Why Computer Science Belongs in Every Science Teacher’s Classroom

By Sheena Vaidyanathan (Columnist)

Why Computer Science Belongs in Every Science Teacher’s Classroom

During the summer, I taught a computer science course for educators at the Krause Center for Innovation at Foothill College. Funded by Google’s CS4HS grant, this was a four-day intensive “crash course” for 60 teachers in the San Francisco Bay Area. Within that group were science teachers who decided to spend their summer break learning how to incorporate computer science into their classes. This would not only engage their students in science topics, but more importantly, it would bring many of the Next Generation Science Standards (NGSS) practices to life. Released in 2013, the NGSS was created to align science education with how scientists actually work and think. It encourages students to learn science content and concepts deeply by using critical thinking and primary investigation skills. Adopted by 18 states (with as many as 40 interested and in the process), the standards define science education through core concepts (such as wave properties), practices (like analyzing and interpreting data ) and crosscutting concepts (like cause-and-effect). Some of the NGSS guidelines directly overlap with the practices listed in the K-12 Computer Science framework and the new CSTA Computer Science standards. Here’s a doodle that illustrates how the two subjects overlap.

For more, check out the diagram at bottom of this page on the K-12 CS framework website. So if you want to implement NGSS, consider adding computer science to your science classes in the following ways.

Analyzing data

Analyzing large data sets is an important topic in computer science. Every day, we generate data directly or indirectly as we use our phones, shop online or go for a walk with wearables strapped to our wrists. Understanding how data is stored, and how to analyze it using computing, will make this information useful. Scientists often spend more time analyzing their lab data than running experiments. They use existing computational tools or write code for their specific needs. So it is no surprise that data features prominently in two of the eight NGSS practices (Analyzing and Interpreting Data; Using Mathematics and Computational Thinking). In my summer CS professional development program, teachers learned how to run computations on spreadsheets before sorting and graphing the data. Science teachers explored publicly available data sets available at data.gov to find data on science topics such as earthquakes and weather.

As an example, students can analyze extreme weather data in their local area and then graph the number of times a certain condition is reached. The screenshot above examines extreme weather from counties in my area. I used this data to find out how many times in the last 20 years, heavy rain caused more than $50,000 in property damage. The COUNTIFS function in Google Sheets counts the number of times multiple conditions are met. In my example, the conditions are heavy rain and damage exceeding $50,000. The formula for counting that is:

COUNTIFS(E1:E900,”=Heavy Rain”, G1:G900, “> 50000”)

My analysis showed that high wind, not heavy rain, is the weather event in my area that causes the most damage. Students can run similar computations on actual climate data and then sort or graph results to test their hypothesis on what weather causes damage in their areas.

Building simulations using block- or text-based computer models

There are many science experiments that cannot be replicated in a classroom—natural ecosystems, radioactivity or natural selection, for example. Such topics can only be addressed using a simulation, where students can set up a hypothesis, manipulate variables, test their ideas, collect data, and then analyze and graph their results. Simulations offer a useful way for teachers to address the “Developing and Using Abstractions” and “Creating Computational Artifacts” practices in the K-12 Computer Science framework. For example, a science project might involving creating a simple producer–consumer ecosystem model using a programming tool. Students might build a computer model of an ocean ecosystem and test the complex adaptive behaviors that occur when fish population changes due to changes in food, temperature, pollution or number of predators. Two of the NGSS practices—“Developing and using models” and “Using mathematics and computational thinking”—can also be addressed using simulations. Science teachers may be glad to learn that they do not have to be expert coders and create the computer model with original code. Several ready-to-use simulation models are available. With some professional development, teachers can show students how to run the simulations and then read and modify some of the code to extend their experiments.

MIT’s Starlogo Nova is a simulation tool for the K-12 classroom. It uses an easy, block-based language similar to MIT’s Scratch (and this September released a major upgrade to HTML5). Several ready-to-use models, as well as detailed lesson plans, can be found at the Project Guts curriculum site, and also through Code.org’s CS in Science. The screenshot above shows an ecosystems model of rabbits and grass from this curriculum. (Note that the graph changes, showing data on number of rabbits and grass as the simulation runs.)

NetLogo is a text-based tool that can offer K-12 teachers a powerful modeling environment. It is used for research and at colleges and can be downloaded or used via a web browser. The screenshot above shows a model where students can manipulate amplitude and frequency to study wave patterns.

Science teachers can also use the popular block language, Scratch, to let students build their own simple computer models to represent scientific phenomena such as water cycles and chemical reactions. In California’s Los Altos School District, where I work as a computer science integration specialist, some third graders use Scratch to make a food chain model. (See the sample student project screenshot above). Fifth graders also connect Scratch with Makey Makey to turn their physical science models, made from cardboard, into an interactive model that lists facts or quizzes the viewer.

