Computational Thinking at the Midpoint
The midpoint is a tricky place to be. Sometimes it’s an indicator of progress, something to inspire you to keep moving forward. Sometimes it’s a reminder that there’s still so much further to go.
Computational thinking can help you — and your students — find your footing as you work toward a goal, whether it’s the end of a project or the end of the semester. By using such skills as decomposition, abstraction and algorithmic thinking, you can make the midpoint less frustrating and build a pathway to keep going.
Abstraction for the Bigger Picture
Abstraction takes the minutiae of things and puts it into larger focus. With coding, abstraction helps to make small details become codified objects. It creates stand-ins for big-picture ideas. For everyday life, abstraction makes sure that less important details don’t derail a project. Abstraction also helps to clarify how smaller parts contribute to a whole.
At a midpoint, it’s difficult to switch grading schemas, but to abstract things even further, look at single-point rubrics and ungrading. Both of those grading styles focus on grades as a general concept in broad strokes. If your assignments aren’t yet set in stone, could you abstract your grading?
Abstraction, in some ways, helps to create a clearer image of what’s expected. What are you working toward, and how do these pieces fit into the bigger picture? Seeing coursework as a whole — as with a long-term view on a syllabus — can help everyone find their bearings in the middle of a busy academic year.
Especially at the midpoint of a semester, where content becomes heavily scaffolded, confirming general understanding is helpful. Feeling shaky on background knowledge may be making students feel less confident. Using retrieval practice and adding in more broad-strokes questions will keep abstraction in mind even while focusing on new materials.
Decomposition for Detailed Focus
If abstraction is zooming out, then decomposition is zooming in. Decomposition breaks things down into smaller parts. Things can be broken down by category, by where they stand in a sequence and so on.
In coding, decomposition can help you describe classes or build functions. In everyday life, decomposition thrives in the to-do list. Even if you don’t yet know the best sequence of actions, those actions can feel more manageable, being smaller and with shorter timeframes for completion.
Say a student is feeling overwhelmed by a final project for the course. Rather than getting stuck on the big picture — a nebulous project with no defined start and end — a student can break down the assignment into tasks. First there’s research, then outlining (which is itself a form of decomposition), then writing. Those tasks can then be broken down even further: research becomes “look up articles on topic X” or “brainstorm keywords.”
Decomposition is useful throughout the entire academic year, be it for figuring out how to tackle a paper or managing time. Midway through a semester, those tasks begin to take on vital importance. With time being limited, students might begin to stress. Having a to-do list with specific tasks can be comforting, and for students who struggle with executive function, a very clear, detailed to-do list can help mitigate some of the strain of completing said tasks.
Algorithmic Thinking for Figuring Out What’s Next
With a big-picture idea of expectations and a decomposed look at what needs to be done for any specific assignment, what else is there?
Algorithmic thinking is looking at how tasks are sequenced. It’s creating directions or recipes and letting them run through steps. In coding, algorithms are everywhere. They are the Excel macros that go through formulas in a specific order. They are the behind-the-scenes code that runs TikTok’s For You page or Google’s search results.
For students, algorithmic thinking can help make tasks more efficient. To finish this project, steps X, Y and Z need to be completed. But can X be done before Y? What about Z?
Using algorithmic thinking can help keep to-do lists manageable. Ideally, tasks will build off each other, but algorithmic thinking doesn’t have to be so linear. Students may want to chunk similar tasks together (sorting them through pattern matching or shared attributes). Students might want to “snowball” their tasks, doing the shortest ones first in order to build momentum.
Either way, having students consider what to do next can make the midpoint less stressful, as even the tiniest step forward is progress.
While each part of computational thinking can be useful, some parts will be more helpful than others depending on the person or the issue at hand. If a student is paralyzed by course expectations, having them look at things from a big-picture view and abstracting assignment types can help them feel more in control. Likewise, for a student anxious about things as a whole, decomposition can help them to define next steps more clearly. Everyone has their individual struggles.
Midpoint stress is compounded by outside factors as well. You can’t computationally think your way out of burnout! That said, by keeping yourself focused on small tasks (so-called “small wins”), you can keep moving forward, little by little.
About the blogger:
Jesika Brooks
Jesika Brooks is an editor and bookworm with a Master of Library and Information Science degree. She works in the field of higher education as an educational technology librarian, assisting with everything from setting up Learning Management Systems to teaching students how to use edtech tools. A lifelong learner herself, she has always been fascinated by the intersection of education and technology. She edits the Tech-Based Teaching blog (and always wants to hear from new voices!).
