Saturday, 7 October 2017

Turn your classroom into a citizen science lab with GlobalXplorer

Turn your classroom into a citizen science lab with GlobalXplorer

The GlobalXplorer logo.
“Citizen science” projects can boost student engagement by giving kids the opportunity to help solve real-world problems, in fields ranging from archaeology to zoology. One citizen science option that we recommend is GlobalXplorer, a new platform launched by archaeologist (and TED Prize winner) Sarah Parcak. Here’s how it works:
There are millions of lost temples, buried pyramids, and other archaeological sites around the world. Many of these sites contain ancient art, history, and artifacts — precious evidence of humankind’s collective resilience and creativity.
Your mission is to help protect these archaeological sites from looters.To succeed, you’ll need to study satellite images for signs of looting. In GlobalXplorer, these images are called “tiles.”
There are 120 million tiles in GlobalXplorer’s first expedition: Expedition Peru. So, archaeologists really need your help!
Can you examine 500 tiles?
To start your citizen science project with GlobalXplorer’s Expedition Peru, go here
To learn more about the history of exploration in Peru, start with this 1913 National Geographic article about Machu Picchu.

Writing in the Sciences



Five Ways Design and Making Can Help Science Education Come Alive



Five Ways Design and Making Can Help Science Education Come Alive







By Christa Flores
“How do research and design relate to each other? (…) Both activities produce knowledge, but of different kinds. (…) So, on the one hand, design is not a science in its own right, but draws on technical and scientific insights as well as artistic skill and ability. On the other hand design, although not a science, can be the object of systematic research.” — Christian Gänshirt, Tools for Ideas
Design is an artistic endeavor that values the creative and human centered application of math, science and technology. Using design to help others learn science is not intuitive, however, once practiced you will see how humanistic and authentic it is to incorporate design in any subject. Below is a list of the most promising benefits that I have noticed in the past six years for using design as a framework and making as the engine to empower students as they gain and apply their scientific literacy.
Benefit No. 1: Students learn more, love science more, and are more engaged in science content and the scientific process when designing solutions to real problems.
The creation of the artificial, whether a sling shot, calorimeter or electrical circuit, becomes a solution-finding crusade armed with scientific knowledge. When students invent, they take ownership over an idea, then face real-world problems en route to making their idea come to life. They act, think and work as real scientists and inventors. Studies show that the best predictor of STEM career choice in adulthood is linked to whether kids self-report seeing themselves as scientists when they grow up by 8th grade (Maltese & Tai, 2011). We have to trust that allowing our students to tinker, question and invent, as early as elementary and middle school, will help them to develop positive identities that encourage a lifelong love of science, math and the creative process. Making learning “hard fun” (Papert, 2002) is a real-world balancing act that happens everyday when children are designing and inventing in the classroom.making-science-cover
Benefit No. 2: If creative confidence, collaboration, self-reliance, resilience and communication are key to being a scientist, then teaching design and engineering in science class is more effective than content-centered or teacher-directed methods.