Integrating CS in science class gets us closer to CS for ALL

In addition to implementing the NGSS guidelines, integrating CS into science classes will help schools introduce computer science to every student without finding additional class time or computer science teachers. It also gives every student exposure to coding—even those who may not think computer science is for them: minorities, girls, English language learners. Once students see that CS concepts apply to every subject, they will see it as a tool to solve problems. Coding also encourages trial and error, thus offering a fun, experimental approach to learning. Later on, this exposure can motivate a student to take a CS class if it is offered at their schools. Above all, introducing computer science projects and activities can bring a new, experimental hands-on element to many science lessons. This integrative approach also helps students who do not have a CS elective in their school, or who cannot add a CS elective due to schedule conflicts or space availability. (Many schools have limited CS elective classes due to a teacher shortage.) Integrating CS principles into science classes is feasible. Teachers will need professional development, and ongoing support from their administrators, other teachers, parents, and from a professional learning community. Where possible, a computer science teacher or a computer science integration coach can help bridge the gap. At KCI, Foothill College, we plan to offer Saturday workshops throughout the year, and more professional development sessions every summer to help teachers learn CS concepts. I hope science teachers looking to implement NGSS will integrate these ideas in their classroom and give students a glimpse into the world of computer science.

Read the original article here


Building A.I. That Can Build A.I.

SAN FRANCISCO — They are a dream of researchers but perhaps a nightmare for
highly skilled computer programmers: artificially intelligent machines that can build other artificially intelligent machines. With recent speeches in both Silicon Valley and China, Jeff Dean, one of Google’s leading engineers, spotlighted a Google project called AutoML. ML is short for machine learning, referring to computer algorithms that can learn to perform particular tasks on their own by analyzing data. AutoML, in turn, is a machine-learning algorithm that learns to build other machine-learning algorithms.
With it, Google may soon find a way to create A.I. technology that can partly take the humans out of building the A.I. systems that many believe are the future of the technology industry. The project is part of a much larger effort to bring the latest and greatest A.I. techniques to a wider collection of companies and software developers.
The tech industry is promising everything from smartphone apps that can recognize
faces to cars that can drive on their own. But by some estimates, only 10,000 people worldwide have the education, experience and talent needed to build the complex
and sometimes mysterious mathematical algorithms that will drive this new breed of artificial intelligence. The world’s largest tech businesses, including Google, Facebook and Microsoft, sometimes pay millions of dollars a year to A.I. experts, effectively cornering the market for this hard-to-find talent. The shortage isn’t going away anytime soon, just because mastering these skills takes years of work. The industry is not willing to wait. Companies are developing all sorts of tools that will make it easier for any operation to build its own A.I. software, including things like image and speech recognition services and online chatbots. “We are following the same path that computer science has followed with every new type of technology,” said Joseph Sirosh, a vice president at Microsoft, which recently unveiled a tool to help coders build deep neural networks, a type of computer algorithm that is driving much of the recent progress in the A.I. field. “We are eliminating a lot of the heavy lifting.” This is not altruism. Researchers like Mr. Dean believe that if more people and companies are working on artificial intelligence, it will propel their own research. At the same time, companies like Google, Amazon and Microsoft see serious money in the trend that Mr. Sirosh described. All of them are selling cloud-computing services that can help other businesses and developers build A.I. “There is real demand for this,” said Matt Scott, a co-founder and the chief technical officer of Malong, a start-up in China that offers similar services. “And the tools are not yet satisfying all the demand.” This is most likely what Google has in mind for AutoML, as the company continues to hail the project’s progress. Google’s chief executive, Sundar Pichai, boasted about AutoML last month while unveiling a new Android smartphone. Eventually, the Google project will help companies build systems with artificial intelligence even if they don’t have extensive expertise, Mr. Dean said. Today, he estimated, no more than a few thousand companies have the right talent for building A.I., but many more have the necessary data. “We want to go from thousands of organizations solving machine learning problems to millions,” he said. Google is investing heavily in cloud-computing services — services that help other businesses build and run software — which it expects to be one of its primary economic engines in the years to come. And after snapping up such a large portion of the worlds top A.I researchers, it has a means of jump-starting this engine. Neural networks are rapidly accelerating the development of A.I. Rather than building an image-recognition service or a language translation app by hand, one line of code at a time, engineers can much more quickly build an algorithm that learns tasks on its own. By analyzing the sounds in a vast collection of old technical support calls, for instance, a machine-learning algorithm can learn to recognize spoken words. But building a neural network is not like building a website or some run-of-the-mill smartphone app. It requires significant math skills, extreme trial and error, and a fair amount of intuition. Jean-Fransois Gagna, the chief executive of an independent machine-learning lab called Element AI, refers to the process as “a new kind of computer programming. In building a neural network, researchers run dozens or even hundreds of experiments across a vast network of machines, testing how well an algorithm can learn a task like recognizing an image or translating from one language to another. Then they adjust particular parts of the algorithm over and over again, until they settle on something that works. Some call it a “dark art,” just because researchers find it difficult to explain why they make particular adjustments. But with AutoML, Google is trying to automate this process. It is building algorithms that analyze the development of other algorithms, learning which methods are successful and which are not. Eventually, they learn to build more effective machine learning. Google said AutoML could now build algorithms that, in some cases, identified objects in photos more accurately than services built solely by human experts. Barret Zoph, one of the Google researchers behind the project, believes that the same method will eventually work well for other tasks, like speech recognition or machine translation. This is not always an easy thing to wrap your head around. But it is part of a significant trend in A.I. research. Experts call it “learning to learn” or “meta-learning.” Many believe such methods will significantly accelerate the progress of A.I. in both the online and physical worlds. At the University of California, Berkeley, researchers are building techniques that could allow robots to learn new tasks based on what they have learned in the past. “Computers are going to invent the algorithms for us, essentially,” said a Berkeley professor, Pieter Abbeel. “Algorithms invented by computers can solve many, many problems very quickly — at least that is the hope.” This is also a way of expanding the number of people and businesses that can build artificial intelligence. These methods will not replace A.I. researchers entirely. Experts, like those at Google, must still do much of the important design work. But the belief is that the work of a few experts can help many others build their own software. Renato Negrinho, a researcher at Carnegie Mellon University who is exploring technology similar to AutoML, said this was not a reality today but should be in the years to come. “It is just a matter of when,” he said.

