I’m amped to share this rough draft that will frame the book in the works with three fantastic co-authors. (We’re writing in the third-person to help the chapters cohere.) Feedback welcome in comments or via social. 🙂 -TLL

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Introduction

There is something to be said for getting lost in one’s own town. The navigation app on Tom’s phone said the trip would take forty minutes. He left over an hour ahead of time. Walking through the pre-dawn familiar streets to the subway, he boarded the D train at 125th street and was whipped along to his destination in plenty of time. Disembarking at Grand Street, Tom had time to spare. So he sat on a bench in the station and read the last chapter in an unfinished novel for his book club.

Checking his watch, Tom hurried upstairs ready to walk the couple blocks to the high school where he was scheduled to run a morning workshop.  As he emerged at street level, though, he was struck by what can only be described as a linguistic hit-and-run. A man was yelling (Tom assumed at him) in a language Tom didn’t understand. As he looked around, Tom’s eyes were knocked into a state of alphabetic disorientation, the sweeping calligraphy of unfamiliar characters seemed to be everywhere. He began walking briskly now, as you can imagine, in the direction where he believed the school to be. After a few blocks, Tom realized he hadn’t recognized English words anywhere. While the street signs were bilingual, the presence of English was overshadowed by the sheer dominance of Mandarin.

Chinatown was not Tom’s intended destination, but the school is in a part of Manhattan where several neighborhoods overlap. Lower Manhattan predates the neat and tidy grid system that defines the island north of 14th street. The East Village, the Lower East Side, Chinatown, Little Italy—they all blur together. Tom was in old Manhattan. Old, as in streets crisscross illogically to intentionally confuse invading British troops. Old, like that’s the spot where Boss Tweed rigged elections. Old, like centuries of stories of war and corruption and ambition and hope and love, and the myriad dialects and languages in which such stories were told.

Tom found the entrance to the school. The security guard commented on his ensemble, noting Tom’s light blue shirt and dark blue pants resembled her own uniform. Tom smiled, raised his foot in the air, and asked if his polished brown leather shoes with red laces are standard issue for school safety officers. The officer laughed, also a believer in the power of unexpected bursts of color in the world. Tom navigated the halls, huffed up the stairs, and was soon thereafter standing before a group of ten teachers, mostly English, but also special education, math, and physics. Apparently, the title of the workshop attracted a respectable interdisciplinary crowd: “An Introduction to Integrating Computer Science into ELA.”

Key Concepts

In what follows, we wish to frame what we mean by computer science, what it looks like in action, and why it’s important. Some readers will see the term “computer science” and be tempted to immediately put the book down. Please don’t. Give us a chance to first explain what it is and what it is not, beginning by dispelling two common misperceptions: that computer science is a separate STEM subject and that computer science is for technology nerds.

1. Computer science is not a STEM subject. It can deepen and expand learning in all grades and disciplines. In a talk Tom gave in Seoul a few years ago, he began by saying that there are two problems with K-12 computer science in the United States (Lynch, 2017). The first problem is computer; the second problem is science. What he meant was that there are too many of us who do not identify as overly computer-loving nor do many of us identify as particularly scientific. In fact, it might surprise you to know that the field of K-12 computer science is relatively young compared with secondary mathematics and English, for instance. Get a group of people who work in computing in one form or another and watch the debate ensue as to what computer science even means. You might currently find the term mysterious, but it’s actually fairly broad and ambiguous.  For our purposes, we use the term computer science 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. In short: forget computers and forget science. Think in terms of computationality. We believe that computational methods have a place across grades and disciplines, that embedding such methods into one’s classroom can deepen and expand one’s practice.
2. Computer science is more than a technical subject. It’s about inquiry, logic, and language. It is really helpful to think about computationality in relation to 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 the same talk in Seoul, 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 that language teachers? There were a few claps, but most of the attendees remained unconvinced. Still, Tom meant it. Still does. Computationality is about methods of inquiry, logic, and language.

