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New Post has been published on https://nclab.com/computers-wore-skirts/

When Computers Wore Skirts

Today, the word “computer” usually refers to a powerful electronic machine. But this was not always the case. Before the use of electronic computers, and even before the use of mechanical computers, the computers were human. Among the earliest records of organized human computing was the computation of the trajectory of the Halley’s Comet by Alexis Claude Clairaut and his two colleagues in France in the 18th century. In the late 19th century, thirty five human computers were employed to create the British Nautical Almanac of data that was used for navigation at sea. Many human computers were employed to compile Western European mathematical tables, mainly those for trigonometry and logarithms. The computers were often educated middle class women. In many cases, they stayed at home and received and send back large packets of calculations by post.

New Post has been published on https://nclab.com/31273-2/

NCLab in Public Libraries in Nevada

by Tammy Westergard, Assistant Administrator of Library / Library Development Services, Nevada State Library, Archives and Public Records

Every day changes and advancements in technology, accelerates what it takes to live day to day. Everyday learning in the 21st century means level up or be left behind. This is the stark reality. Fortunately, within Nevada’s Everyman’s University System (also known as our public libraries) NCLab is there to explain, demonstrate and inspire. Through their exceptionally designed online learning system, anchored with sound teaching methods and learning design, Nevadans are developing coding skills and abilities essential in today’s workplace. Nevadans are learning at the library how to combine building and problem-solving. This talent will allow them to work, earn a living wage and find purpose in creating tomorrow’s digital tools and solutions necessary to address challenges we can’t even see today.

New Post has been published on https://nclab.com/building-karel-robot-lego/

Building Karel the Robot is an Invaluable Learning Experience for Students

by Dave Riske, Networking Technologies Instructor, Western Nevada College (WNC), Carson City

NCLab’s Karel the Robot is a perfect tool to teach students the fundamental ideas of computational logic and structured design, uncluttered by specific programming language requirements. The premise behind the Karel language is to use a simple syntax structure with simple logic as a starting point and then slowly building the complexity of both syntax and logic. Though students were grasping the concepts presented in the activities in Karel, there was still a tangible component missing. WNC already owned several versions of the Lego Mindstorm NXT and EV3 robotic kits. Therefore I decided to try to externalize Karel the Robot from the screen to the tabletop. Initial requirements were simple:

The robot must look like Karel the Robot.

The robot must respond in a manner consistent with the virtual robot in NCLab.

Students should be able to use a command set similar to the commands available in NCLab to control the robot to solve mazes.

Building mazes for the robot must be easy and relatively inexpensive.

Physical Robot and Playing Field

The robot and the playing field must mimic the environment available in NCLab. The initial plan for the playing field was to use a 4×8 sheet of plywood as a base. This was gridded out in 8×8 squares yielding a playing field of 6 by 12 squares, which could expand easily with the addition of another board to 12×12. This would require careful consideration of which puzzles and mazes to externalize as many of the mazes are larger than the available physical space.

Ultimately, the playing field is a maze. We are currently investigating using foam core, cut in an interlocking method, that will allow us to build an interior wall that can be ‘seen’ by the robot and provide the means to have the robot respond to the environment of the maze. The foam core will also be used to provide a border wall around the playing field.

Initial searches yielded a robot design that looked similar to Karel the Robot and was modifiable with respect to available components within the Lego kits already owned by WNC. This initial build was also modifiable dependent on needs that became evident as the various programs were designed to control the robot. Further work and development will be done on the physical aspects of the robot to simplify the build to one type of standard kit and its associated expansion kit.

During the rebuild of the physical robot to include a single Lego Kit the robot base size grew by approximately 1.5 inches when the EV3 treads system was used. This made the initial design of the field inadequate to hold the robot. Resizing the squares to 12 inches will further shrink the effective playing field.

From left to right: Karel the Robot model 2.0 (single Lego set) and model 1.0 (two Lego sets).

Command Structure

The commands available to control Karel the Robot are Go, Left, Right, Get and Put. These are initially used in sequences and are later combined within decision structures (If statement) and loops (repeat) to enhance both the logic and the syntax of the program as well as provide discussion for the three primary components of programming structure. Our initial intent is to make Karel the Robot as independent as possible with respect to movement, not relying on cues from the maze like line following. Get and Put commands would rely on control of the robot’s screen to display output with respect to number of objects.

