A version of this post was originally published on the Tech-Based Teaching blog as “Computational Lesson-Planning: Easy Ways to Introduce Computational Thinking into Your Lessons.” Tech-Based Teaching explores the intersections between computational thinking, edtech and learning.

Sometimes a syllabus is set in stone. You’ve got to cover X, Y and Z, and no amount of reworking or shifting assignments around can change that. Other factors can play a role too: limited time, limited resources or even a bit of nervousness at trying something new.

But what if you’d like to introduce some new ideas into your lessons—ideas like digital citizenship or computational thinking? Introducing computational thinking to fields that are not traditionally part of STEM can sometimes be a challenge, so feel free to share this journey with your children’s teachers, friends and colleagues.

Computational thinking is a mindset that is complemented by technology, not necessarily bound by it. In fact, some concepts can be as simple as adding a reflective assessment to the end of your lessons, allowing students a chance to uncover their thought processes and engage in metacognitive thought.

While computational thinking most often relates to coding (unsurprising given its connection to computer science), it’s really a way of looking at problems. This means that computational thinking can be introduced into all sorts of classrooms—not just in STEM classes, but in art and music classes, and even in physical education.

Why Computational Thinking Gets an A+

Like digital citizenship, computational thinking is a useful skill for students to master before they enter the “real world.” Practicing computational thought enables them to pick up new technologies, utilizing them for work and play. Computational thinking is a transferable skill, and it can act as a lens through which students can view problems outside the classroom.

In a way, practicing computational thinking through such tasks as deconstruction and experimentation can help to abolish the fear of failure that stymies a growth mindset. After all, if failure is a necessary component of success, what’s there to fear? Students who feel comfortable with limited information and unknown variables are more prepared to tackle new ideas.

Given that computational thinking can provide real value for your students, how can you add it to your lessons?

Pattern Recognition: Beyond Stripes and Solids

One component of computational thinking is pattern recognition. Pattern recognition can help to determine the build of a system as well as find inefficiencies, perfect for generating an engineering mindset. It can also help to determine the “variables” of a given problem.

One way to practice pattern recognition is to have students look at data and see where they can find repeating data points. The typical thematic analysis assignment found in many English language arts (ELA) classes relates well to the concept of pattern recognition. When looking for symbols in a text, students are searching for repeating patterns.

Going beyond symbols, some digital humanists use computers to analyze punctuation or the overall sentiment of a book or corpus that the human eye might not catch. For example, this teacher has his students perform “distant reading” with Wolfram Mathematica, leading to insights on everything from speeches to rap lyrics:

✕ textRaw=Import["http://www.gutenberg.org/cache/epub/608/pg608.txt"];

✕ StringPosition[textRaw,{ "A SPEECH FOR","End of the Project"}]

✕ areo=StringTake[textRaw,{635,102383}];

✕ Row[WordCloud/@{textRaw,areo}]

If doing such a deep dive isn’t possible, you can still have students be more thoughtful about their theses. Perhaps you can challenge them to look for unusual patterns in their texts. What if every student drew a noun from a hat prior to reading a novel, and they were responsible for noting when that noun appeared?

Take the noun “food,” for example. Primed to notice every incident in which a character eats a meal, a student could begin to see how food is used in a particular novel—as an abstracted symbol, or an incitement of plot or even a tool for characterization.

Build ’Em Up and Break ’Em Down: Deconstruction and Reconstruction

Just as pattern recognition can be a helpful tool in discovering the possible inner workings of a system, deconstruction and reconstruction allow students to demolish and rebuild the systems they discover. Systems can be found in set formulas, interconnected biological processes or even historical structures.

Changing built-in systems and tinkering with “variables” is the basis of coming up with new algorithms for solving problems. Understanding systems is also inherently valuable, even in the humanities—grammar and syntax underpin language, for example, while “soft skills” like communication are wrapped up in social mores.

Going back to the ELA classroom, perhaps looking at a broad overview of a certain genre could help students see the commonalities of that genre’s books. For a fun question, you could ask, “What makes a graphic novel?” This could be a good way to introduce the idea of critical lenses.

You could also use charts or graphs to deconstruct a genre into its core components. If one component changes, is the book still a part of that genre? You could connect this idea to generators and bots, showing how traits and qualities can be remixed into new forms. (YA readers may enjoy this John Green plot generator!)

