Defining and Linking CT to Weatherblur

Computational Thinking has become a new catch-phrase in education over the past few years and has grown in popularity due to its inclusion in the Next Generation Science Standards. Like so many terms in education circles, it can be a bit of a mystery. Despite how it sounds, however, computational thinking (CT) is not that complicated. 

CT,  popularized by Carnegie Mellon professor, Jeannette Wing in a 2006 article, is defined as the “thought processes involved in formulating problems and their solutions so that the solutions are represented in a form that can be effectively carried out by an information processing agent (human or machine, or more generally, by a combination of both).”  

Okay, it’s a little complicated.

In a nutshell – CT can be broken down into four sets of practices:

  1. Decomposition
  2. Pattern Recognition
  3. Abstraction
  4. Algorithmic Design.

The CSTA has produced a very useful chart that explains the vocabulary and progression of CT,  Pre-k-12. You will notice, this chart includes more than our four, big themes for understanding CT. Pattern recognition, for example, is broken down into Data Collection, Data Analysis, and Data Representation. Different organizations (CSTA, ISTE, etc) have approached this emerging field differently. Rather than focus on semantics, let’s get to the heart of the matter

What does CT look like in your classroom?!

Decomposition: Breaking big problems into smaller pieces. 

In grades 3-5, an example might be:

Develop a plan to make the school “green.” Separate strategies such as recycling paper and cans, reducing use of electricity, and composting food waste.

In grades 6-8, an example might be:

In planning the publication of a monthly newsletter, identify roles, responsibilities, timeline, and resources needed to complete the project.

Pattern Recognition: Looking for and learning from repeating sequences. Where do we find these sequences? In DATA. Pattern recognition includes collecting data, analyzing data, and visually representing data in a useful way. In other words – nearly every step of carrying out Weatherblur  investigations will help strengthen this skill. 

In grades 3-5, an example might be:

Categorize strong and weak examples of writing samples to develop a rubric.

In grades 6-8, an example might be:

Produce and evaluate charts from data generated by a digital probe and describe trends, patterns, variations, and/or outliers represented in the chart.

Abstraction: In one sense, abstraction is making one solution work for multiple problems. In a simpler way, abstraction is the process of sifting through information to find the key points. It’s about removing details from a solution so that it can work for many problems

In grades 3-5, an example might be:

Hear a story, reflect on main items, and determine an appropriate title.

In grades 6-8, an example might be:

After studying a period in history, identify symbols, themes, events, key people, and values that are most representative of the time period (e.g., coat of arms).

Algorithmic Design: Step by step instructions on how to get things done (not to be confused with a “program” that is those actual steps translated into a language (coding) that a computing device can understand).

In grades 3-5, an example might be:

Design a board game and write instructions to play. Test instructions on peers trying to play the game. Refine instructions with feedback from peers who played the game.

In grades 6-8, an example might be:

Program a robot to find its way out of a maze such that given any maze, the robot could exit successfully within a specified time period.

Now, let’s take a look at a day in the life of a weatherblur investigation and see if we can find where the buckets of computational thinking (decomposition, pattern recognition, abstraction, and algorithmic design) might be.

 

Here is a lesson sequence you can use to introduce this in your classroom

The key here is explicitly using the vocabulary of CT to call out things that you likely are already doing in your lessons.

  1. Introduce a large, vague or generic problem. It can be connected to something you are already doing in your classroom or just anything you think is engaging to your students. Possible ideas might include: how to draw a cat, build a bicycle, host a party, start a newspaper..

Example: You want to bake a cake

  1. Ask your students to break down the problem into a set of smaller tasks. These can be at any level of detail. Don’t worry about too much direction here! In person you can use post-its or notecards. For a virtual activity, use Google Jam Board (jamboard.google.com). Make the connection to DECOMPOSITION – breaking down a problem into smaller pieces.

Example: look at recipes, buy sugar or other ingredients, turn on the oven, measure vanilla, lick the bowl, ask about food allergies, clean the measuring cups

  1. Have your students take the smaller tasks and begin grouping them into larger categories. In person, they can move post-its around to form groups. Virtually, you can take feedback from them and make some of these groupings on the jamboard or allow students to move things around themselves. Make the connection to PATTERN RECOGNITION – looking for common themes and repeating sequences.

Example: Categories might include: measuring ingredients, baking equipment, following recipe instructions, types of cake

  1. Once the small tasks are grouped, look at them and try to make decisions about which problems are detailed and which ones might be more generic. Work with your students to write a few small summary tasks. Make the connection to ABSTRACTION – looking at the patterns to work out what is important and what can be ignored. Figure out how to represent a problem in general terms.

Example: For baking equipment – Details  might include whether it is chocolate or vanilla, whether it is baked in a round pan or a square pan. What is necessary is that you must have some sort of pan. 

  1. Lastly, write a series of generic steps for how to solve your problem. Keep these simple enough so that you have a general approach to the problem that can be easily applied to multiple situations. Make the connection to ALGORITHMIC DESIGN – creating a list of steps that you can use to finish a task.

Example: Write an algorithm for baking a cake. Keep in mind that this is a general approach to baking a cake. It might include:

  1. Read a recipe
  2. Purchase the proper ingredients
  3. Measure and mix the wet ingredients
  4. Measure and mix the dry ingredients
  5. Mix the wet and dry ingredients together
  6. Pour the batter into the pan
  7. Bake at a certain temperature for a certain amount of time

Now, let’s take a look at a day in the life of a weatherblur investigation and see if we can find where the buckets of computational thinking (decomposition, pattern recognition, abstraction, and algorithmic design) might be.

A Day in the Computational Thinking Through Citizen Science Project

The sounds of forks clattering on plates and the sweet smell of syrup wafted out of Ms. Smith’s 7th grade math classroom. Today students were enjoying pancakes smothered in maple syrup that had been collected by the 6th grade science class. The 6th graders had been involved with a citizen science initiative called WeatherBlur. Throughout the school year, they had walked through an “I Wonder” process, designed questions around where maple syrup came from, developed data collection protocols for tracking air temperature, sap volume, and other aspects of processing the sap, boiled sap into syrup, and tracked production in every step. They had studied the role of temperature in sap volume and had some hypotheses about why this had been a poor year for sap as compared to previous years. They had leveraged the WB citizen science online platform to access, compare and contrast data from other students across the state. It had been a rich learning environment, but they wanted to see the big picture – how might climate change affect the maple syrup industry in Maine? The 6th grade needed help answering this question so the 7th grade math class stepped in to help tackle this research question.  

The 7th grade loaded data sets that described air temperature over time for regions known for their maple syrup (Maine and Minnesota) into Tuva (see below). Tuva helped them visualize the questions they were asking. One student leader suggested they look at average daily temperatures over a ten year period for those regions versus sap production. Using Tuva, Ms. Smith guides the students through the process of learning to ask the right questions, breaking those questions down into smaller pieces, manipulating the data in a way that leads to analysis and then visualizing the results. They add components of the local data they had collected throughout the investigation into Tuva representations of data, revise which data is used to tell the story and answer questions, and work through different representation formats and how they influence the message the data tells.   Students shared their data representations with other WB classes in Maine and Mississippi to get feedback on their work.  Ms. Smith was prepared to learn alongside them and guide their process because of the community she had built and the support they had been given.