With the initial trials with respect to turning the robot, we discovered very quickly that there is an inherent lack of accuracy in the Lego parts. Variations in the motors, power applied to the motors, the treads, the maze and even the battery power all made for variations on the turn. Installing the gyroscope sensor on the robot allowed for measurement of the error, +/- 4 degrees on a 90-degree turn. It was evident that a self-correcting program was needed that would measure and seek the desired turn. Searches revealed programs for selecting the direction of the turn (left or right) and for controlling (and measuring) the amount of turn. Combining these two programs yielded a program that allows for precise turns with self-correction. The correction, however did allow the discovery of the inherent error in the gyroscope measurement. Measurement of the turn, when the gyroscope reads 90 degrees is currently at 90.8 degrees. The hope is that this error is consistent and, therefore, able to be programmed out of the turn.

Future Work

Currently students are working on refinement of the turning function, a true 12 inch Go Function and providing the Karel the Robot with the ability to recognize different colored objects which will be used to distinguish object within the mazes. They are also working on the physical aspect of the maze as additional materials are found which can provide for building.

New Post has been published on https://nclab.com/31221-2/

Video Games Are Good for Learning

by James P. Gee, (Regents Professor and Fulton Presidential Chair of Literature Studies at the Arizona State University. Member of the National Academy of Education)

What makes video games good for learning has little to do directly with the fact that they are games. Furthermore, video games that are most interesting for learning are not just any video games. Different types of games can have different effects. Puzzle games like Tetris and Bejeweled may very well exercise pattern recognition capacities; Trivial Pursuit games may well make learning facts fun. But these are not, in my view, the sorts of video games which are most interesting in regard to learning.

The video games that I think are most interesting for learning, are digital simulations of worlds that are “played” in the sense that a player has a surrogate or surrogates through which the player can act within and on the simulation and that have “win states” (reachable goals that the player has discovered or formed through his or her surrogate). By the way, in augmented reality games, a person can be playing a virtual role (e.g., urban planner, toxic spill specialist, detective) in a rule system that is designed to play out partly in a virtual world and partly in the real world.

Games are simulations that are focused on problem solving and which players can enter and not just inspect from the outside. Since humans learn best from experiences that are focused on well-mentored problem solving, games are an ideal setting for learning. And, by the way, games have teachers: the game designer and the mentoring and teaching built right into the game design itself.

Video games don’t just carry the potential to replicate a sophisticated scientific way of thinking. They actually externalize the way in which the human mind works and thinks in a better fashion than any other technology we have. We humans think by simulating and testing possible courses of action in our minds before we act. Video games are just an external simulation and testing platform, directly supplementing our minds with the potential to better prepare us to act in the world (Imagine that Presidential candidates had to win at least one game of Civilization before they could run for office).

Writing, digital computers, and networks each allow us to externalize some functions of the mind. Though they are not commonly thought of in these terms, video games are a new technology in this same line. They are a new tool with which to think about the mind and through which we can externalize some of its functions. Better yet, they allow for collaboration and collective intelligence as forms of intelligence than can far transcend our individual knowledge.

Video games are good for learning because, among other reasons, they have the following features:

They can create an embodied empathy for a complex system built as a world focused on particular types of problems.

They are action-and-goal-directed preparations for, and simulations of, choices and actions.

They involve distributed intelligence via the creation of smart tools like the “mods” in World of Warcraft that tell players how to coordinate their play with others.

They create opportunities for cross-functional affiliation where smart teams combine people with different specific deep skills but where each team member can coordinate with each other’s skill set effectively.

They allow meaning to be situated in (associated with) images, actions, goals, and experiences, not just in verbal definitions cut off from the world.

They can be open-ended, allowing for goals and projects that meld one’s personal goals, social goals as collaborators, and the game designer’s goals (the teacher’s goals).

None of this is to say that video games do these good things all by themselves. It all depends on how they are used and what sorts of wider learning systems (activities and relationships) they are made a part of. None of these reasons why video games are good for learning stems primarily from a game’s great 3-D graphics or the mere fact that it is a game in the general sense of “game”. The cutting edge of games and learning is not in video game technology—although great graphics are wonderful and technical improvements are important. The cutting edge is realizing the potential of games for learning by building good games into good learning systems in and out of classrooms and by building the good learning principles in good games into learning in and out of school whether or not a video game is present.