There is more we will unravel in the coming chapters. But before we do, let us return to that classroom in Chinatown where Tom was out of breath and full of ideas.

A Story of Practice

Tom began his workshop with a picture. It was an image of the gates of Columbia University at 116th Street and Broadway in New York. As you observe the gateway, which is called College Walk, there are two statues flanking the entry to campus. On the left, there is a male figure wearing a robe, his bare chest exposed for all to see. He holds an orb. He is labeled “Sciences.” On the right, there is a female figure wearing a fuller robe. She holds an open book, pages faced out. She is labeled “Letters.” Tom used this image as a way to introduce the idea that, for centuries, we have confidently separated the sciences from the language arts. Today, we tend to associate computer science with the former. The result is that computational methods become the expertise area of STEM professionals whose titles declare science, technology, engineering, or math. The problem with that, however, is that the value of computer science in K-12 spaces is not in the least bit limited to STEM subjects. We do not limit the value of reading and writing to English class, do we? Of course not. That’s because we understand that the use of language to consume and produce texts transcends all disciplines. Similarly, we must not limit the value of computational methods to a subset of disciplines that evoke the image of bare-chested men holding orbs over Broadway.

In Chinatown that morning, the teachers’ interest in the workshop flowed, in part, from a large-scale initiative in New York City schools called Computer Science for All or CS4All. In 2014, the Obama Administration launched CS4All as a nationwide program, kicking it off with a press event in which the president learned to write a simple computer program with students at the White House. He was hailed as Coder in Chief (Finley, 2014). CS4All was not strictly a federal initiative, per se. Instead, it consisted of a network of public, private, and philanthropic partners who joined forces to promote computer science education in K-12 schools. President Obama’s photo op was just one piece. In addition, companies like Google, Amazon, Facebook, and Microsoft all supported raising awareness of computer science in K-12 schools (Google Inc. & Gallup Inc., 2016). Philanthropists like Fred Wilson, the venture capitalist behind Union Square Ventures, worked with elected officials to support public schools in the city at scale. Wilson funded the establishment of two high schools in New York–one in Manhattan, another in the Bronx–that made software engineering a core part of their vision. With broad support, New York mayor Bill de Blasio launched the city’s own CS4All campaign, providing curricular resources and incentives for schools to step up their computer science offerings (Taylor & Miller, 2015). All that buzz likely lured teachers to Tom’s workshop that morning.

“Our goal today is to walk between those two statues flanking the entrance to College Walk,” Tom began, pointing at the picture of Columbia. “Computer science is not the domain of STEM. In fact, it can be employed very meaningfully in the humanities as well. Rather than treat computer science as a discipline in its own right, I want to share with you how computational methods can be used to deepen and expand the ways you already teach (or were taught) literature.”

Deepen and expand content-area instruction. For Tom, that is an oft-repeated phrase. When teachers stop thinking about computer science as a circumscribed discipline and begin thinking in terms of computational methods that can be adapted for the work they already do, pedagogical paradigms shift.

“How many of you have read (or were supposed to have read) Shakespeare’s Romeo and Juliet?” All hands shot up. “Excellent,” Tom went on, “so you all have a general recollection of the story. Well, let me share with you an assessment question similar to the ones you might have encountered in a middle or high school English class.” On the screen flashed a prompt: How does Shakespeare portray the relationship between love and death in Romeo and Juliet? That question seemed straightforward enough. Reading the room, Tom said, “By the looks on your faces, you don’t seem impressed with the prompt, right? It might not strike you as very computational or very scientific. So, let’s add something else.” He clicked on the next slide, which included the original uninspiring question, but now with an addition: Be sure to use both quantitative data (i.e. from the graph and table) and qualitative data (i.e. textual evidence) in your response.

Now Tom saw eyebrows raise.

