Education for the Anthropocene

About two weeks ago, I found myself in a world of unfamiliar acronyms and jargon.  CM and PLM and CAD were thrown around casually and I had difficulty decoding what was going on, as it’s not jargon I usually work with.  During icebreakers, I found myself chatting with aircraft engineers and chemists working industrial assembly lines, and it wasn’t easy for me to understand exactly what they did or, for that matter, explaining what I did.  When I described my education work, a quizzical look crossed their faces.  “Why are you at this conference?”  I wasn’t really sure.  It was the ConX19 conference by the Institute for Process Excellence.  I had been recruited a few months back to speak about Habitable Worlds, but I didn’t quite understand how I fit in with an industry group.  But it started to make sense as the conference progressed.  Industry is transitioning to Industry 4.0:  digitalization (with Industry 1.0 being steam power, Industry 2.0 being electrification, and Industry 3.0 being automation).  Digitalization requires working with and understanding complex systems, often powered or analyzed by analytics, machine learning, and artificial intelligence.  But our educational pathways are not preparing students with the skillsets necessary to understand and work with complex systems.  My purpose, it turned out, was to explain both how I taught systems thinking through my astrobiology course Habitable Worlds (to demonstrate better curricula) and the system processes I used to develop the course (to demonstrate better processes for teachers and developers).

Astrobiology is a naturally cross-disciplinary systems science.  Unlike many basic sciences, where principles and relationships can be described with equations or simple cause-and-effect relationships, astrobiology teaching and research requires thinking about multiple cause-and-effect relationships at once, as well as their feedbacks and how they can amplify or cancel each other out.  This makes it an especially challenging topic to teach, especially when structuring curricula.  Take climate or paleoclimate, for example.  Climate integrates topics such as the physics of molecules, light-matter interactions, emission of light by stars and planets, gas chemistry, water-rock chemistry, atmospheric circulation, ocean circulation, density gradients, temperature gradients, and water phase changes (for starters).  How do you teach all that?  Do you start with what we observe in the world and work back to basic principles?  If so, in which order do you tackle the basics?  Some basics require two or more prerequisites!  Or do you start with the basics?  If you do that, how do you keep students interested in all these disconnected and often abstract topics until you show them how they are integrated into the big picture?

There are many narrative structures you can use when teaching complex systems like this.  The digital classroom allows new forms of scaffolding that aren’t possible in a physical classroom.  Top-down or bottom-up?  Using technology, you can do both.  A properly designed digital experience can allow students to proceed from first principles to the big picture, or dissect the big picture into its individual components, depending on which they prefer.  Additionally, well-designed digital approaches, much like a video game, allow for evaluation of systems thinking.  In a videogame, this kind of evaluation is described as a “set piece” or “boss fight”.  It’s usually a big, dramatic event in the narrative of the game and requires scaffolding of multiple skills that have been taught and practiced piecemeal throughout the game.  Digital educational experiences can do something similar.  In Habitable Worlds, it was the final project, where students were given 500 stars with observational data and given six weeks to analyze and interpret them to find a habitable world.  The skills had already been taught during the rest of the course.  The project, then, was evaluating when students could assemble all the pieces into a strategy that would help them solve the puzzle.  Success or failure in the project gives a good indication of students’ systems thinking capabilities in a way that a multiple-choice question exam can’t.

This is quite the change from a physical classroom or even a basic digital classroom built in a learning management system like Blackboard, Canvas, or Moodle.  Often the evaluations in these environments boil down to multiple-choice questions, true/false, or fill in the blanks to deal with large numbers of students or the design limits imposed by learning management systems.  These are decent approaches for evaluating content knowledge, but not particularly useful for evaluating higher-order thinking skills.  To evaluate those skills, we often use written activities or oral exams, but these don’t scale particularly well.  More game-like projects or challenges and curricula structured around them can be a better method of evaluating higher-level thinking.  They are more complicated to build and take more time, but they allow an instructor to teach and evaluate how students understand and work with systems, rather than specific concepts.  This applies well not just to astrobiology but also to preparing students for the Anthropocene, a proposed geologic epoch where Earth system processes are dominated by humans.  As we take control of complex systems on a global scale, whether through climate manipulation or Industry 4.0, we need to prepare upcoming generations with better systems thinking abilities so that they can act as more responsible planetary stewards than we have been so far.

Adapting course material for the digital realm, which is a common task for many of us as universities embrace the online model, requires systems thinking as well.  In the typical resource- and time-stressed educational environment, it is difficult to think of the course itself as a system.  It can become a rapid-fire sequence of lectures, homework assignments, grading, and exams.  To cope, we may recycle other people’s slides and activities, construct linear storylines for content that is non-linear, and throw together multiple-choice questions for easier grading.  However, if we teach the same course multiple times over many years, we adopt an iterative process, during which we improve assignments that didn’t work well, or change the order in which we teach concepts to tell a better story.  Usually when we do this, we’re working with limited data.  Anecdotes from students.  Complaints on student reviews.  Poor exam results.  Still, it’s enough to allow us to make somewhat informed changes.  But as we start making these changes, we quickly see that there are non-linear dependencies.  A new approach to a topic may necessitate new lectures, which then impact subsequent lectures.  This may then necessitate changes to exam questions, which necessitate changes in homework assignments to prepare students for the new exam questions.  Mature classes operate as a well-functioning system, a complex interaction between teacher, student, and content that evolves over time.  A systems approach is the only way to think about a course, really, because as students change over time, their interaction with the teacher and content will change as well.  What worked five years ago may no longer be as effective.  Good classes remain robust and adaptable to changing scholarly environments.

“Courses as a complex system” is an important point to remember when moving courses into the digital realm.  The approaches you use in the classroom to adapt content and keep it relevant over the years also apply to digital courses.  Towards that end, you want to find tools and platforms that allow quick and easy modification of your content.  Additionally, because digital systems can capture a lot more data, you’ll want to use tools and platforms that gives you more insight into what students are doing, which allows for improved improvements to the course.  But it’s not just the technology you use.  Your build process needs to recognize that you are interacting with a complex system with non-linear dependencies.  A linear process, where first you write the questions, then program simulators, then record the videos, then make the graphics may not necessarily work, particularly if the approach doesn’t allow for iteration, addition, or whole-scale deletion of material that isn’t coming together or that just plain isn’t working for students.  This concept, of a course as a system, can often be frustrating for developers and engineers, who expect a linear process and for all the unknowns to be understood and accounted for prior to beginning any building at all.  But as teachers, we recognize that some of these unknowns can’t be identified until after students have a go at it.  For all the planning in the world, a handful of students will always do something unexpected.  Sometimes it’s more than a handful.  Sometimes it’s all of them.  If you are working with developers and engineers, you need to prepare them for multiple iterations as new observations come to light that necessitate changes.  An instructor or team that understands that a digital course is a complex system will, over time, optimize it into a phenomenal experience for students.  Instructors and teams that try to enforce linearity on the development process end up with non-optimized experiences with no mechanism for improvement, or even mid-stream error correction.  The shelf life of these digital experiences will be brief.

Systems can be tricky.  They aren’t easy to teach and they aren’t easy to manage, especially if they contain unknowns.  But if we want to succeed as planetary stewards, we all need to become systems engineers, or at least learn how to think like them.  We can start by using digital tools and platforms to better teach systems thinking in the classroom that can prepare our students for the Anthropocene.  And using these tools and platforms successfully requires the realization that a course is a complex system itself and processes that we put in place, either as individuals or teams, need to respect, work with, and become comfortable a system’s inherent complexity.

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