Recently Added Curriculum

This unit introduces students to neuroscience through a systems approach with a strong emphasis on computational thinking and data analysis. Students investigate the neural origins of muscle movement by collecting and analyzing electrical signals from surface electrodes placed on the arm during simple hand gestures, such as wrist and finger movements. Using microcontrollers and an inquiry-based approach, students explore how different patterns of neural activation produce specific motions. The unit fosters practical data analysis skills while deepening students’ understanding of the interdisciplinary connections between neuroscience, computer science, and engineering.

This fourth and final activity in the unit allows students to analyze and visualize neural data, providing them with the opportunity to explore patterns in brain signals. Using Python, students interpret neural activity linked to various movements, such as finger and wrist motions, to understand how neural signals correspond to physical actions. The graphics.py library offers an intuitive introduction to data visualization, enabling students to create clear graphical representations of neural data before advancing to more sophisticated tools like matplotlib. By visualizing and analyzing their findings in a report, students deepen their understanding of neural data processing while honing critical skills in data analysis, programming, and scientific communication.

This third activity builds on the data collected from the previous "Decoding Muscle Movement: Analyzing Neuromuscular Signals With EMG" activity. Students now gather electrical data from various groups' experiments using Muscle SpikerBox kits, which save the data in .wav format. They then use Google Colab, a cloud-based Python development environment, to create a shared database for collaborative data analysis. As they convert and process the data, students write Python scripts and engage in data analysis, honing key computational thinking skills such as breaking down complex problems, identifying patterns, abstracting essential details, and designing algorithms. This hands-on activity promotes teamwork, enhances data manipulation proficiency with Python, and deepens students' understanding of neuroscience research methodologies.

In this second activity, students dive deeper into the neuromuscular system by exploring how the body recruits and activates muscles in response to various gestures. They begin by examining the neuromuscular junction and diagramming the neuronal circuitry pathway involved in muscle activation. Using electromyography (EMG), students learn how to measure subtle muscle responses triggered by wrist and finger movements. They collect and analyze EMG data with the help of surface electrodes and computer software, focusing on recording and processing signals from both large and small muscle groups at different speeds. By practicing data acquisition, filtering, and signal analysis, students apply the scientific method and engineering design process to understand how neurons interact with muscles. As they work in teams to compare signal parameters such as amplitude and frequency, they gain valuable insights into muscle recruitment and learn to distinguish between different types of gestures based on their EMG data. This activity reinforces students' understanding of neuromuscular function while enhancing their skills in data collection and analysis.

This is the first activity in a unit of four activities. Students are introduced to foundational neuroscience concepts, focusing on motor and sensory neurons and their connections to muscles at the synapse. Through handouts and activities, they explore brain anatomy and construct a basic homunculus to map brain regions to body functions. Students then engage in a hands-on micro:bit activity—observing and manipulating a simulated “beating heart” and a finger dexterity game—where wrist and finger movements control the heart’s size. This experiment helps students explore how different levels of movement affect neuron recruitment. Using an inquiry-based approach, they analyze these patterns and apply their findings to build a detailed neuron-muscle interaction map.

In this activity, students learn concepts related to the brain and nervous system via a hands-on mini robot activity. They learn about the similarities between the human brain and its engineering counterpart, the computer, as they create a program to navigate a maze. Given that students work with computers routinely, this comparison strengthens their understanding of both how the brain works and how the computer parallels the brain’s processing power, in addition to reinforcing human and robot interactions. Students strengthen their skills in experimental design, the engineering design process, testing, prototyping, and asking questions—important skill sets needed for success in the 21st century marketplace.

Students explore neuroscience concepts to understand muscle performance during exercise, focusing on motor units, muscle fibers, adenosine triphosphate (ATP), and fatigue. They create diagrams to compare large and small motor units and learn basic coding through a beating heart activity. Using the engineering design process, students design and build prototypes to record biopotential signals using EMG sensors during selected arm exercises. They test their prototypes, gather feedback, and refine their designs to improve signal recording and data accuracy. Finally, students analyze their data to identify signs of muscle fatigue and present real-world connections to fitness and health.

