Integrating Computer Science Across the Core

Strategies for K-12 Districts

Tom Liam Lynch, Gerald Ardito, Pam Amendola

• Publisher: Routledge
• Year: 2020
• ISBN:
  9780429516931
• Edition: 1
• Topic: Education
• Subtopic: Education General

Integrating Computer Science Across the Core is a guide to systematizing computer science and computational thinking practices in your school. While most books explain how to teach computer science as a stand-alone discipline, this innovative approach will help you leverage your existing curriculum to deepen and expand students' learning experiences in all content areas. Effective, equitable, and sustainable, this blueprint provides principals, curriculum directors, directors of technology, and other members of your school or district leadership team with suggested organizational structures, tips for professional learning, and key resources like planning instruments.

We are tempted to assume that you think computer science is worth teaching in K–12 settings. After all, you have opened a book about that very topic. It would be safe to think that you need little convincing. But people open books like this for different reasons. Sure, you yourself might have an intrinsic interest in coding or robotics or things like that. It is also possible that someone else asked (or told) you to read this book. We get it. No judgement either way.

So, let's do this a little differently. Let's assume that you don't necessarily believe computer science has any place in K–12 schools. Let's assume that some readers might not even be sure what computer science means. A couple paragraphs into the first page, most readers are naturally skeptical–not sure they're 100 percent committed to the chapters that follow. To be fair, we, the authors, are skeptical about you, too. That's why we are going to level with you: We have little interest in computer science for its own sake. Not in this book.

Then why did we write it and why might it be worth your time? Give us a few more pages of your attention and we will explain.

We want to be transparent about why we believe we have a compulsory school system in the first place. In our experience, it is easy for teachers, parents, administrators, and elected officials to expound on the virtues or shortcomings of school without ever being clear about why schools are important. That is understandable when you realize that unlike most other nations to which we compare ourselves, the United States does not define the purpose of public education in the national Constitution. (No kidding. Google the Constitution and search around. You won't find words like school or learning or teaching or anything like that.) As a result, the purpose of our schools appears in state constitutions, but that means there are fifty different articulations of why we educate children. Some articulations are specific and thoughtful, preparing children to participate in a democracy or contribute to an economy. Others are more problematic, like the many clauses in state constitutions that really don't say why educating children is important at all. They just say that states will use tax money to do it.

Here's why we believe we educate our children, and it is with this explicit purpose in mind that we frame our entire book. We argue that we have compulsory education in the United States because as a democratic republic, we rely on an informed population to participate in making our communities better. The individual right to vote does our nation little benefit if the individual is not taught to read, to write, to learn about the world, to assess the validity of arguments, and to engage in complex conversations with others about things that matter to us all. Tightly tethered to civic engagement is economic engagement. We educate our children because we want to know that they can contribute to our collective economy, whether it's holding down a steady job and providing for one's family or creating a new technology that changes the world. Civic and economic engagement—that is why we believe we have schools and that is also why we believe computer science must be meaningfully introduced into K–12 spaces.

Over the last twenty years, digital technologies have revolutionized the way so much of our society operates. First, there is the basic stuff most of us have begun to take for granted. We send messages to each other in an instant, share ideas and pictures, book tickets, and stream entertainment. Much of this happens on our phones or via ever-flattening computers. Second, there is the bigger stuff that happens digitally that we tend to think less about on a daily basis, like our banking systems, the entirety of the aviation industry, identification management, and medical record keeping. Digital technologies now mediate our individual and collective lives in ubiquitous, hidden, speedy, and wide-scaled ways.

With the spread of digital technologies has come new opportunities. In some parts of the world, you can order virtually any good or service you need from your phone and it will be provided in hours, if not minutes. You don't exchange money with the provider directly; it's all handled digitally. In other parts of the world, communities whose governments have not yet provided a basic communication infrastructure—either because they cannot or choose not to—can now access information via the Internet using very simple and inexpensive mobile phones. Digital technologies are credited with making democratic revolutions possible in countries like Egypt and Tunisia. The same technologies can make it possible for anybody, anywhere to learn something they need or want.

Digital technologies have become so much a part of our daily experience that we forget how little the average person really understands about how they work, who makes them work, and why. It was a lack of understanding that was put on full display in the wake of the 2016 presidential elections in the United States, when it became increasingly evident that social media platforms like Facebook and Twitter were used by foreign powers to stoke division among American voters. What's more, few understand just how much digital data are generated and collected about individuals today. Companies have been able to leverage more data than ever before in order to market to consumers. In many states, schools have also been encouraged to digitize more of their instructional and operational practices. There are mobile apps dedicated to classroom management and parental communication, web-based apps for running blended classrooms, and paper-based textbooks are on the decline. For administrators, grades are increasingly submitted via digital systems, student records are collected and shared via third-party products, and professional learning opportunities are increasingly offered and tracked online.

It all seems so normal. But how many readers know what data companies collect about our children and teachers and what they do with them? How many readers know how to spot a suspicious and inflammatory post in their social media feed? How many realize that when they share a picture online, they are sharing many dozen types of personal data with the company who owns the platform and device?

