We live in a world surrounded by technology, and we know that whatever field our students choose to go into as adults, their ability to succeed will increasingly depend on understanding how technology works. But only a fraction are learning how technology works.

That’s why Emerson joins the largest learning event in history: The Hour of Code, during Computer Science Education Week, the first full week of December every year. This Hour of Code makes the statement that Emerson Elementary is ready to teach these foundational 21st-century skills.

What is the Hour of Code?

The Hour of Code is a worldwide movement that aims to introduce millions of students to computer science through one-hour coding activities. Through Hour of Code, we aim to demystify coding and show that anyone can learn the basics, inspiring future interest in computer science.

Hour of Code @ Emerson

Volunteers have run the Hour of Code at Emerson since 2013 for 1st-5th grade; and introduced it to Kindergarten in 2022.

These are the lessons we currently run by grade:

  • 5th — Tetherball — this walks students line by line through a javascript-like language to build a simple game from scratch. They learn the concepts of variables and functions, using code to draw circles, lines, and text on the screen; and even implementing basic physics! The instruction method is somewhat pedantic, where they must type exactly the code they are being told to, and it can be challenging for students who are not comfortable typing.

  • 4th — ComputeIt — this turns the tables on programming and makes the students have to act on the instructions they are being given. They have to infer how to interpret various kinds of loops, functions, if/else/switch, and more; while getting immediate feedback on how they’re doing. ToxiCode (the publisher) has a host of games like this, but this is the most holistic.

  • 3rd — Blocks Jumper — this uses a Scratch-like interface to build a game where they can draw the level, assign actions to different sprites, and work through different game states. They’re walked through step by step, with examples, explanations, and instructions. CodeMonkey has a solid platform for teaching by example. Running this with a class on chrome books comes with its own challenges—you can see a 20-minute walk through of the entire lab here: https://youtu.be/uf8krjJAymE

  • 2nd — Flappy Code — our first foray with CodeMonkey’s Scratch-like interface, Flappy Code is a favorite that most students get to the end of. It helps accustom them to an IDE of sorts, and can be a review of clicking, dragging, and keyboard use. There’s a 12-minute walk through of the entire lab here: https://youtu.be/2lFvVAw2Ago

  • K + 1st — Kodable — there are too many “solve the maze with programming” options on the hour of code website; but this is one of the best ones, instructing fuzzballs how to traverse a maze—starting with simple order of operations, then color-based conditionals, loops, and more. For K, we’ve had success with the 2nd graders coming in and helping them, which is great learning for both grades.

AI In the Classroom

We’ve tried two of the AI hour of code projects over the years with mixed success.

AI is a Hoot is an excellent project where students train pose classifiers to control an owl’s actions on screen—it essentially builds from Blocks Jumper, but has them stand up/sit down (or other poses of their choice) to make the owl jump, etc. There are a few confusing bits from a UX standpoint, but the real kicker is the chromebooks really struggle to run it. If you have a more powerful computer at home (with a webcam), this can be a great more-advanced lesson (we’ve run it in 4th and 5th). We’ve got a 30-minute walk through of the lab: https://youtu.be/ODvl4QPZ8s4

AI for Oceans is a little too simple for a whole hour for later grades; but awkward to run as a class activity for earlier grades. It has some great social, environmental, and AI-relevant topics; and could be a nice activity on the side with a student who was focused and interested. Students select data to build their own classifiers (starting with “fish” and “trash”); and build an understanding of bias and the importance of data.