Although I had attempted to capture the essence of an object through photos before, I found I made it way too easy to guess the item. So I chose a more unusual object, which is probably cheating, and tried for less obvious angles. People will still guess it within minutes, I’m sure.
To perceive is to not only use the entirety of the senses to observe an object or an idea, but to also create a mental model that can be recalled, manipulated, and transformed into something original. This level of perception can lead to a expertise that allows new understandings to be reached before they can even be expressed (Root-Bernstein & Root-Bernstein, 1999).
I thought it difficult at first to perceive an abstract concept such as algorithms. It was easier to explore my feelings about them, using words such as “rigid”, “cut-and-dry” and “repetitive.” Algorithms seemed to be the antithesis of creativity, that if they played too much of a role in teaching, learning in classrooms would become color-by-number. Our everyday word for it, that we follow a routine, is to be stuck in a predictable but hollow loop of behavior, day-in, day-out.
“For me, great algorithms are the poetry of computation. Just like verse, they can be terse, allusive, dense, and even mysterious. But once unlocked, they cast a brilliant new light on some aspect of computing.” – Francis Sullivan
Algorithms are found throughout mathematics and computer science. Whether it’s teaching students how to multiply multi-digit numbers or using a sorting routine to place information in order, following step-by-step instructions to achieve a consistent result is quite useful in those disciplines.
Nothing like a few brain burners to show the usefulness of computational thinking skills. I asked Meghan, my partner in crime and second grade teacher, to work with me in an attempt to solve two problems, using guidelines from Basic Strategy for Algorithmic Problem Solving. The tools presented in this algorithm were exhaustive and exhausting; we only applied some of them the two problems.
The first problem involved eight suitcases, identical in every way, except one was lighter than the rest. You have use of an balancing scale to compare their weights, with the goal of performing the fewest number of weigh-ins in order to identify the lightest briefcase. I had come across this problem before, most likely on Car Talk, so Meghan took over the problem solving and did an admirable job. She identified the necessary information in the problem including the data provided and what was being asked, just as she would ask of her students on a test. She determined she had all the information needed, so now it was time to find the algorithm.
Since the program I will be presenting through work for the next few months will be on using the Arduino in art and science, I decided to explore if there would be a post-lesson activity that could show use of the Arduino in other subject areas. I settled on Language Arts, since I enjoy poetry and wanted to see if the form could be rethought in an interactive way that will have students create a machine-aided haiku using core computational thinking concepts such as problem decomposition, data collection and automation (Barr & Stephenson, 2011). I’d come across computer poetry in Mindstorms (Papert, 1993) and a quick Google search of “Arduino Poetry” showed that several people have had similar ideas. This approach would be unique in that the program will use readings from temperature, light, and sound sensors to choose appropriate descriptive words.
I thought an app designed to develop programming skills in elementary aged children would not pose a challenge to someone twenty years older. Yet once I reached levels in LightBot that required you to create loops or make function calls with limited memory, I couldn’t mindlessly drag and drop instructions to have the bot behave correctly. I had to identify what movements the bot would take repeatedly or how to break the instructions into meaningful chunks. LightBot was already testing my computational thinking in several ways with these challenges.
From the very start, I had to think abstractly, which Grover and Pea (2013) identify as the “keystone” of CT, that which differentiates it from skills in other disciplines. My first problem was figuring out how to give instructions to the robot. The app confined the problem, requiring that I drag and drop predefined instructions such as movement and lighting up a square. This showed me that the designers already had to think abstractly on what tools to provide in order to solve give instructions, and I would have to think along the same lines. If I could define my own instructions, such as teleporting several blocks away or duplicating myself, this would be an application of my ability to abstract.
As a follow up to my previous pictures of a mystery object, here’s a picture of the object in exploded form:
Which makes it even easier to tell that this is indeed a joystick, which can be seen in non-exploded form at Sparkfun. Thanks to all who guessed! It would appear I should have made it more difficult to decipher, but the joystick ball (or clown nose, as some noted) was a giveaway.
Here’s my interpretation of the Count the Dots activity. Sorry for the shakeycam, my production assistant had gone to bed.
The program was created using Python on a Raspberry Pi, and the LED lights were controlled with an Arduino. The binary value was passed from the Pi to the Arduino via serial over USB.
In his book Mindstorms: Children, computers, and powerful ideas, Seymour Papert describes a vision of the use of computing and related thinking tools in education that is eerily prescient yet is far from being fully achieved. Taking into account ideas from Papert (1980), a broader definition of computational thinking can be reached than in my prior post: Computational Thinking is a mode of thought that is informed by or aided by computers. I view it now as not only problem solving skills, but also ways of building and authenticating knowledge or even as a different way of perceiving the world.
Through my readings, I’ve also come to realize what computational thinking (CT) is not. It’s not how computers think, since they don’t think in the sense that humans do, but how to pair human ingenuity with computer processing (Wing, 2006). Wing also points out that it’s not just computer programming, although software developers have used CT even before the term was coined, but a way of approaching problems. Papert (1980) argues that the use of a computer so fundamentally changes how the mind works that it need not always be present, as it acts as “a carrier of cultural ‘germs’ or ‘seeds’ whose intellectual products will not need technological support once they take root in an actively growing mind” (p.9).
Veja du (or jamais vu) challenges the viewer to recognize a common object depicted in an unusual way.
My object is perhaps not as commonplace in the everyday home, but I’m fairly certain we’ve all seen one before.
Too obvious? Any guesses?