A model for thinking about scientific experimentation: An InquirySpace framework

How do students learn to think like scientists so they can uncover the natural world’s secrets?

Scientific experimentation allows students to discover some of nature’s secrets. However, it is not simply about collecting data through the use of various apparatus, whether it’s a conventional microscope or advanced computer technologies. Scientific experimentation requires students to engage in the dynamic process of designing, collecting, analyzing, and explaining in order to investigate nature systematically.

The goal of our NSF-funded InquirySpace project is to develop each student’s ability to engage in open-ended science investigations of their own design. Using computer-based tools and scaffolded curriculum modules in physics, chemistry, and biology, students learn to think like scientists.

To better understand student reasoning during inquiry-based experimentation, InquirySpace researchers developed a theoretical framework that illustrates how students are mastering scientific practices and understanding phenomena. The diagram depicts an experimental feedback loop that is influenced by the limits of the physical world on the one hand and the mental models students develop on the other.

“There are multiple levels to consider when you imagine student agency,” explains Dan Damelin, Senior Scientist and Co-Principal Investigator of InquirySpace. “Students have to be clear on the question that is driving their investigation. This will guide them in asking themselves ‘What can I measure and observe? How should I go about designing the experiment to collect data? Once I have the data, how certain am I of the story I can tell? Do I need to collect more data, or redesign my experiment?’”

“Material resistance” (Pickering, 1995) is caused by the constraints imposed by materials used to create, measure, and analyze data related to a scientific phenomenon. For example, the availability of appropriate equipment or the accuracy of the equipment; real-world experiments often yield messy data. Or maybe the location where data collection needs to happen is inaccessible or it’s the wrong time of year.

“There are limitations to any investigation. The weather could be stormy, the river deep, the planet unreachable,” Dan notes. Just as important is the inherent noise of the instruments and procedures used to collect the data. “There’s a constant struggle between the desire to understand the world and the world pushing back. Mother Nature tends not to give up her secrets easily.”

Some of these obstacles or limitations are known to those who conduct experimentation and are subsequently discovered and addressed. Other obstacles and limitations are not necessarily known, which leads to deep uncertainty. The competent investigator’s job is to address as much as he or she is able in order to obtain findings that are reasonable to established standards. Acceptable compromises should be consciously noted and justified in the context of experimentation because they also influence the findings. Examples may include a less precise or less expensive instrument that may not be as accurate but is within acceptable limits; a closer location might glean similar data; samples may have to be collected in the fall instead of summer.

As students consider these challenges, they simultaneously bring and iteratively refine their mental models not only of the phenomenon being investigated, but the process of scientific investigation itself. “This is what scientist do when they’re trying to understand something. They create a model, whether it’s a mental model or a diagram,” explains Dan. “It’s a model of how the world works.”

The experimental feedback loop—design, collect, analyze, explain—is where students are testing their theory and constantly responding to the physical limitations of the experiment and emerging model development. As new data are collected and analyzed, it might change the experimental design or provide a new explanation. That explanation might refine the model or prompt a new design entirely. This constant push and pull between the two sides of the framework eventually coalesces around an explanation and ultimately creates a better understanding and sense of agency of the scientific process.

Our inquiry framework is constantly being tested and evolving as well. This summer, a new cohort of InquirySpace teachers was introduced to the framework in a three-day workshop. They’ll use this pedagogical approach in their physics and biology classes, providing additional data to refine this inquiry framework.

Read more about InquirySpace research on our website.

Pickering, A. (1995). The mangle of practice: Time, agency, and science. Chicago: University of Chicago Press.

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