New Post has been published on https://nclab.com/art-3d-modeling-efficient-boolean-operations/

The Art of 3D Modeling - Efficient Boolean Operations

In 3D Modeling, by Boolean operations we mean creating intersections and unions of objects, as well as subtracting objects from each other. All these are set operations that students know from Venn diagrams. Let’s stay with the last one for the moment. In order to demonstrate subtraction, let’s consider a pair of overlapping objects, such as a cylinder and a cube:

Subtracting the cube from the cylinder is not technically difficult – usually this is done using a single command or mouse click in the 3D modeling software at hand. As a result, one obtains a set of 3D points that belong to the cylinder but not to the cube:

However, Boolean operations become tricky when complex objects are involved. Let’s have a look at this spoked wheel which is an extension of the hollow cylinder that we just made:

The wheel can be built in many different ways. Let’s consider two. First, let’s (1) create the interior cylinder, (2) subtract the cube from it and (3) create a union with the spokes and the rim. Exactly the same result can be obtained by (1) creating a union of the interior cylinder, the spokes, and the outer rim and (2) subtracting the cube from it. However, one of these two approaches will be significantly computationally simpler and faster than the other. Can you guess which one it is?

Before we answer, let’s understand how the subtraction works. It is an operation between 3D objects. The surface of each object is defined via certain number of faces. The algorithm analyses the interaction of all faces of one object with all faces of the other object. Therefore, the computational complexity increases rapidly when objects have a large number of faces.

In the former approach, the subtraction occurs between two relatively simple objects – a cylinder and a cube. In the latter, the cube is subtracted from a much more complex object that is formed by the inner cylinder, 16 cylindrical spokes, and the outer rim. Therefore, the former approach is computationally much more efficient. In concrete numbers, the CPU time for the former approach was 1.9 s while the latter approach took 5.4 s which is almost three time more.

New Post has been published on https://nclab.com/playing-engineering-discovery-museum/

Playing with Engineering at the Discovery Museum

NCLab partnered with the Terry Lee Wells Discovery Museum in Reno to build a setting where kids can play with an engineering app to computationally test mechanical properties of materials. Click on the image to read the full article:

New Post has been published on https://nclab.com/teach-3d-modeling-nclab/

How to Teach 3D Modeling with NCLab

Watch a 45-minute webinar recording where NCLab’s Director of Education Sheila Bunch explains how to teach 3D modeling with NCLab:

New Post has been published on https://nclab.com/fourth-industrial-revolution/

What is the Fourth Industrial Revolution?

What is the Fourth Industrial Revolution, sometimes (inaccurately) called Industry 4.0 or just 4IR? Before we answer, let us take a step back.

1. Steam

The First Industrial Revolution, or “Industrialization”, began in England in the late 18th century, following in the wake of James Watt and his steam engine (1774). Tasks previously done laboriously by hand in hundreds of weavers’ cottages were brought together in a single steam cotton mill, and the factory was born. The initial focus of industrialization was on textiles. The First Industrial Revolution marked a major turning point in history – almost every aspect of daily life was influenced in some way. Economic historians are in agreement that the onset of the First Industrial Revolution is the most important event in the history of humanity since the domestication of animals and plants.

2. Electricity

The Second Industrial Revolution, also known as the “Technological Revolution,” was a phase of rapid industrialization in the final third of the nineteenth century and the beginning of the twentieth century. It was powered by electricity. It is considered to have begun with Bessemer steel in the 1860s and culminated in mass production and the production line. The enormous expansion of rail and telegraph lines after 1870 allowed unprecedented movement of people and ideas, which culminated in a new wave of globalization.

3. Computers

The Third Industrial Revolution, or the “Digital Revolution”, refers to the advancement of technology from analog electronic and mechanical devices to the computers that are available today. The era started to during the 1980s and is ongoing. Advancements during the Third Industrial Revolution include the personal computer, the internet, and information and communications technology (ICT).

4. Cyber-Physical Systems

While the first three Industrial Revolutions were characterized mainly by advances in technology, the fourth one is characterized by a fusion of technologies that is blurring the lines between the physical, digital and biological spheres. In other words, the Fourth Industrial Revolution is not changing what we are doing – it changes us, humans. This is possible thanks to emerging technology breakthroughs in fields such as artificial intelligence, robotics, the Internet of Things, autonomous vehicles, 3D printing, nanotechnology, biotechnology, materials science, energy storage, and quantum computing. Examples of this fusion include using 3D-printed parts such as artificial limbs or vertebrae to repair the human body, enhancing impaired human bodies with robotic parts, combining 3D printing with living stem cells to grow artificial tissues and bones. Engineers, designers, and architects are combining computational design, additive manufacturing, materials engineering, and synthetic biology to pioneer a symbiosis between microorganisms, our bodies, the products we consume, and even the buildings we inhabit.

Unfortunately, not all outlooks are entirely positive. Especially those regarding the development of social inequalities and division of labor in the human population. If you have 11 minutes of time, watch this highly inspiring video: