Students should learn science the way scientists do, using inquiry-based learning.


Students should learn science the way scientists do, using inquiry-based learning. But finding student projects that are feasible and interesting is difficult. InquirySpace gives students tools, guidance, and ideas that greatly expand the range and sophistication of meaningful open-ended science investigations.

Every student should have the chance to experience the exciting practice of science. But far too often, students encounter only highly structured “cookbook” labs in their science classrooms. We’re combining a software environment that integrates probeware, video analysis tools, and data exploration capabilities with instructional guidance, and helping students move from fundamental data analysis and scaffolded experiments to open experiments of their own design.

InquirySpace uses innovative 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 our Common Online Data Analysis Platform (CODAP). These tools are integrated into a coherent, Web-based environment enabling rich, collaborative scientific inquiry.

Student materials are designed to help students experience open-ended investigations through a sequence of four steps with increasing degrees of student autonomy, culminating in an open-ended exploration of their own design. Physics activities are already available. New activities are being designed for biology and chemistry.


InquirySpace has demonstrated that typical students can learn to use an integrated set of computer-based tools to undertake sophisticated, open-ended investigations similar to the approach and thinking used by real scientists.

  • We identified and characterized student reasoning during inquiry-based experimentation, called Parameter Space Reasoning (PSR). PSR is associated with planning experiments, operationalizing a set of parameters, navigating the parameter space through multiple experimental runs, identifying patterns in parameter space plots, and reflecting on sources of error. After using InquirySpace, students increased their understanding of PSR and its application to near and medium transfer contexts, but not to far transfer.

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  • We found seven distinct patterns of learning in the Ramp Game. One pattern showed students not improving in scores and another pattern displayed students’ quick mastery while five patterns showed students’ struggles followed by successful learning with different rates and success.

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  • The Bayesian Knowledge Tracing (BKT) model is a popular model used for tracking student progress in learning systems such as an intelligent tutoring system. We analyzed the mathematical structure of the BKT model and constructed a Monte Carlo BKT algorithm to analyze individual students’ knowledge growth during the ramp game developed as part of the IS project. We found that the Monte Carlo BKT analysis can detect the student’s knowledge growth during game play.

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  • We developed a metric based on the sample entropy concept for measuring the systematicity of students’ experimentation patterns in an open-ended simulation environment where a number of parameters are at students’ disposal to explore. Unlike other indicators of systematicity proposed in the literature, the sample entropy metric provides a continuous scale and draws upon the up-to-date computational algorithm applied to dynamic processes involved in physical and biological systems. This sample entropy-based metric correlates significantly with student learning outcomes related to (1) how well students described the nature of relationship explored during their experimentation and (2) whether students coordinated between claim and data collected from their experimentation.

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  • Digital games can provide an opportunity for players to learn new scientific knowledge. Players’ transactions with digital games and related game performances can be automatically collected in time-stamped log files. In this study, we collected data from log files generated by high school students playing a serious game on a computer and analyzed game score patterns based on Monte-Carlo Bayesian Knowledge Tracing. We found a statistically significant, positive scaffolding effect on knowledge gain when students used a graphing tool as compared to when they did not.

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View, launch, and assign activities developed by this project at the STEM Resource Finder.

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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.
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