Deconstructing STEM
A Systems Thinking Approach to Science, Technology, Engineering, and Mathematics
For over two decades, I've had the privilege of introducing students – from incoming freshmen to graduating seniors and even seasoned colleagues – to a particular way of understanding the interconnected world of Science, Technology, Engineering, and Mathematics (STEM). It's a framework born from years of teaching and research, an attempt to move beyond viewing STEM as a mere collection of disciplines and instead see it as an integrated, dynamic system. This perspective, rooted deeply in Systems Thinking, not only helps define what STEM is but also illuminates how its components interact and drive innovation.
My goal here, much like in my introductory presentations, is to offer a conceptual map – a way to compartmentalize and then reintegrate these vast fields. Each element we'll discuss could (and does) fill entire libraries and university majors; they are "prequels" with immense depth. Our purpose here is to see the forest, to understand the architecture of the "System of STEM."
The Fundamental Unit: The System in a Box
At the heart of understanding any complex endeavor, including STEM, is the concept of a system. In Systems Thinking, we often visualize a system as a bounded entity – a metaphorical "box." This isn't to limit our thinking, but to define its current focus. The crucial insight is that this "box" is not isolated.
Inputs: Things flow into the box. In scientific or technical parlance, these are our inputs.
Outputs: Things flow out of the box. These are our outputs.
Process: Inside the box, a process occurs that transforms the inputs into outputs. "Thinking inside the box" is essential here – understanding this transformative engine.
Environment: Crucially, the box exists within an environment. This isn't just the physical surroundings (though that's part of it – a lab, a field, deep space) but also the broader context – societal needs, economic conditions, existing knowledge. The environment influences the availability and nature of inputs and the relevance and impact of outputs. We must always "think outside the box" by considering this environment.
This simple Input-Process-Output model, situated within an Environment, is a universal starting point.
The Foundational Flows: Energy, Material, and Information (E/M/I)
When we speak of inputs and outputs, what exactly is flowing? Across countless systems, these flows can be categorized into three primary types, which form the "Foundational Trinity" of my Standard Systems Model:
Energy (E): The capacity to do work, the driver of change.
Material (M): Physical substance, the stuff things are made of.
Information (I): Pattern, order, data, signals, knowledge – the blueprint or the message.
Most real-world inputs and outputs involve combinations of these, but recognizing their distinct roles is key.
Mapping STEM onto the System Model
Now, let's place the components of STEM within this systems framework:
SCIENCE as INPUT:
In the "System of STEM," Science serves as the primary input. Science, with its long history and rigorous methodologies (the scientific method itself being a process-rich prequel), is the endeavor that discovers the fundamental "parts" of the universe – the laws of nature, the properties of matter and energy, the catalog of what exists and how it behaves. It provides the raw materials – the knowledge, the phenomena, the characterized components – that fuel the rest of the STEM engine.
MATHEMATICS and ENGINEERING as the PROCESS:
Inside our STEM "box," the process of creation, innovation, and problem-solving is driven by Mathematics and Engineering.
Mathematics provides the language, the logic, the predictive models, and the quantitative tools necessary to understand the scientific inputs and to design and analyze the outputs. It's the intellectual toolkit for the "doing."
Engineering is the application of scientific and mathematical principles to design, build, and implement solutions. The "-eering" suffix itself implies doing, making, constructing. This is where the "machine is turned on," where inputs are actively transformed.
TECHNOLOGY as OUTPUT:
The primary output of this STEM process is Technology. In its broadest sense, technology encompasses the tools, techniques, systems, organizations, and methods created to solve problems or achieve practical goals. A new alloy, a software algorithm, a medical device, a manufacturing process – these are all technological outputs.
The Crucial FEEDBACK LOOP:
This system is not linear; it's dynamic and cyclical. The Technology created as an output feeds back to become a new input for Science. New tools (e.g., more powerful microscopes, faster computers, novel sensors – all technologies) enable scientists to make new discoveries, observe phenomena at finer resolutions, and gather more precise data. This enriched scientific understanding then fuels further mathematical modeling, new engineering designs, and ultimately, even more advanced technology. This feedback loop is the engine of accelerating innovation.
The Roles within the System: Specialists and Systems Thinkers
Within this "System of STEM," individuals often specialize. Some are primarily Scientists, focused on discovery and understanding fundamental principles. Others are Technologists, expert in deploying, integrating, and managing existing and emerging tools. Many are Engineers, designing and building new solutions, or Mathematicians, developing the formal frameworks.
However, there's also a crucial role for the STEM Professional or Systems Specialist – individuals who, while perhaps having deeper expertise in one area, understand the entire interconnected process. They see how science informs engineering, how mathematics enables technology, and how new technologies drive further scientific discovery. They are the "Jacks of all trades" in this context, but critically, "masters of the system itself," capable of integrating the pieces and leading the overall innovative effort.
The Measurement System: A Core STEM Process
A significant portion of my work, both in research and teaching (particularly in Instrumental Analysis - CHEM 421), involves understanding and designing Measurement Systems. This, too, is a system that fits our model perfectly and illustrates the E/M/I flows:
Environment: As always, the starting point. Where are we measuring? Inside a coffee cup? A jet engine? A living cell? The International Space Station? Mars? The Environment dictates everything that follows.
Data: The specific property we wish to measure, existing within the sample in its environment (e.g., the temperature of the coffee, the concentration of a pollutant in water, the pressure inside an engine).
Transducer (Sensor): This is the critical interface. The transducer interacts with the sample/Data and converts that property into a different form, typically an electrical signal. It allows the "Data to cross the barrier of the box." This conversion is often driven by an input of Energy (e.g., electrical energy to power the sensor, light energy in spectroscopy). Examples abound:
Sound: Microphone (mechanical vibration to electrical signal).
Temperature: Thermometer (thermal energy causing expansion of a material, or change in resistance).
Light: Photoresistor (light energy changing resistance).
Force: Strain gauge (mechanical force changing resistance).
Acceleration: Accelerometer (inertial forces generating a signal). The transducer's output is Information – a signal (voltage, current, frequency) that represents the Data.
Information: This signal, now "outside the box" of the original sample, flows to the next stage. It might be the rise of alcohol in a thermometer, a voltage from a pH probe, or a digital data stream from a camera.
Model: The Information from the transducer is often not directly the "Knowledge" we seek. We need a Model to convert this raw signal into a meaningful, calibrated value. For a simple thermometer, the model is literally the calibrated scale printed on its side. For more complex instruments, the model is often a computer program – software implementing mathematical algorithms, calibration curves, and data processing logic.
Knowledge: The output of the model is the Knowledge – the actual value of the property we wanted to measure (e.g., "72 degrees Fahrenheit," "pH 7.4," "10 ppm caffeine"). This knowledge is then disseminated, used for decision-making, or fed back into other systems.
The "Ingredients" for Building Measurement Systems: Hardware and Software
Creating these measurement systems in a modern lab, especially when teaching students to innovate, involves tangible "ingredients":
Hardware:
Electronics: This forms the core of most transducers and signal conditioning. While professionals use high-grade components, accessible tools like Arduino microcontrollers provide an excellent platform for students to learn basic circuit design, sensor interfacing, and data acquisition.
Enclosures/Physical Structures: Instruments need to be housed. 3D Design and Printing offer a rapid and flexible way to create custom enclosures, mounts, and mechanical parts for experimental setups, moving from digital design to physical object.
Software:
This is where the "Model" primarily lives. While many programming languages can be used (C/C++ for Arduino, Python for Raspberry Pi), in my laboratory and teaching, LabVIEW (Laboratory Virtual Instrument Engineering Workbench) has been a cornerstone for decades. LabVIEW's graphical programming environment allows users to design "virtual instruments" (VIs) that control hardware, acquire data, perform complex analysis, and display results. Its dataflow paradigm and modular structure make it exceptionally well-suited for designing and visualizing the complex signal pathways and processing steps inherent in measurement systems. Companies like SpaceX utilize LabVIEW for sophisticated instrument integration and testing, demonstrating its real-world power.
Diverse Environments, Unified Principles
The beauty of this systems approach to STEM and measurement is its universality. The specific transducers, models, and technologies will change drastically depending on the Environment, but the fundamental principles of Input-Process-Output, E/M/I flow, and the systematic conversion of Data to Knowledge remain constant. Whether we are:
In a clean, controlled research laboratory.
Analyzing airflow over a model in a wind tunnel using pressure-sensitive paint.
Studying cells under a microscope in biomedical research.
Optimizing processes on an automotive manufacturing plant floor.
Deploying autonomous sensor platforms underwater, like the Manta Ray UUV, perhaps as a precursor to colonizing the ocean floor (our "Atlantis").
Sending rovers packed with instruments to the surface of Mars to analyze its geology and atmosphere.
In every case, STEM professionals are applying these core systems principles to design tools and processes that gather information about their specific environment and turn it into actionable knowledge.
Conclusion: STEM as an Integrated, Evolving System for Agency
Viewing STEM through this systems lens – as an integrated process of using Science (input) with Mathematics and Engineering (process) to produce Technology (output), which then feeds back to enhance Science – provides a powerful, holistic understanding. It moves beyond seeing these as separate subjects and reveals their dynamic interplay. Similarly, understanding measurement as a system, driven by energy to convert environmental data via transducers and models into knowledge, equips students with a versatile analytical framework.
This approach, grounded in first principles and emphasizing the interconnectedness of Energy, Material, and Information, is fundamental to "Education as Agency." It aims to cultivate not just skilled technicians trained for specific tasks, but adaptable, systems-thinking professionals capable of understanding, innovating, and solving complex problems across diverse and evolving environments. They learn not just what to think, but how to think systematically about the world around them and the tools we use to understand and shape it.
Attribution: This article was developed through conversation with my Google Gemini Assistant (Model: Gemini Pro).