Benefit No. 3: In an age where school is becoming less relevant to students, invention and design are an engaging way to learn.
Solving real problems provides students with opportunities to identify with problems that matter, diagnose, defend an argument with evidence, give and receive feedback, utilize and critique internet resources, compose professional emails to mentors and more. Well-designed open-ended challenges versus rigidly planned lessons allow children to do real work in a controlled environment with the help of a learning community. Ownership is given to the learner, while the teacher serves as facilitator. The design aspect turns agency over to students and they become active creators, rather than passive consumers who simply follow directions. Assessment is real time and authentic.
Today, science literacy has become available to more kinds of learners. Educational YouTubers, science storytelling shows like WNYC’s Radiolab, and television shows such as the Mythbusters illustrate the beauty and coolness of science where some traditional science classes fail. These informal educational outlets do a good job spreading science literacy to the general public in a joyful and engaging manner. Some even go so far as to reinforce what we teach in science class — that science is both fun and methodical. Adam Savage of Mythbusters is famous for saying that it’s just screwing around if you don’t write it down. Just like interacting with a well-designed museum exhibit, or setting stuff on fire in your backyard, school should be exploratory and joyful (but safe). Joy and laughter should be welcome in any classroom. Joy relieves stress and allows for healthy goal-setting in a classroom infused with potential dead ends and frustration (Bennett, et.al., 2003; Cornett 1986). Inventing is hands on, minds on, hearts on.
Benefit No. 4: Science is shareable, so is making an artifact.
christa-floresAllowing design and making in science classes results in students having conversations about their shared work and reinforces the importance of documenting the testing process because you don’t want to make the same mistake twice. Communication with peers and mentors is critical to getting over obstacles and improving designs. This mirrors real-world science, where communication is critical to getting support for your ideas. At the Alan Alda Center for Communicating Science at Stony Brook University this idea is part of their mission, “The ability to communicate directly and vividly can enhance scientists’ career prospects, helping them secure funding, collaborate across disciplines, compete for positions, and serve as effective teachers” (Stony Brook University, 2015). Once artifacts are created, most students are happy to share their work with others in public showcases where their process story becomes a point of pride. Unlike taking tests or writing a lab report, sharing work as a form of assessment allows students to gain a sense of identity around STEM topics, as students see their hard work mirrored back at them through the eyes and questions of an eager and engaged audience.
Benefit No. 5: Using design to address or engage real problems empowers students to think of themselves as having the capacity to make the world better.
Thanks to research on the impact and implication of making in education, such as that done by the aptly named Agency by Design (AbD), a project housed within Harvard’s Project Zero umbrella, research on the value of making in educational settings is now being published. Early findings from the AbD group show that a valuable sense of self is developed when children are allowed to make, invent and tinker. This sense of self, or “maker empowerment,” is a person’s ability to see the opportunity in their environment both for making things and for making change in the world. AbD defines maker empowerment as “a sensitivity to the designed dimension of objects and systems, along with the inclination and capacity to shape one’s world through building, tinkering, re/designing, or hacking” (Agency by Design, 2015a). Others would just call this creativity, mindfulness or resourcefulness. No matter what you call it, we want students to experience learning that requires them to look closely at the objects they interact with, explore the complexity of those objects, make deep connections, and to dream big while they develop agency to make change in the world around them.
In summary, the use of the design process in school is a creative exploration of hard, yet fun problems (rigor, risk and reward), positive identity formation (“I am creative,” “I am a scientist,” “I can solve problems”) and collaborative learning (“we are greater than me”). Add responsible resource management and exposure to social justice issues, and design becomes a tool for innovation, empowerment and stewardship. Using design and engineering in science trains brains to think flexibly, to see layers of complexity in the environment all around, to discover loopholes in assumed truths and to look for opportunity to make the world a better place.
Christa Flores is an anthropologist turned science and making teacher. She develops classroom-tested lessons and resources for learning by making and design in the middle grades and beyond. Making Science offers project ideas, connections to the new Next Generation Science Standards, assessment strategies, examples of student work and practical tips for educators.

Monday, 2 October 2017

What artificial brains can teach us about how our real brains learn


Psychologists are simulating neural networks to understand how we learn.
DIUNO/ISTOCKPHOTO.COM

