Student groups are challenged to program robots with light sensors to follow a black line. Learning both the logic and skills behind programming robots for this challenge helps students improve their understanding of how robots "think" and widens their appreciation for the complexity involved in programming LEGO® MINDSTORMS® NXT robots to do what appears to be a simple task. They test their ideas for approaches to solve the problem and ultimately learn a (provided) working programming solution. They think of real-world applications for line-follower robots that use sensor input. A PowerPoint® presentation and pre/post quizzes are provided.

Students are challenged to design and program Arduino-controlled robots that behave like simple versions of the automated guided vehicles engineers design for real-world applications. Using Arduino microcontroller boards, infrared (IR) sensors, servomotors, attachable wheels and plastic containers (for the robot frame), they make "Lunch-Bots." Teams program the robots to meet the project constraints—to follow a line of reflective tape, make turns and stop at a designated spot to deliver a package, such as a sandwich or pizza slice. They read and interpret analog voltages from IR sensors, compare how infrared reflects differently off different materials, and write Arduino programs that use IR sensor inputs to control the servomotors. Through the process, students experience the entire engineering design process. Pre/post-quizzes and coding help documents are provided.

Based on their experience exploring the Mars rover Curiosity and learning about what engineers must go through to develop a vehicle like Curiosity, students create Android apps that can control LEGO MINDSTORMS(TM) NXT robots, simulating the difficulties the Curiosity rover could encounter. The activity goal is to teach students programming design and programming skills using MIT's App Inventor software as the vehicle for the learning. The (free to download) App Inventor program enables Android apps to be created using building blocks without having to actually know a programming language. At activity end, students are ready to apply what they learn to write other applications for Android devices.

As part of a design challenge, students learn how to use a rotation sensor (located inside the casing of a LEGO® MINDSTORMS ® NXT motor) to measure how far a robot moves with each rotation. Through experimentation and measurement with the sensor, student pairs determine the relationship between the number of rotations of the robot's wheels and the distance traveled by the robot. Then they use this ratio to program LEGO robots to move precise distances in a contest of accuracy. The robot that gets closest to the goal without touching the toy figures at the finish line is the winning programming design. Students learn how rotational sensors measure distance, how mathematics can be used for real-world purposes, and about potential sources of error due to gearing when using rotation sensor readings for distance calculations. They also become familiar with the engineering design process as they engage in its steps, from understanding the problem to multiple test/improve iterations to successful design.

As the first engineering design challenge of the unit, students are introduced to the logic for solving a maze. First they observe a blindfolded student volunteer being guided through a classroom maze by the simple verbal instructions of another student. In this demonstration, the blindfolded student represents a robot and the guiding student represents programming commands. Then student groups apply that logic to program LEGO MINDSTORMS(TM) NXT robots to navigate through a maze, first with no sensors, and then with sensors. A PowerPoint® presentation, pre/post quizzes and a worksheet are provided.

Students work as engineers to learn about the properties of molecules and how they move in 3D space through the use of LEGO MINDSTORMS(TM) NXT robotics. They design and build molecular models and use different robotic sensors to control the movement of the molecular simulations. Students learn about the size of atoms, Newman projections, and the relationship of energy and strain on atoms. This unique modular modeling activity is especially helpful in providing students with a spatial and tactile understanding of how molecules behave.

Students design, build and evaluate a spring-powered mouse trap racer. For evaluation, teams equip their racers with an intelligent brick from a LEGO© MINDSTORMS© NXT Education Base Set and a HiTechnic© acceleration sensor. They use acceleration data collected during the launch to compute velocity and displacement vs. time graphs. In the process, students learn about the importance of fitting mathematical models to measurements of physical quantities, reinforce their knowledge of Newtonian mechanics, deal with design compromises, learn about data acquisition and logging, and carry out collaborative assessment of results from all participating teams.

Students' understanding of how robotic touch sensors work is reinforced through a hands-on design challenge involving LEGO MINDSTORMS(TM) NXT intelligent bricks, motors and touch sensors. They learn programming skills and logic design in parallel as they program robot computers to play sounds and rotate a wheel when a touch sensor is pressed, and then produce different responses if a different touch sensor is activated. Students see first-hand how robots can take input from sensors and use it to make decisions to move as programmed, including simultaneously moving a motor and playing music. A PowerPoint® presentation and pre/post quizzes are provided.

This activity helps students understand how a motor in a LEGO MINDSTORMS(TM) NXT robot uses electricity produced by the battery to move a robot to do useful work in the form of throwing a ball. Students relate the concepts of electricity and battery to the movement of the LEGO NXT motor and connected links.