Our point is to say that today what it means to prepare young people for civic and economic engagement requires that they critically understand the way digital technologies enable and inhibit such engagement. Senior officials have warned of the danger of election systems being hacked throughout the world. But do our soon-to-be-voting children know what “hacking” an election consists of? Economic experts have foreshadowed that jobs across industries and pay scales will increasingly require at least a basic, if not an advanced, understanding of how to communicate with computers. But what happens when computer science as a subject is disproportionately offered to economically advantaged students or as a single elective or Advanced Placement course? We don't advocate computer science in K–12 schools and districts because it is in vogue or trendy. We advocate computer science in schools because we believe the future improvement of our society requires it. No hyperbole.

In our experience, there are two problems with K–12 computer science education in the United States. The first problem is computer. The second problem is science. Here's what we mean when we say that computer and science are so problematic:

1. Computationality is not about “computers,” per se. It is about inquiry, logic, and languages. By overemphasizing the word “computer,” advocates have deemphasized some important things about digital technologies. First, you can learn about computationality without computers. Electricity isn't necessary. Second, computers don't appear out of the ether. Human beings build them, program them, and network them. Those human beings have different motivations for doing so, and any particular way they do it could always look differently than it does. We suggest forgetting about “computers” and think about computationality instead, including its distant etymological cousins composition and communication. When it comes down to it, computer science is fundamentally about systematically and logically communicating with machines—as well as how machines increasingly communicate with us (Frabetti, 2015). It is about how human beings compose instructions to tell machines what to do—and it is about realizing how computationality shapes the world around us. Once you realize that, you will begin to see that insofar as you and your students are fluent in communicating in any language, you already have the foundation to communicate with computers. It's all just inquiry and logic and language. At a talk in Seoul a few years ago, Tom told a group of several hundred English-language teachers that they in fact had comparable expertise in teaching coding in schools as computer science professors. Coding is just a form of writing, a way to communicate with a particular mechanical audience (Vee, 2017). Who better to help students learn to code than language teachers? There were a few claps, but most of the attendees remained unconvinced. But Tom meant it. Still does.
2. Computationality is not about “science.” It can deepen and expand learning in all grades and disciplines. It might surprise you to know that the field of K–12 computer science is young compared with other subjects like secondary mathematics and English. In their eagerness to support schools in this important work, advocates sometimes treat computationality in an overly narrow way, limiting their paradigms to those of computer scientists and technology industry experts. The problems with the resultant paradigm are manifold. First, it means that computer science is framed as an external discipline that has to be introduced anew to existing K–12 school curricula and structures. But most schools already have a dense curriculum and tight schedules, and the traditional structures by which schools operate simply don't change swiftly. Ask a science or math teacher how much room in their curricula they have to include meaningful and extended activities in computer science. Watch them sweat. Second, and relatedly, by framing computer science as a science, it means that non-STEM (science, technology, engineering, math) subjects are deemphasized as sites of computational study. And yet there are rich and engaging ways to embed computationality into the humanities, ways that deepen and expand disciplinary study. In short: forget computers and forget science. Think in terms of computationality. We believe that computational methods have a place across grades and disciplines and that embedding such methods into one's classroom can deepen and expand one's practice (Lynch, 2017).

Here's the paradox of K–12 computer science education: If you really believe that computer science is important for young people to learn, then it might be best to stop talking so much about computers and the sciences! Nevertheless, it is a familiar term and one we will use generously to refer to ways of thinking, solving problems, creating, and communicating that empower teachers and students to better understand how computers operate in the world. We will also use other terms at times, like computational thinking, computationality, and computational methods. In this book, those words are used mostly interchangeably. To better appreciate how computer science (see, we're back to using it already!) has been framed in schools currently, let's look at its emergence on the national stage in the United States and how the nation's largest school district attempted to introduce computer science to 1,800 schools at scale.

An Official, Top-Down Approach

In December 2014, President Barack Obama became the first president to write computer code as part of the White House's promotion of Computer Science Education Week and its Computer Science for All (CS4All) initiative (Finley, 2014). Nine months later, the nation's largest school district announced its plans to provide computer science education to its 1.1 million students and 80,000 teachers (Taylor & Miller, 2015). New York City might not be representative of many other districts, but nevertheless it serves as a case study for how computer science has emerged in American schools. Two main efforts took root in New York City to operationalize CS4All: a centralized and official top-down model and a decentralized and unofficial bottom-up model. The first effort was the city's establishment of a formal team in its central offices devoted to K–12 computer science education. The team focused on several ways to support schools, including: designing an accessible K–12 computer science framework for the city's teachers, soliciting sample curricula, and building community via social media.

Accessing K–12 Computer Science Frameworks

Multiple sets of K–12 computer science standards already exist. Two of the main ones in use are provided by the International Society for Technology in Education (ISTE) and another set created by the Computer Science Teachers Association (CSTA). The ISTE Computer Science Educator standards are divided into four areas: knowledge of [computer science] content, effective teaching and learning strategies, effective learning environments, and effective professional knowledge and skills. The CSTA standards (Seehorn et al., 2011) are somewhat more detailed and are organized into two areas: concepts and practices. Concepts include computing systems, networks and the Internet, data and analysis, algorithms and programming, and impacts of computing. Practices include fostering an inclusive computing culture, collaborating around computing, recognizing and defining computational problems, developing and using abstractions, creating computational artifacts, testing and refining computational artifacts, and communicating about computing. New York City's team felt that those two standards sets were aimed at teachers who taught computer science as an isolated content area. Their concern was that if computer science was going to be truly scaled in K–12 classrooms, there needed to be a framework for ...