Summer provides a natural opportunity to reflect and regroup for many educators. Within a museum, the time is often filled with camps, teacher workshops, and special public events, but time must still be set aside for planning. Over the next couple months, I will be transitioning to a new position at work as well as finishing my Master’s degree, so considering the future is particularly relevant at the moment. In order to continue to grow personally and professionally, my current learning goals include gaining experience in hobbyist programming, learning more about how non-profits are managed, and finding new ways to engage the public within a museum.
These goals are chosen based on the idea that I want to continue to do well in a museum environment, but may also wish to explore my own interests in another non-profit setting where much of my experience will transfer over. Of particular interest to me is how to engage a wide variety of ages in learning new technologies, including developing programming and computational thinking skills. I also want these goals to be achievable in the next year and have concrete end products that show evidence of growth.
A video produced to demonstrate differences in teaching approaches when presenting to large and small groups in a museum education setting.
Forty years ago, Donald Knuth argued that computer programming is both an art and a science, with the two aspects not at odds but complementary to each other. Writing a program “can be like composing poetry or music…programming can give us both intellectual and emotional satisfaction” (Knuth, 1974, p. 670). He perceived that it is not only the result of an algorithm that can demonstrate creativity, but the act of creating the algorithm as well. This is akin to recognizing that the techniques of a painter can be as creative as the painting itself, as the process and product are intrinsically linked. Creativity in programming has long been accepted in the computer science community, but not necessarily with the general public. One contributing factor is that the algorithms and code that implements them have often been obscured from the end user, or to borrow an art term, viewer.
A new museum exhibit, The Art of Algorithms, will pull back the curtain to reveal the spectrum of creativity used in creating and implementing algorithms and demonstrate applications of computational thinking in the arts. By participating in this interactive museum exhibit, visitors will not only be able to experience examples of digital art, but also create works to be displayed within the gallery. Museums play an important role in communities by not only elucidating difficult concepts but nurturing passions in different fields of study and showing connections across disciplines.
To model is to gain a deep understanding of an object, a process, or idea by representing essential information by physical or virtual means. This can be done as a preliminary sketch in the act of creation, but it can also be used to represent knowledge that would otherwise be difficult to experience first-hand. Often modelling makes use of dimensional thinking, which is the translating or conceptualizing across dimensions of space, time, or any other range of related values (Root-Bernstein & Root-Bernstein, 1999).
Models of instructing computing machines to aid in problem solving have existed for over 150 years. When modelling a computer algorithm, I chose to explore its place across multiple sets of dimensions. The most obvious would be of scale and thus visibility,starting with that which can be viewed easily, such as the general solution to a problem and its implementation, then proceeded into that which is usually hidden , such as low level instructions, or microscopic in the case of transistors. This model also explores proceeding from the general to the specific, as implementing an algorithm is to use your own creativity in how to best do that, as my recipe analogy suggests. Parallel to this progression is starting with the virtual, such as an idea of how to accomplish a task, to the physical, with the actual tools being used to help complete that task.
If an algorithm is itself a model for understanding how to accomplish a task, the difficulty lies in creating a model to represent what is a model already, just as I found when trying to abstract an abstraction. Not surprisingly, the two skills of modelling and abstraction rely on each other, as a model is a useful abstraction. To accomplish this goal, I’ve decided to focus on the transition from algorithm to implementation, or in computing, creating a program based on an algorithm. Part of this process is accomplished by a person, the rest by a computer, so this lends itself well to computational thinking.
To gain a better understanding of how an algorithm is transformed into a program, I created a model that views this across physical scale, from the large to the very small; transitions from digital to physical; and demonstrates changes in logic, from the abstract to the specific. It elucidates what occurs behind the scenes when a program is executed, and shows how layers of abstraction in a computer provides human with the ability to intuitively instruct them.
Programming can be intimidating, but by allowing students and museum visitors to take on roles of characters engaged in computational thinking, they can gain an understanding of how an algorithm works in a visceral way. Visitors can not only view but also participate in a stage show where they can work alongside museum staff to act out a basic understanding of computer programs. During the play, characters will use motions to reinforce what each part of the algorithm does, further embodying the concept being learned.
Programmers solve problems. They accomplish this by working with or taking inspiration from computers to find solutions to problems, but they don’t do it alone. They have a set of thinking tools that will allow them to accomplish their tasks more easily called computational thinking. The play will explore different computational thinking skills as well as basic tools available to programmers. Even the play itself is an example of computational thinking, as actors are using a simulation to gain a better understanding of algorithms, just as a computer may be used to run a simulation to better understand a problem.
In my experience, teaching can often feel like you are working within a bubble. While you may discuss your teaching practices with coworkers at short meetings, there is little chance during the day to truly share your ideas, including those on effective use of tools within the classroom. To alleviate that sense of isolated teaching, I prepared and sent out a survey to my colleagues at the Cranbrook Institute of Science on how they use technology to support learning. Seven of the educators, or just over half, responded to thirteen multiple-choice questions, and while the sample size was small, there were a few questions where there was a clear consensus which could be useful. I don’t see our workplace as having cutting-edge technology nor do we often consider how to effectively use what we have at our disposal, so I see this survey as a starting point for further discussions on integrating effective tools for learning in our programs.
A couple popular tinkering activities that we’ve used in the past at the Cranbrook Institute of Science. I’m linking to instructions to where we found the ideas, but then also list how we made modifications.
Equipment (per student):
We’ve used a band saw to remove the handles of the toothbrushes, but can also snip them off. We just use tape instead of two-sided foam tape since we can just create a loop of tape to hold the battery to the toothbrush. We also use a piece of tape to act as a switch, by folding one end of the tape on itself to create a handle that can lift the tape off the battery to turn it off.
On Friday, I had the opportunity to visit the Exploratorium, an amazing convergence of art, science and technology, in order to gather more information on their workshop on scientific inquiry. I went up early to browse the piers before the onslaught of the crowds, and while waiting for the meeting to start, I happened to walk next to an open door in the building next to the Exploratorium, only to find a seven foot tall Strandbeest facing me.
With Thursday being my only free day, I decided to explore just a few of the museums in the area.After a trip by train, subway and bus, I finally reached the California Institute of Science by mid-morning, where they were already inundated with school groups. Every student I saw saw loved this place, and so did I.