“Check this out,” Tom went on as he presented a table of data on the screen. “This table shows the frequencies for the keywords love and death in every scene in the play.” (See Table 1.) Each row was labeled with the acts and scenes; each column was labeled for the keywords of interest.

Act / Scene	“Love”	“Death”	Act / Scene	“Love”	“Death”
Act 1, Scene 1	22	4	Act 3, Scene 1	3	3
Act 1, Scene 2	5	0	Act 3, Scene 2	8	6
Act 1, Scene 3	5	0	Act 3, Scene 3	9	12
Act 1, Scene 4	11	1	Act 3, Scene 4	3	0
Act 1, Scene 5	5	0	Act 3, Scene 5	10	5
Act 2, Scene 1	11	1	Act 4, Scene 1	7	6
Act 2, Scene 2	29	2	Act 4, Scene 2	1	0
Act 2, Scene 3	10	1	Act 4, Scene 3	0	1
Act 2, Scene 4	5	0	Act 4, Scene 4	0	0
Act 2, Scene 5	7	1	Act 4, Scene 5	5	10
Act 2, Scene 6	5	1	Act 5, Scene 1	2	2
			Act 5, Scene 2	0	0
			Act 5, Scene 3	9	19
Table 1. A table depicting the word frequency of the words “love” and “death” in Shakespeare’s Romeo and Juliet.

“What you now see is precisely how many times in every scene of the play the words love and death appeared. This doesn’t count for other related words like lovely or topically related words like passion. Just the exact words love and death. That all make sense?”

Heads nodded.  

“Next, I am handing out customized graph paper for a little experiment. You will see on the graph paper that the y-axis is labeled from bottom to top with the numbers 0-30. The x-axis is labeled a bit more strangely. Each tick mark represents a scene in the play. Working in small groups, I am asking you to spend the next ten minutes plotting the data from the table for love and death on your graphs. Use different colors or lines to make it clear in your line graphs which plots are for love and which are for death.”

Figure 1. Customized graph paper in which the x-axis represents every act and scene in Romeo and Juliet.

Participants got to work, frequently looking up at the table on the screen as they plotted their data. Tom overheard one participant sigh, saying that scribbling points on a graph is not what reading literature is all about. Tom agreed completely, but said nothing. The real fun hadn’t started yet.

After ten minutes, Tom took pictures of a few graphs with his phone, sending them up onto the screen so the whole group could see. (See Figure 2.)

“Who’s graph is this one?” Tom asked.

A balding man in a green short-sleeved shirt raised his hand. “It’s mine.”

“Would you talk us through what you see?”

“Sure. At first, there wasn’t much of a surprise really. You can see that in the first two acts of the play the word love is used way more often than death. In the last two acts, the word death is used more often.”

Tom nodded.

“But as my group kept talking about it, we noticed two things that got us talking.”

“What were they?”

“First, we noticed that in Act 3, Scene 1, the two keywords are used the same number of times. One of my colleagues pointed out that it is in that scene that the conflict of the play really heats up when Tybalt kills Mercutio.”

“So what? Why is that interesting?” a voice asked from the back of the room.

Turning behind him, the teacher continued.

“It’s interesting because the numerical data drew our attention to a moment in the play that is actually significant from a literary perspective. The numbers aren’t random. They show patterns very clearly. So the fact that a simple graph could pull our attention to an important scene surprised us.”

Figure 2. A recreation of a typical line graph created by participants in workshops exploring the word frequencies of “love” and “death” in Romeo and Juliet. [TEMPORARY]

Participants began revisiting their own graphs. Some making notes, others erasing marks.

Tom asked, “That’s really intriguing. You said you had two points. What was the second?”

“The second observation is in the last scene of the play. That’s where everything comes to a tragic end with both Romeo and Juliet killing themselves.”

“And what did you see there? I have the same data, same graph even, and I don’t notice anything,” a woman with brown hair inquired.

