Technology Adds Depth to Three-Dimensional Learning
The Next Generation Science Standards (NGSS) and related “three-dimensional learning” approaches define a new paradigm in STEM teaching and learning that is refreshing, revolutionary, and research based. The science and engineering practices they espouse frame pedagogy in important new ways. However, shining the bright light of technology’s potential on these practices reveals intriguing new possibilities—and uncovers a few glaring gaps.
The notion that accelerating interconnectedness is an inescapable aspect of modern life could be disheartening, particularly for those of us in education. However, we see things differently from our vantage point at the Concord Consortium. Rather than become buried in the problems of the present, our tools, approaches, and research are rising to the challenge of the future, enabling learners to investigate elaborate problems and to build the skills necessary to navigate a complexity-rich world.
As we implement three-dimensional learning, it is important to stay attuned to the many possibilities technology opens up. A close-up view of two of the NGSS science and engineering practices demonstrates how the introduction of technology’s full potential can change their emphasis in subtle, but often critical ways.
When we imagine science education in action, the most common image that comes to mind is the science lab. The laboratory, we all know, is where students are supposed to experience real science. Pendulums swing, beakers bubble, students hunch over microscopes—the lab is where hands-on meets minds-on.
Despite what the posters in countless science labs might still have you believe, it’s now widely accepted that the “scientific method” bears scant resemblance to the way true scientific discovery proceeds. As practicing scientists uncover new knowledge about our world, they do not consult checklists or follow a series of linear steps. The NGSS acknowledge this in several ways, most prominently by framing science as a set of related practices. “Planning and carrying out investigations” is one of these practices.
In many ways, the laboratory is the centerpiece of the scientific endeavor—science would be nothing without experiments. However, even the standards can be interpreted in ways that threaten to reduce this practice to the mere application of a generalized strategy, such as controlling variables. While this strategy is undeniably important, applying it too generally can create shallow experiences for learners, sidestepping opportunities to engage with the underlying conceptual model being investigated. Placing too heavy a focus on the generalized technique of controlling variables also risks mischaracterization and missed opportunity. Scientists in many major branches of science—geologists, field biologists, cosmologists—simply cannot run controlled experiments. Yet their techniques of comparative analysis hold just as much value and their contributions to science carry just as much import.
Our work in projects such as InquirySpace and InSPECT pushes these boundaries, redefining what technology-based investigation means for STEM learning. These projects integrate data collection directly into activities and house collected data within a data analysis tool, inviting learners to explore and examine continuously. They provide concrete representations of the data collection process, aiding learners’ comprehension, and allow fine-grained command over automated data collection processes. They also provide programmable triggers, enabling learners to incorporate controlled external modules such as lights, pumps, and motors into their experiments. Equipped with these tools, learners can investigate otherwise inconceivable realms, and are empowered to ask new “What if?” questions.
Tools such as these also introduce meaningful personal agency to investigations, a subtle notion that can be stunning to observe in action. The first time a student lights up with an idea that might crack the question she’s been working at all week, the moment when two disengaged students break into excited debate about what color of light to shine on their plant, the hour they then spend diving into their data to build, support, and refine original conjectures—moments of empowered learning such as these make technology’s value starkly apparent. With new technology-based tools and approaches, our projects are forging novel ground, deepening student investigation across all STEM domains.
Though all STEM practices are naturally interlinked, “analyzing and interpreting data” is perhaps one of the most intertwined of all. Practically all meaning-making in the science classroom centers on the analysis of data in one way or another. However, we need only look at the daily news to see the disconnect between “real” data and its classroom counterpart. News stories regularly tout the role of data in all corners of industry and everyday life, yet the real data they describe are entirely different from the oversimplified versions students encounter in a typical science class.
Real data are messy—multiple variables, heterogeneous data types, large datasets—and they harbor complex, multi-layered stories. Working with these messy datasets is a science in itself (so much so that it’s called data science). But, despite the fact that it’s practically impossible to imagine a major future occupation that won’t touch on data science in some form, practically no place in our current STEM curricula builds these much needed skills. STEM education must evolve to incorporate data science education.
The Concord Consortium has helped spearhead the field of data science education. A core aspect of this movement involves rethinking what’s included in the practice of analyzing and interpreting data. Data moves* are operations that re-structure, re-represent, and re-construct data in ways that help uncover hidden meaning. Though they represent core aspects of engaging with data, such moves need not be inherently complex—filtering a dataset to look at a subset and reorganizing a dataset to reflect inherent groupings are two key examples. However, they are rarely, if ever, emphasized in today’s STEM learning.
Part of the reason is that it is nearly impossible to engage in activities resembling data science education in the absence of technology. Furthermore, the mere presence of technology alone is not sufficient in itself; many technologies allow students to enter data and create graphs, but provide no opportunity to engage in essential activities such as exploring a dataset or interacting with its structure. Data tools such as our Common Online Data Analysis Platform (CODAP) are designed from the ground up for learning about and with data. As such, they intentionally evoke exploration and deepen discovery, helping learners uncover new questions in the process of seeking answers. They highlight connections across different representations, permit learners to easily reorganize and filter data, and enable the rapid generation of multiple different graphs from a dataset. Through the design and application of such technology, we aim to enrich the practice of analyzing and interpreting data and bring data science education to learners worldwide.
The role of technology
These examples, viewed through the practices of “planning and carrying out investigations” and “analyzing and interpreting data,” reflect the Concord Consortium’s fundamental view of the world: technology used to its fullest extent can introduce critical new dimensions to STEM teaching and learning, deepening and expanding all science and engineering practices to reach topics or enable methods that might otherwise be unimaginable. At its core, technology makes open exploration possible across a huge diversity of STEM domains, whether the study of genetics or plate tectonics, the examination of individual chemical bonds, or the motion of everyday objects. Technology allows learners to explore concepts freely and broadly, pose their own questions, conduct original experiments, and make novel claims. At its most powerful, technology does what we all aim to do: bring students as close as possible to the process of truly doing science.
* See Tim Erickson’s blog at bestcase.wordpress.com.
Chad Dorsey (firstname.lastname@example.org) is President of the Concord Consortium.