortium offers citation styles as a guide only. We cannot offer interpretations about citations as this is an automated procedure.
The project will study both how to optimize the project design as well as what students learn when they use this approach.
The first study will use design research with small groups of students to improve the software and the curriculum. Three cycles of observation and revision will produce an effective package that students and teachers can easily use.
The second study will characterize what students learn when they undertake extended inquiry with InquirySpace and what role the tools and general approach had in student learning. We will characterize student learning and develop rubrics that can be used to measure more general experimentation skills and knowledge about the process of science.
A curriculum for inquiry may sound like an oxymoron. But students cannot simply be turned loose and asked to invent a great project. They need tools and the ability to use those tools.
Most educators interpret "inquiry" or "extended inquiry" as a hypothesis-testing design in which students formulate a hypothesis and design a study to prove or disprove it. But this is not the way most science proceeds. In fact, almost all research outside medicine and the social sciences is of the form "I looked at this system and observed the following." This is a simpler and more intuitive research design—and the approach we’ll use.
We will start by guiding students through a possible project while introducing software, probeware, and a general roadmap for similar explorations. For instance, a guided inquiry of pendulums would involve measuring the pendulum angle as a function of time over multiple runs. The length of the pendulum and its mass would be varied. The period and decay rate values would be extracted. Software based on Fathom will be used to explore the relationships between the parameters and the values. Modeling software using NetLogo and the Molecular Workbench will allow students to build a mathematical model of the pendulum to understand how it works.
This general approach of making multiple runs for different parameters, measuring values, and modeling the system forms a general roadmap that can be used in many investigations of real and virtual systems. Once students understand the roadmap and have mastered the tools, they are ready for their own inquiries. InquirySpace will provide dozens of ideas along with suggestions for how to proceed.
Articles and Papers
Gweon, G.-H, Lee, H.-S., Dorsey, C., Tinker, R., Finzer, W., & Damelin, D. (2015). Tracking student progress in a game-like learning environment with a constrained Bayesian Knowledge Tracing model. Proceedings in Learning Analytics & Knowledge Conference 2015, Poughkeepsie, NY.
Gweon, G.-H, Lee, H.-S., Dorsey, C., Tinker, R., Finzer, W., & Damelin, D. (2015). Tracking student progress in a game-like physics learning environment with a Monte Carlo Bayesian Knowledge Tracing model. Paper presented at the annual meeting of American Physical Society, San Antonio, TX.
Finzer, W. (2014). Hierarchical data visualization as a tool for developing student understanding of variation of data generated in simulations. In Proceedings of the ninth international conference on teaching statistics (Vol. 6). Voorburg: International Statistics Institute.
Finzer, W., & Tinker, R. (2015). Under the hood: Embedding a simulation in CODAP. @Concord, 19(1), 14.
Lee, H.-S.,Gweon, G.-H., Dorsey, C, Tinker, R., Finzer, W., Damelin, D., Kimball, N., Pallant, A., & Lord, T. (2015). How does Bayesian Knowledge Tracing model student development of knowledge about a simple physical system? Proceedings in Learning Analytics & Knowledge Conference 2015, Poughkeepsie, NY.
Lee, H. -S., Pallant, A., Tinker, R., & Horwitz, P. (in press). High school students' parameter space navigation and reasoning during simulation-based experimentation. In (Eds.),Proceedings of the Eleventh International Conference of the Learning Sciences (ICLS ’2014) (pp.). Boulder, CO: International Society of the Learning Sciences.
Pallant, A, Lee, H-A, and Kimball, N. (2015). Analytics and student learning: An example from InquirySpace. @Concord, 19(1), 8-9.
Stephens, A.L. and Pallant, A. (2015). From Graphs as Task to Graphs as Tool: Scaffolded Data Analysis. Manuscript submitted for publication.
Hazzard, E. (2014). A New Take on Student Lab Reports. The Science Teacher. March. (Posted with permission of The Science Teacher.)
Tinker, R., Hazzard, E. (2012). InquirySpace: A Space for Real Science. @Concord. 16(2) 8-9.