Using new knowledge acquired in the associated lesson, students program LEGO MINDSTORMS(TM) NXT robots to go through a maze using movement blocks. The maze is created on the classroom floor with cardboard boxes as its walls. Student pairs follow the steps of the engineering design process to brainstorm, design and test programs to success. Through this activity, students understand how to create and test a basic program. A PowerPoint® presentation, pre/post quizzes and worksheet are provided.

Students will use previous knowledge of Ozobots and coding to color code a path in a maze to create a snowman.

Students apply concepts of disease transmission to analyze infection data, either provided or created using Bluetooth-enabled Android devices. This data collection may include several cases, such as small static groups (representing historically rural areas), several roaming students (representing world-travelers), or one large, tightly knit group (representing urban populations). To explore the algorithms to a deeper degree, students may also design their own diseases using the App Inventor framework.

Students download the software needed to create Arduino programs and make sure their Arduino microcontrollers work correctly. Then, they connect an LED to the Arduino and type up and upload programs to the Arduino board to 1) make the LED blink on and off and 2) make the LED fade (brighten and then dim). Throughout, students reflect on what they've accomplished by answering questions and modifying the original programs and circuits in order to achieve new outcomes. A design challenge gives students a chance to demonstrate their understanding of actuators and Arduinos; they design a functioning system using an Arduino, at least three actuators and either a buzzer or toy motor. For their designs, students sketch, create and turn in a user's manual for the system (text description, commented program, detailed hardware diagram). Numerous worksheets and handouts are provided.

In computer science, program analysis is used to determine the behavior of computer programs. Flow charts are an important tool for understanding how programs work by tracing control flow. Control flow is a graphical representation of the logic present in the program. In this lesson, students learn about, design and create flow charts for different scenarios, including a game based on the Battleship® created by Hasbro©. In the associated activity, Flow Charting App Inventor, students apply their knowledge from this lesson and gain experience with a software application called App Inventor. This lesson and its associated activity can be stand-alone or used as a launching point for the Android Acceleration Application unit or any lesson involving App Inventor.

In this culminating activity of the unit, students bring together everything they've learned in order to write the code to solve the Grand Challenge. The code solution takes two images captured by robots and combines them to create an image that can be focused at different distances, similar to the way that humans can focus either near or far. They write in a derivative of C++ called QT; all code is listed in this activity.

Students analyze a cartoon of a Rube Goldberg machine and a Python programming language script to practice engineering analysis. In both cases, they study the examples to determine how the different systems operate and the function of each component. This exercise in juxtaposition enables students to see the parallels between a more traditional mechanical engineering design and computer programming. Students also gain practice in analyzing two very different systems to fully understand how they work, similar to how engineers analyze systems and determine how they function and how changes to the system might affect the system.

Working in small groups, students complete and run functioning Python codes. They begin by determining the missing commands in a sample piece of Python code that doubles all the elements of a given input and sums the resulting values. Then students modify more advanced Python code, which numerically computes the slope of a tangent line by finding the slopes of progressively closer secant lines; to this code they add explanatory comments to describe the function of each line of code. This requires students to understand the logic employed in the Python code. Finally, students make modifications to the code in order to find the slopes of tangents to a variety of functions.

Students write Arduino code and use a “digital sandbox” to create new colors out of the three programming primary colors: green, red and blue. They develop their own functions, use them to make disco light shows, and vary the pattern and colors of their shows. The digital sandbox is a hardware and software learning platform powered by a microcontroller that can interact with real-world inputs like light, while at the same time controlling LEDs and other outputs.

Through the two lessons and five activities in this unit, students' knowledge of sensors and motors is integrated with programming logic as they perform complex tasks using LEGO MINDSTORMS(TM) NXT robots and software. First, students are introduced to the discipline of engineering and "design" in general terms. Then in five challenge activities, student teams program LEGO robots to travel a maze, go as fast/slow as possible, push another robot, follow a line, and play soccer with other robots. This fifth unit in the series builds on the previous units and reinforces the theme of the human body as a system with sensors performing useful functions, not unlike robots. Through these design challenges, students become familiar with the steps of the engineering design process and come to understand how science, math and engineering including computer programming are used to tackle design challenges and help people solve real problems. PowerPoint® presentations, quizzes and worksheets are provided throughout the unit.

This unit is designed for advanced programming classes. It leads students through a study of human vision and computer programming simulation. Students apply their previous knowledge of arrays and looping structures to implement a new concept of linked lists and RGB decomposition in order to solve the unit's Grand Challenge: writing a program to simulate peripheral vision by merging two images. This unit connects computer science to engineering by incorporating several science topics (eye anatomy, physics of light and color, mathematics, and science of computers) and guides students through the design process in order to create final simulations.