Students could add new “members” to the systems they uncover. For example, they could pitch a new graphic novel, or brainstorm alien biology, or try their hand at world-building for a fictional country. Deconstructing a math problem using Wolfram|Alpha could lead to insights on the whys and hows of a particular formula.

Failure Means You’re Learning

Part of computational thinking relies on students’ lack of fear in getting an answer “wrong,” particularly during the early exploratory stages of a project. Fear of failure is common, and it persists well into adulthood. These feelings can stand in the way of making real progress, especially in a classroom where peer pressure is paramount.

To a certain extent, the only way to get a classroom to feel like a true learning environment is to make every student feel that failure is okay. Doing so relies on understanding your students as people, as dealing with personality clashes and students’ individual backgrounds is a huge part of classroom management. Still, one way to banish a fear of failure is to consider ways of incorporating small wins into lessons.

In some fields such as writing or art, professionals must fight through rejection on a near-daily basis. To counter that feeling of failure, some people have created games in order to get past their initial knee-jerk reaction of despair. Some writers and artists hold “100 rejections” challenges, aiming to collect rejection letters. Others engage in “rejection therapy,” in which failure is the end goal, not an unfortunate “game over” end state.

Why not gamify failure in the classroom as well? One example in higher ed comes from an anecdote found in the book Art and Fear. In it, a professor divided a pottery class into two groups. While the first group had to submit one pot for a final grade, the second group had to submit a specific poundage of pots. In the end, members of the second group had the highest grades, as they were unbridled by the stress of perfection. They were able to fail over and over.

To alleviate this stress for your students, you could try emphasizing process over perfection. Rather than having students submit a long-form story as a capstone assignment, perhaps they could be graded on the amount of flash fiction they produce. The very process of iterating story after story imparts useful writing skills. In fact, there might be some unconscious pattern recognition as they go along, wherein the students notice their preferred tropes or storytelling techniques.

You could also emphasize reflection in STEM classes. More and more, educators are stressing the value of writing in math class. For a math assignment, students could notate their problem-solving processes through a program like the notebook-based Mathematica. By reflecting on their decisions through inline comments, students will use metacognition, a powerful learning tool.

Experiment, Experiment, Experiment

Similar to reconstruction, experimentation results in new ideas being extrapolated from recognized patterns. Students can create experiments to figure out what makes systems tick—and without a fear of failure, tinkering becomes play.

Obviously in STEM fields, experimentation is not only expected, but celebrated. But even in the humanities, students can intuit how cause and effect works. In music, changing between a minor key and a major key can shift the perceived mood of a piece, at least to Western ears. Color swaps in a piece of art can have emotive effects.

One idea for experimental writing assignments could be to have students create choose-your-own-adventure stories with branching paths. This type of writing emphasizes the “if this, then that” thought process that’s so vital to creating algorithms or step-by-step instructions. There are several game development–based tools available for creating these stories, but even pen and paper can work.

Exploring Further Resources

Some of these ideas might seem a bit simple. And that’s because they are! Computational thinking doesn’t have to be complicated to be useful.

Even with the rote standby of analyzing a text for themes and characters, you can cement the idea of recognizing patterns or breaking down a system. As you become more comfortable with computational thinking, and if the IT resources are available, you can then begin to introduce technology into your lessons. For example, using the Wolfram Language to dig deep into problems using code could vastly aid in analysis and experimentation.

As more people recognize the value of computational thinking, educators are publishing their own lesson plans and ideas online. This online book, for example, offers a treasure-trove of ideas for incorporating computational thinking into lessons by subject. Examples range from robotics to data analysis and more.

Another useful resource is Computational Thinking Initiatives (CTI), a nonprofit group devoted to sharing tools and resources for educators looking to introduce computational thinking into their classrooms. They share pre-developed lesson plans built using the Wolfram Language, offer an AI League and coding challenges, and help engage in community efforts to spread the word about computational thinking. If you want more personalized advice, you can reach out to them with questions.

If you’re interested in exploring the Wolfram Language, you can check out An Elementary Introduction to the Wolfram Language. Otherwise, take a look around this blog under the Education tag to see how other educators are using Wolfram Research tools in their lessons.