“Oh, look at Act 5, Scene 3. The word love had really dropped off in frequency throughout the second half of the play. But then, in the last scene the word spikes again. Death is used a lot too, but love comes out of nowhere. Look at the graph: It’s like love is chasing death. That’s a pretty profound observation given the prompt we got.”

Now, all eyes were on the graph on the screen. Two participants got up to look at the plots more closely. One teacher began tracing the lines on his paper with two fingers to see how the two keywords interrelated.

“So,” Tom asked after a couple minutes, “what questions or insights are coming to mind about the relationship between love and death in the play? Talk with your group briefly and then let’s hear what you got.”

Participants talked excitedly for three minutes, pointing to the data table, studying the graphs, and even pulling up digital copies of the play on their laptops and phones. After the volume in the room dipped to a hum, Tom reengaged.

“Who would like to start?”

The speaker for one group reported that they wondered exactly how the words love and death were used in Act 3, Scene 1. Because it was a scene where the two words were used equally, and it was a key scene where a main character dies, they wanted to examine more closely exactly how Shakespeare used them.

“We only got to look briefly. But we did notice that the words are not used like they are earlier in the play. Earlier in the play, when the word love is used, it’s all rainbows and sunshine and bliss. But when love is used in this scene, it’s way more loaded. Look. When Romeo encounters Tybalt early in the scene, he says, ‘Tybalt, the reason that I have to love thee / Doth much excuse the appertaining rage / To such a greeting. Villain am I none. / Therefore farewell; I see thou knowest me not.’ Romeo’s love for Tybalt is confused. It’s a forced love because of Tybalt’s relationship to Juliet. Romeo loves Juliet so he has to love Tybalt. I think their cousins or something.”

At this point, most participants had pulled up the play and were scrolling through the scenes. Some were using the search feature on web pages to find the words love and death to see how they were used, then compared them with what they saw on their line graphs. In a matter of a half hour, that group of teachers had confidently begun to ignore the false separation of science and letters, strutting through the gates with a newfound confidence that computational methods belonged to no single discipline. Certainly not STEM alone. Computationality served only one’s curiosity.

Computational methods are invaluable tools for teachers in any subject area at any grade level. As the participants in Tom’s workshop experienced, even the use of non-digital methods like plotting word frequencies on graph paper can deepen and expand the kinds of questions one asks about literature. To be clear, the value of the activity does not stop at creating the graphs and analyzing the data. The value of the activity, at least to an English teacher, emerges when readers begin asking new kinds of questions about the text, seeing patterns and curiosities that they otherwise might not have noticed so easily. As the workshop demonstrated on a small scale, K-12 computer science can take many shapes and sizes. To illustrate just that variety on a larger scale, let’s look at how the landscape of K-12 computer science took shape in New York City after President Obama launched CS4All and the city’s mayor Bill de Blasio earmarked funding for its expansion.

An Official, Top-Down Approach

As the nation’s largest school district, New York might not be representative of most others with 1.1 million students and 80,000 teachers, but it does serve as a useful case in point. There were two main efforts that 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 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: design an accessible K-12 computer science framework for the city’s teachers, solicit sample curricula, and build community via social media.

Accessing K-12 Computer Science Frameworks

There already exists multiple sets of K-12 computer science standards. 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 standards [ADD CITATION] divide their standards 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 more detailed, 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. The city’s team felt that those two standards sets were aimed at teachers who taught computer 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 teachers across grade-levels and content-areas to integrate computational concepts and methods into the classes they were already teaching. So they created their own framework, referring to the resultant document as The Blueprint. The Blueprint [ADD CITATION] is divided into three main areas: perspectives, practices, and concepts. Perspectives frames learning via different roles students might play, which they call explorers, creators, innovators, and citizens. For practices, the team boiled it down to just three: analyzing, prototyping, and communicating. Finally, the core concepts The Blueprint puts forth are limited to abstraction, algorithms, programming, data, and networks. In each case, teachers can drill down to see in greater details what is meant by the various terms. It is interesting to note that the team saw it necessary to design an entirely new heuristic when others existed already. Their rationale, that other standard sets like ISTE’s and CSTA’s were too focused on computer science as a separate subject, has proven a compelling one. Many public school districts lack the funding to sustain separate computer science teachers, courses, and programs. If officials truly want computer science for all students, then embedding computer science into current courses will likely be much more equitable and sustainable. To do that, an alternative framework like New York’s seems reasonable.

