Tag Archives: Computational Thinking

Need inspiration for some fun, STEM projects to do at home? Check out the new YouTube series “Make with me”! Join the Modular Robotics staff as we try out fun, challenging, robot activities that we can do around the house, like building this Hand Washing Timer robot: Of course STEM at Home doesn’t need to be involved projects! Kids can learn a lot about robotics just by building their own Cubelets racing robots. Whether it’s harnessing creativity by building robot seascapes with LEGO or figuring out how to code a Toilet Paper Ration Robot in Blockly, the “Make with me” videos have something for everyone. Check it the whole playlist on YouTube and don’t forget to subscribe so you never miss a new video!
It’s been over a year since we launched #CubeletsChat, our blog and email series for teachers by teachers. Every topic we write about comes from a question or conversation with an educator like you. Whether we’re highlighting some great resources for your sub binder or helping you dive deeper into the computational thinking skills that Cubelets can teach, #CubeletsChat is specifically for you. Whether you’re new to the Cubelets community or are an adept looking for next steps with Cubelets, hopefully you’ll find a couple articles that meet your needs along your Cubelets journey. Check out these blog posts for educators just getting started with Cubelets. For the Beginners: It’s easy to be intimidated by Cubelets when you first pull them out of the box. After all, they’re blocks…that teach computational thinking…but how? Continue reading
The critical thinking required for effective programming and computer science is increasingly being recognized as a fundamental 21st-century skill. As experts around the world began to ask how to present concepts like decomposition, abstraction, algorithmic solutions, and debugging, one of their first steps was to make the act of coding more accessible to younger and more diverse learners. Now, we’re used to seeing such programs as Scratch and Cubelets Blockly in elementary and middle school. These color-coded pre-built code blocks allow students to drag and drop to build a program without needing to memorize the vocabulary and syntax of a  programming language first. We all agree this is more developmentally appropriate for young learners who are simultaneously still grasping the fundamentals of their primary language through reading and writing instruction. But what about students who are pre-literate or are struggling with reading in their native language? That’s where Cubelets come in. Cubelets are block-based programming. Literally.  Each Cubelet is itself a color-coded block of programming. We also refer to this as Tactile Coding, since Cubelets program robot behaviors without a screen. For example, the Inverse Cubelet is equivalent to an inverse block in Cubelets Blockly. The Inverse Cubelet creates the same behavior as the inverse of block in Blockly Continue reading
Computational Thinking is a term that’s being thrown around left, right, and center these days. By now, we all have a pretty good understanding of what computational thinking means (if not, check out our blog post about it here!), but for all the definitions of computational thinking, how do we know if students are demonstrating growth in their computational thinking? As with all growth measurements, having students take a pre- and post-assessment is the best way to get growth data, especially if it’s supported by formatives along the way. So, what if we started with a bigger-picture approach? We could measure students at the beginning of the year, in the middle of the year, and at the end of the school year. That way we would get an idea of how students’ computational thinking overall changes during this time in our classrooms. So, what does computational thinking look like?  How is it different from, say, number sense or even being proficient in a specific programming language like Blockly? Researchers have been trying to isolate computational thinking in assessments for years, and their hard work is starting to pay off. From rubrics, to programming analysis, to multiple choice tests, the options are growing and constantly being tested for greater accuracy and reliability. Here is an example of a computational thinking behavioral rubric developed for the Livingstone Academy in East London. It is clear this resource is designed for teachers by teachers. These teachers focus primarily on the supporting behavioral aptitudes. Things like: confidence in understanding complex problems, persistence in working with difficult problems, iteratively developing solutions, and communicating throughout the process with peers. Behavioral aptitudes are often a great launchpad for teachers seeking to gather data about a new skill or process. After all, if students are struggling with any of these behavioral categories, it will be incredibly hard for them to demonstrate the thinking they are capable of. Regardless of the age of your students, you may consider learning more about the Bebras assessment which provides great examples of non-coding-based questions that were developed in conjunction with the University of Oxford. They have printable cards (.pdf) for primary students (grades 1-5) as well as an app for middle school and high school students. Here are some sample challenges from previous years for different age groups in the UK. So, how can you assess students’ growth in computational thinking using Cubelets? Try giving related challenges to students at the beginning and the end of the year. First, build a steering robot (or a lighthouse if you are working with younger students). At the end of the year, build a maze-solving robot (or an energy-efficient lighthouse for our primary learners). While these end-of-year challenges may be repeats from an earlier lesson, their value is in how students approach the challenge and which intermediate robots they try before they are successful. Ask students to explain their solution as well as how they got to that answer. Listen for language specific to each of the four main subskills of computational thinking:
  • Decomposition:“First, I thought about the different things my robot would need to do. For instance….”
  • Pattern Recognition:“Then I thought about other robots, examples in nature, examples outside the classroom that this reminded me of. For example…”
  • Abstraction:“At first, I tried really hard to work on_____. But then I realized that was sending me down the wrong path and it wasn’t as important as I originally thought it was. So I decided to focus on ____ instead.”
  • Algorithmic Solutions:“Finally, I landed on this robot. You’ll notice it meets the design challenge because it does ______ and _______. Let me tell you how it works: [gives an explanation of the robot that includes both cause-and-effect and sequential language tags].”
Cubelets provide an intuitive way for students to build their computational thinking skills throughout the year regardless of how you assess their computational thinking. I recommend including Cubelets challenges as part of your Computational Thinking math workshop station or as a part of your science rotation. Even if you are choosing to use Cubelets alongside your fiction writing or character traits unit, you’ll be providing your students a highly-engaging way to flex their computational thinking muscles, which is exactly what we want students to experience: fun while learning.  
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By now, you’ve probably heard all about Computational Thinking. You’ve already defined it and shown how it relates to your content. But of course, Computational Thinking applies to many subjects and tools, including Cubelets. Here at Modular Robotics, we define computational thinking as being a problem-solving process that helps break down complex problems into smaller parts, so you can develop a model to solve the problem, evaluate the results, and recreate the solution over and over!  (If you’d like to learn more about our definition, check out our page devoted entirely to Computational Thinking.) Computational Thinking is commonly divided into four subskills:
  • Decomposition
  • Pattern Recognition
  • Abstraction
  • Algorithmic Solutions
Continue reading