Preparing Learners for the Future
We live in an interconnected world of accelerating complexity. As populations expand and people, economies, and nations become inextricably intertwined, even seemingly simple problems reveal intricate subtleties. And the issues of our time are far from simple. Preparing for and defending against disease. Assessing global energy needs. Taking a principled stand on major political causes. All are rife with complication. How can we prepare today’s children to live and work productively in a future defined by complexity?
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.
Dealing with the data deluge
Some of the most imposing aspects of this rapidly approaching future appear in the guise of data. “Big data” now permeate all aspects of life. In less than a decade, data science has grown into a high-demand profession, and education has not kept pace. Our children are in danger of being stranded without proper preparation. Across varied projects with multiple partners, we at the Concord Consortium are responding by spearheading the new field of data science education and forming and refining its requisite tools, networks, and pedagogies.
To fuel this new field, we are developing the Common Online Data Analysis Platform (CODAP), a data exploration tool designed to enable more learners to use more data in more places. We envision a future where hundreds of curricula, thousands of classrooms, and millions of students worldwide engage in data exploration with ease and sophistication. To realize this vision, we are collaborating with others to define data science education, provide examples to further it in classrooms from kindergarten to college, and establish essential research networks to guide our work.
Comprehending complex systems
One of the biggest issues is the need to grapple with the dynamics of complex systems. As the number of interconnections within systems increase, they assume an entirely new nature. Actions involving tiny elements on a system’s periphery can elicit significant effects throughout. These effects may arise with surprising immediacy or emerge only following mysterious dormancies or delays. In other cases, feedback loops dampen or amplify effects.
Most large problems involve such complex systems and their effects. In fact, most actually involve multiple, interlinked complex systems. Such situations, often called “wicked problems,” are defined by their almost inscrutable causalities. Learning the ins and outs of such systems is a new educational challenge that demands wholly new tools and approaches. Our SageModeler software is one such innovative tool, a dynamic technology environment aimed to make concepts of system modeling accessible for students as young as middle school. Representing quantities through custom icons and connections through easy-to-understand arrows, SageModeler permits learners to quickly sketch intricate systems of their own making. Once learners have depicted a system’s basic components and connections, they can specify the nature of the interdependencies and run dynamic simulations to explore the system’s behavior. Further, students can compare their model output with real-world data. This ability—to move from concept to near-instant results—makes cause and effect visible in dynamic detail and transforms complexities and abstract musings into immediately testable hypotheses.
Though such a modeling environment is already transformative, combining technologies in cutting-edge ways can provide even more powerful views into systems. Agent-based simulations, in which individual entities such as ants or people operate independently, each following simple sets of rules, have long helped both learners and professionals visualize important aspects of complex phenomena. Technologies such as MIT’s StarLogo—with its intuitive block programming approach—make it possible for learners to create systems and examine their emergent behaviors (e.g., population curves or flocking) with relative ease. This ease of use has a flip side, however. The block programs underlying these simulations can rapidly become difficult to parse. This presents a dilemma: Should we provide learners with access to the details of complex systems and risk muddling the big picture in ungainly coding or forego the coding for a conceptual view and risk losing learners in high-level abstraction?
We are currently merging the two into a whole greater than the sum of its parts—an example of linked-hybrid modeling. It will seamlessly fuse high-level system diagrams with detailed agent-based simulations, permitting learners to move smoothly back and forth between the two. This new technology, patterned after similar approaches used by practicing complexity theorists, will open new possibilities for systems learning, facilitating broad, inclusive perspectives and fostering previously inaccessible connections.
When confronting nuanced problems and large datasets, even tiny points of friction in the inquiry and exploration process can quickly become substantial barriers to learning. We’re addressing this by integrating proven tools with new ones in ways that can make essential scientific practices seamless. These tools allow learners to use wireless probes to collect data easily and quickly and make exploring and analyzing data seamless and engaging. And we’re crafting approaches that scaffold learners’ use of these tools, allowing them to compare real-world data with predictions from both theory and computational models.
Our research seeks to understand how these tools and approaches redefine the very nature of learning through experimentation. This work sheds new light on the familiar—highlighting the fundamental importance of “messing around” with parameters and experimental setups and defining the nature and trajectory of “parameter space reasoning” critical for operating in a world drenched in data. In the process, we’re creating learners with the skills and tenacity needed to undertake extended, independent investigations into open-ended problems.
Collaborating amid complexity
We don’t expect learners to approach tomorrow’s complicated world alone. On the contrary, we must prepare them for a future of creative, technology-based collaboration centered on highly interdisciplinary problems. Our groundbreaking work is creating and researching tools and patterns that foster productive collaboration. We’re building innovative technology environments that can enable seamless cooperation around simulations, experiments, and shared datasets. We’re identifying curricular approaches that inspire collaborative work and make the best possible uses of technology. And we’re researching new ways to use analytics to monitor collaboration in real time and provide meaningful feedback.
As students work on technology-based challenges in classroom groups, our systems will aid them while simultaneously providing vital information and guidance to their teachers. Such tools may steer teachers toward the students most in need of assistance, supplying real-time background notes and suggestions. A dynamic, whole-class overview can enable teachers to “be everywhere,” allowing them to observe a busy collaborative classroom while saving targeted artifacts of student work for later use in large-group conversations.
The world’s increasing complexity can feel overwhelming. We see it as an open call for experimentation and invention. We also find it deeply inspiring. As we develop and research transformative approaches to learning, we’re not merely helping to solve today’s problems, we’re equipping entire future generations of youth with the skills to solve complex problems in innovative new ways.
Chad Dorsey (firstname.lastname@example.org) is President of the Concord Consortium.