Computational Thinking through Classroom Assessment

“What did you find challenging about today’s lesson?”
“How do you think you did on this test?”
“What was the toughest part of planning for this project?”

More than idle conversation starters, these questions share one thing in common: they promote metacognition, or thinking about thinking. They encourage students to practice recall and remember previous lesson content, as well as assess how they approach new problems.

Reflection is invaluable, as it provides a way for students to take control of their learning. Metacognition is an important skill even outside the classroom. Just as important, however, is incorporating other computational thinking skill sets into assessment.

Computational Thinking and the Cognitive Toolbox

Computational thinking is a problem-solving framework that focuses on the ways that problems can be assessed, approached and solved through computational means. For example, a problem can be decomposed, or broken down into smaller, more easily defined components. A problem can be analyzed for patterns, providing new insights on possible solutions.

While computational thinking has a strong connection with computer science, it’s applicable to many fields. Artists can analyze works or use abstraction to create new art. Cooks can use algorithmic thinking to design new recipes.

When computational thinking is used in the classroom, it helps students develop problem-solving skills. Each computational thinking task becomes a tool stored away for later use, a part of a larger cognitive toolkit. For this reason, adding computational thinking to classroom activities such as assessments provides opportunities for students to grow and learn.

Formative Versus Summative

When it comes to classroom assessment, people usually reference one of two assessment types: “formative” or “summative.” The first type, formative, refers to an informal assessment that looks at student progress. It monitors learning. The second, summative, encapsulates students’ learning into a final metric, typically a final grade. It sums up learning.

Formative assessment can act as a gauge to determine how well students understand new material. If students do poorly on formative assessments, you can use this feedback to hold off on sharing new information until the proper scaffolding is in place. Summative assessment, on the other hand, indicates mastery. It shows that students have learned a task enough to meet a specific level or criterion.

Formative assessments can be simple (a quick exit slip, for example) or complex (grouping students for a think-pair-share activity). They can be ungraded, and are often low stakes. Formative assessment pairs well with the types of questions that promote computational thinking, such as those that involve reflection. It also is a great way to include evidence-based teaching, such as retrieval practice.

Summative assessments often involve more fact-based, objective learning goals. These may include tests and quizzes. Because summative assessments usually attach to metrics, benchmarks, rubrics or standards, these types of assessments may be less reflective. They can still include computational thinking tasks, however, such as decomposition, abstraction and more.

Computational Thinking and Assessments

Computational thinking doesn’t have a singular focus: it’s comprised of small tasks, or practices, that serve to engage with a certain type of problem-solving skill. All tasks offer a different way of approaching a topic. For example, having students decompose something indicates an understanding of the object as a whole, just as abstracting an idea suggests an awareness of key points.

Below you’ll find a few commonly recognized skills, coupled with the types of questions you might add to assessments, formative or summative.

Decomposition/Abstraction

Decomposition involves breaking down problems, ideas or objects into smaller parts, while abstraction involves simplifying ideas and objects, or categorizing them into a specific group. A realistic painting of flowers can abstract into spots of color representing the flowers’ hues, or the flower itself can be broken down into parts such as stamen, pistol and stem.

Questions to get students to engage in decomposition and abstraction include the following:

• Can you break this down further?
• How do you simplify this?
• What category does this belong to?
• What’s the main idea?
• How would you group these?

Students can also use abstraction by changing the way that data is presented. Data visualizations, for example, are visual ways of accessing data. Particularly for number-heavy data, where tables can be hard to navigate, an abstracted visualization (such as a graph or chart) can make information easier to parse.

• Draw a diagram.
• Make a graph.
• Create a timeline.
• Fill in this image/map.

Some programs, such as Mathematica, include tools that allow students to create adjustable visualizations. For example, a student could code a slider tool to show how variables affect an object’s appearance. Even as a paper-based activity, however, having students display information in a new format encourages them to consider data holistically.

Metacognition

Metacognition, or “thinking about thinking,” involves using self-reflection to assess learning efficacy. Because it’s a personal process, there aren’t necessarily any “right” answers to reflective questions. That said, reflective questions provide insights for students in how they approach problems as well as their blind spots and their strengths.

In addition to the reflective questions at the top of this post, here are a few more questions involving metacognition:

• What did you learn today?
• What was easy for you to learn?
• How did X concept help you understand Y?
• What other questions do you still have?

Questions involving metacognition can be used to implement evidence-based teaching, as you can guide the way students approach their own self-reflection, even beyond the assessments. When they see the value of considering how they learn, they may reflect on their learning outside the classroom. In a world where learning new skills quickly is vital, this sort of self-awareness is paramount.

Pattern Recognition/Pattern Matching

Pattern recognition, or pattern matching, is a way of assessing the way that objects in a group or series relate to one another. A pattern arises when members can be added to the group in a regular way. For example, if you look at the pattern “-.-.-.”, you can guess whether a dot or a dash will be added next.

In computer programming, patterns help to simplify code. They can be folded into algorithms or used to make guesses in machine learning. With that in mind, here are some questions to help get students used to thinking of things as groups and patterns:

• Do you see a pattern?
• What comes next?
• What do these things have in common?
• Which one is the odd one out?
• How would you group these?

In English Language Arts (ELA) classes, pattern recognition loosely ties into units on figurative language. Analogies, symbols, metaphors and similes: many of these rhetorical devices are just ways of making connections between ideas, connecting one object to another by their shared qualities. Outside of the ELA classroom, having students compare and contrast can help them recognize initial groupings. This isn’t full pattern recognition — there’s no sense of asking what comes next — but it still helps to provide a foundation.

Algorithmic Thinking

Where pattern recognition asks students to figure out how objects are added to a group, algorithmic thinking expands this idea by asking them to understand and create rules for actions in general. For example, a recipe involves algorithmic thinking in that ingredients must be added in a specific order, with each numbered task acting as a trigger for the next. Technical writing, such as manuals, also relies on instructions.

If your class supports coding, then asking for students to write conditionals or “if-then” statements is a direct way to include algorithmic thinking in assessments. Beyond code, here are a few other ways you might have students explore algorithms:

• Write a lab report.
• Write some instructions for someone on how to repeat what you just did.
• Write a recipe.

If you don’t want students to directly practice technical writing, you can still include questions that hint at it. For example, you might ask “What steps would you use…?” or even “How would you…?” to get students to consider cause and effect.

Itty-Bitty Computational Thinking

Including computational thinking in the classroom doesn’t have to be a daunting task. While the name might suggest high-tech requirements, computational thinking isn’t just coding. It’s a useful, multipurpose framework that aligns well with the computation-heavy society we live in.

Assessments are a matter of course in the classroom. Since you’ll use them anyway, why not take advantage of the opportunities they provide? Whether through reflection, retrieval or reconfiguration, students will be sure to look at their learning in a new light.

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!).