See the whole article here

Tech lobby thrilled about computer coding in schools

People who work in Saskatchewan’s technology sector are applauding the provincial government’s pledge to introduce new computer coding courses in elementary and high schools, hoping to solve an industry-wide labour crunch. The province’s tech sector is still comparatively small, but rapid growth has resulted in a shortage of experienced software developers, and the problem is expected to get worse, according to a spokesman for a new industry lobby group. “We can identify several hundred open jobs right now,” said Aaron Genest, who works for the computer chip developer Solido Design Automation Inc. and speaks for SaskTech, which represents more than 40 companies with about 5,000 employees. “It’s an early indicator of the challenges that we’re going to face in 10, 15, 25 years … In the long term, we need to prepare our children to see (computer science) programs as part of their future.” The Saskatchewan Party


SaskTech spokesman Aaron Genest in the Saskatoon offices of Solido Design Automation Inc.

government committed to developing the curriculum in its throne speech, which was read in the legislature on Wednesday. It said the courses will prepare children for careers in science, engineering and technology.  The promise emerged from consultations with SaskTech and the broader industry.  Education Minister Bronwyn Eyre said this week that while the mechanics have yet to be worked out, she would like to see the courses being taught “as soon as possible.” She declined to provide a specific timeline but said enthusiasm for the proposal is widespread. The main challenge is a shortage of qualified teachers, the Stonebridge–Dakota MLA said. Saskatchewan only has about 70 teachers qualified to instruct high school students in computer science, and the province’s two education colleges must work to increase that number, she said. Saskatchewan’s 28 school boards have spent the last six months grappling with a 1.2 per cent, or $22 million, operational funding reduction handed down in the government’s unpopular 2017-18 budget, which aims to halve a $1.2 billion deficit this year. Eyre said the province’s financial situation has “no relevance” to the development of coding courses. “Now that the focus is there, and so the resources will, I’ve been assured, fall into place,” she said.  Michelle Naidu, associate director of development for the Saskatchewan Teachers Federation, noted it takes years to develop new courses, and professional development resources are already scarce; however, she said the proposed courses could benefit students. “Computational thinking is going to start showing up in all kinds of jobs as we move away from people doing work,” said Naidu, who is also the president of the Saskatchewan Math Teachers Society. “It’s really hard to predict the future, but everyone seems to be very happy to understand that technology is going to play a larger role in everyone’s future, and so that understanding of the basics of how that works is to everyone’s advantage.” Genest said SaskTech is thrilled the government was open to considering the industry’s proposals, and that while introducing the courses will take time it signals a willingness to boost an emerging sector in the provincial economy.  “It means that they’re committing to a homegrown solution to it (so) that Saskatchewan citizens are going to be able to step in and fill the gap in a technology-driven future.” Measuring the size of the province’s tech sector is difficult, as its work is diverse and often overlaps with other industries. However, the provincial government estimates its economic impact is around $540 million — just under one per cent of the provincial GDP. “Absolutely, it has economic potential,” Eyre said of the proposal. “And absolutely that’s why we’re doing it. We need to take our place as a province that offers this to our students.”