Students tackle the challenge of designing a community center by asking, “What does my community need, and what role do I play in shaping it?” They explore their community’s strengths and challenges, comparing them to the United Nations’ 17 Sustainable Development Goals to identify key design elements. By analyzing both local and global communities, they discover features that contribute to meaningful and sustainable development. Using the engineering design process, students apply their insights to digitally create a community center in SketchUp that reflects their city’s needs while aligning with sustainability goals. They refine their ideas through two rounds of peer feedback and redesign, fostering collaboration and critical thinking.

This interdisciplinary activity combines art, science, literature, cultural competency, and the engineering design process to engage young students in creative exploration. After listening to a read-aloud of Hieroglyphs A–Z, by Peter Der Manuelian, students are challenged to design a unique set of hieroglyphs to help a lost explorer navigate the school in search of treasure and cultural artifacts. Using limited materials, students harness the power of the sun, paper, and natural objects to create cyanotype-style prints that serve as maps and clues. They collaborate to design, test, and revise their prints, sharing feedback and improving their work. Along the way, students gain foundational knowledge in hieroglyphics, cyanotype printing, and map-making, while also developing critical thinking, creativity, and teamwork skills using the engineering design process.

Student groups take on the role of sports engineers as they invent a brand-new sport that features a ball and a bounce. First, they use the engineering design process to design the game, including custom rules, scoring methods, and equipment. Then, based on their game plan, they select a play surface and ball type from available classroom materials. Next, students conduct a "bounce test" to experiment with different combinations of balls and surfaces. Through this process, they explore the physical properties of materials and how they affect motion. To deepen their understanding, students apply mathematics to model and analyze motion, using quadratic equations to represent bounce behavior and interpret their results.

Students collect data from a sugar-level simulation by categorizing different food and drink solutions and measuring their impact on glucose levels. They then use this data to train a machine learning model using the "Machine Learning for Kids" platform. By inputting and organizing their data, students train the model to predict blood sugar responses and classify meals as either healthy or unhealthy. They test their model’s accuracy with new inputs and make adjustments to improve its performance. Through this hands-on process, students gain an understanding of how machine learning works, the importance of high-quality data, and how these technologies can support real-world health applications, such as managing diabetes.

Students investigate nitinol (nickel-titanium alloy), a shape memory alloy known for its ability to return to a pre-set shape when heated and its super elastic properties. Through hands-on exploration, they examine how phase transformations between martensite and austenite influence nitinol’s behavior and discuss its applications in fields such as biomedicine, robotics, and aerospace. By connecting microscopic atomic structures to macroscopic material properties, students develop a deeper understanding of how engineers design advanced materials to solve real-world problems.

Students explore the concept of dragging friction and participate in an activity to understand the cultural significance of the minga tradition. They then apply this knowledge to design a method for transporting materials up a hill. Their solution must reduce friction without altering the inclined plane, incorporate principles of the minga tradition, and utilize discarded materials commonly found in the area.

Students explore how animals and humans process sensory information and respond by conducting a reaction time experiment of dropping a ruler to see how quickly they can catch it. They then build a model using various materials to represent how their brain and body work during the experiment. Additionally, students learn the importance of brain protection by using the engineering design process to design and construct their own bicycle helmet using materials such as cardboard, egg cartons, and bubble wrap.

With a design-thinking approach, students incorporate neuroscience into their robotics learning experiences in this activity. They perform an experiment to design a basic human-robot interface through which electromyography (EMG) signal readings from the muscle movements in their arm are translated to simple movements in their robots. Students brainstorm factors they believe will vary between arm movements, and use these factors to develop a data processing program for the EMG data collected. In doing so, students form an understanding of the considerations that are involved in designing, building, and evaluating a human-machine interface.