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Computational Thinking and Data Analysis Go Hand-in-Hand

By Nick Pinder
March 28, 2022
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Data influences all decisions in life, now more than ever.

This isn’t necessarily a new phenomenon, but the ubiquity of data-driven solutions is. Identifying and collecting sources of data, recognizing patterns within it, extrapolating a generalization and communicating all of this to a computer or human is complex. It’s also the core activity of computational thinking (CT).

When I set out to create a lesson plan that integrated CT into a Spanish lesson, I came up with the computational question, “How can we identify and categorize poems?” The way I created this computational question was by focusing on data. “What was the main activity?” is what I centered my thoughts on.

In this article, I’m going to share my experience with creating a lesson where students found data in poems (pattern recognition), generalized their findings (abstraction) and how they got there.

Tip 1: Think about what can be analyzed?

Every subject has data sources. ELA has literature and poems; science has surveys and experiments; social studies has laws and policies; physical education has sports and game statistics. Data is everywhere. 

When it comes to Spanish, I thought about how we analyzed poetry in school. We use the verb “analyze” for data and for poetry, so can’t poetry count as data? Surely the characteristics of poetry are considered data.  

Anything can be a data source. It takes some harmonization with your computational problem. It’s the guiding start of the CT process. It lays out the end goal, something to not only strive for but also to keep in mind along the way.

The relationship between data and a computational question is like the chicken-or-the-egg question. Sometimes you have an awesome question but you need to find data:

  • How can we categorize and identify poems?
  • Where is the best place to put oyster castles in a watershed?

Other times you have an interesting data set that you need to build a question around:

  • We’ve read Macbeth, now what do we do?
  • We know how much food our school wastes, now what?

A question needs data to move forward, and data needs a question to be interpreted. 

Tip 2: Determine age-appropriate activities and adjust from there.

Without any actual students to teach my lesson to, I had to make a lot of assumptions. I assumed students already could identify rhyming words, count syllables and understand the poems, in English and Spanish. These skills are in line with higher-level high school ability, but with an actual classroom, I’d be able to pinpoint the abilities of my students’ using assessments and observation.

In an effort to avoid overwhelming my hypothetical students, I chose a poem called a  décima which has strict guidelines. Using a structured poem like a décima ensures my students aren’t struggling with any wild cards. The data isn’t overly complicated for a higher-ability Spanish learner. 

Tip 3: Give opportunities for practice.

Analyzing data is a skill, and like any skill, it takes some practice to get it right. This is where and why brainstorming came into play in my lesson

I had students brainstorm elements of a poem. Their ideas became an anchor chart, and students would practice analyzing a sample poem while referencing this chart. The anchor chart reminds students to look for rhyming words, syllables, lines, etc. 

This system of brainstorming and anchor charts is reinforcing two elements of CT, decomposition and pattern recognition

Tip 4: Help students sift through data.

Being able to recognize patterns is fantastic, but some patterns are more relevant than others. What determines this relevance is the computational question.

For example, students might notice the following in their poems. 

  • In Poem A, there are 10 syllables per line, and the author’s last name starts with E. 
  • In Poem B, there are 10 syllables per line, and the author’s last name starts with O. 
  • In Poem C, there are 10 syllables per line, and the author’s last name starts with A. 

Some patterns are more relevant than others in the context of categorizing and identifying poems. While all authors’ last names start with a vowel, this characteristic is not relevant to identifying the poetic style.

Having 10 syllables per line seems pretty interesting. Perhaps the structure of the poem is related to the number of syllables per line?

This process is an exercise in finding the CT elements of pattern recognition and abstraction. Finding a pattern, and then contextualizing it with the computational question.

This is where good facilitation comes into play. Asking student groups to share their thoughts provides the teacher with an opportunity to support them. After analyzing poems, we as a class would have a discussion, echoing the first brainstorming session, helping sift through the relevant ideas. 

The relevant ideas students find will be the basis for designing an algorithm to solve their computational problems. 

In this article, I outlined the twin CT elements: pattern recognition and abstraction. Pattern recognition is looking for patterns within a data source. Abstraction is taking identified data patterns and determining which ones are relevant to the computational problem.

These two CT elements are the core activities of CT. Developing these skills in students is one of the reasons why CT should be integrated into classrooms.

ISTE U - Computational Thinking edtech PD

Nick Pinder is a project manager of computational thinking and higher education projects at ISTE. Nick is interested in the promotion of computational thinking and its intersection with language instruction specifically and the humanities in general.