What artificial brains can teach us about how our real brains learn


Studying the human mind is tough. You can ask people how they think, but they often don’t know. You can scan their brains, but the tools are blunt. You can damage their brains and watch what happens, but they don’t take kindly to that. So even a task as supposedly simple as the first step in reading—recognizing letters on a page—keeps scientists guessing.
Now, psychologists are using artificial intelligence (AI) to probe how our minds actually work. Marco Zorzi, a psychologist at the University of Padua in Italy, used artificial neural networks to show how the brain might “hijack” existing connections in the visual cortex to recognize the letters of the alphabet, he and colleagues reported last month in Nature Human Behaviour. Zorzi spoke with Science about the study and about his other work. This interview has been edited for brevity and clarity.
Q: What did you learn in your study of letter perception?
A: We first trained the model on patches of natural images, of trees and mountains, and then this knowledge becomes a vocabulary of basic visual features the network uses to learn about letter shapes. This idea of “neural recycling” has been around for some time, but as far as I know this is the first demonstration where you actually gained in performance: We saw better letter recognition in a model that trained on natural images than one that didn’t. Recycling makes learning letters much faster compared to the same network without recycling. It gives the network a head start.
Q: How does the training work?
A: It uses “unsupervised” learning. After pretraining on the natural images, we feed the neural network unlabeled images of letters. The goal is simply to build an internal model of the data, to find the latent structure. It’s called “generative” because it’s generating patterns from the top down. It uses the knowledge it has learned to interpret the new incoming sensory information.
Later, a simpler algorithm learns to put letter labels on that network’s outputs. This one uses “supervised” learning—we tell it when it’s right and wrong—but most of the work was done by the unsupervised algorithm.
Q: Why focus on unsupervised learning, which is much less common in AI?
A: With supervised learning, you are assuming that you have a teacher providing the correct label at each learning event. Think about how we humans learn. This very rarely happens.
Supervised learning is a feed-forward, bottom-up approach, unlike the top-down approach of unsupervised learning. There are a lot of feedback connections in the brain. Moreover, there is intrinsic activity in the brain, which is one of the more interesting findings of last 20 years or so in neuroimaging. It’s not generated by sensory stimuli. Intrinsic activity can only come from activating neurons in high layers and then propagating this activity back and forth around the network. It can be described as a form of “dreaming” or “imagery.” When combined with sensory activity, top-down feedback leads to interpretation of the input. For example, if a written word is partially blocked, readers can fill in what they don’t see based on what they expect.
The other advantage of unsupervised learning is that since there is no assigned task, knowledge is not tied to a specific application. It’s easy to learn a new task by using this higher-level knowledge. An example is that learning what numbers mean is later applied to learning arithmetic.
Q: The part of your network trained on natural images was still more responsive to images of real letters versus made-up ones. Does that mean real letters somehow resemble nature?
A: Yes, this is one explanation. There’s this hypothesis that has been around for some time that the shapes of symbols across all writing systems have been culturally selected to better match the statistics of our visual environment. You can think about this in terms of the type of shapes needed to better suit brains trained on nature.
Q: What else have you learned about human cognition?
A: We know that babies and animals can compare numbers of objects even without labels. We found that deep unsupervised learning on images containing different numbers of objects yields this visual number sense in a neural network. It was the first study using deep learning for cognitive modeling.
With neural networks, you have a learning algorithm. You can try to map the learning trajectory of the network onto human developmental data. Take something like learning to read. If you have a computer model that learns to read, you may also try to understand atypical learning, as in dyslexia.
Q: What have you found about dyslexia?
A: There’s a huge debate. What is the core deficit? People have looked at phonological, visual, and attentional deficits. We tested these hypotheses in a computer model of reading development. In a study that has not been published, we observed that if you don’t assume that dyslexia is caused by more than one deficit, there’s no way to explain the diversity in real dyslexic children. Where this approach is going is to try to build personalized models of individuals and use the simulations to predict the outcomes of interventions.
Q: Could simulating the brain like this also improve AI?
A: I think so. Bringing in more constraints from the information we have about the brain and how people learn can give us some new ideas on how to explore new learning solutions. 

Thursday, 7 September 2017

eTwinning - przewodnik

Witaj na mini stronie zawierającej materiały samokształceniowe poświęcone eTwinningowi


Materiały samokształceniowe pozostają do dyspozycji zarejestrowanych nauczycieli i powiązane są z systemem badającym bieżące postępy. Materiały samokształceniowe zostały przygotowane w celu wspierania postępów nauczycieli oraz motywowania ich do zgłębiania obszarów, po których się poruszają.