Solicit Sample Curricula

While drafting their Blueprint, the city’s team began soliciting curricular samples from a wide array of partners. Tom and Gerald designed several. The workshop Tom described above grew out of one of the units focused on embedding computational methods into middle and high school English classes. The city’s goal was to amass a library of activities, projects, assignments, and units that teachers throughout the city could adapt or adopt for themselves. The units were aligned with instructional standards like the Common Core and, where possible, made explicit what computer science standards were being evoked. Interestingly, one of the challenges the team faced appeared to be within the city’s own district offices. When Tom presented the Plotting Plots assignment for feedback to the CS4All team, he was surprised to learn that the city’s English Language Arts team would not approve it because it contained “too much English content.” In short, the curricular team themselves did not appreciate just how innovative, strategic, and practical the CS4All team was being. What the CS4All team wanted was precisely what you saw in the aforementioned workshop: teachers using computational methods to deepen and expand their content-area instruction. But what the curricular team wanted was a circumscribed computer science activity that wouldn’t get in the way of teaching English. Still, the CS4All team persisted and ultimately made dozens of curricular resources available to the schools.

Build a Community via Social Media

A final tactic the team used to officially support schools was to build an ongoing conversation online via a weekly Twitter chat. Specifically, the team was interested in engaging teachers in some of the timely and knotty ethical questions that emerged for them when exploring computer science with minors. All under the banner #ethicalcs, the group posted questions and engaged in real-time for an hour each week tackling issues like protecting student data and how information systems can perpetuate school segregation.

An Unofficial, Bottom-Up Approach

The second main effort to operationalize CS4All in New York City came through a non-profit called CSNYC. CSNYC was launched with the support of city and philanthropic funding. A core part of CSNYC’s mission was “To build thriving ecosystems for CS education in NYC and nationwide, CSNYC [ADD CITATION] develops programming across four key areas: community development, teacher pipelines, industry engagement, and research.” CSNYC became the premier connector of individuals and organizations doing work in New York City related to K-12 computer science. They established strategic partnerships, held meet-ups, sent out regular newsletters, and served a constant reminder to others that there was room for many kinds of contributions one could make to the CS4All cause. Tom attended several of their events over the years. The range of curricular resources and services offered was often staggering, ranging from formalized non-profits to impressive individual efforts.  For example, MOUSE is a non-profit that creates a variety of learning experiences for students who live in poverty and are of color. They have a sizable workspace in downtown Manhattan where students come to learn about computing, robotics, electrical engineering, and more. Students might partner with an organization to learn about a pressing need and then design technological prototypes that attempt to solve the organization’s problem. At the other end of the spectrum, take Coding Train. Started by professor Daniel Shiffman, Coding Train is a YouTube channel in which Shiffman teaches anyone how to learn to code. With animations, a lively host, and ever-changing content, the channel has racked up nearly 750K subscribers and the most popular videos have nearly 2M views. Shiffman typically stands at his computer engaging with real-time comments or asynchronous requests. His backdrop is his screen, though, making the videos highly engaging (even if one doesn’t fully understand what he’s doing). There were small organization teaching computational thinking through dance and gaming and Minecraft. The list was endless. Whereas the city’s CS4All team offered official support to schools with a necessarily top-down feel to it, CSNYC worked from the ground up by becoming the force that connected all the smaller efforts that risked getting lost in the vastness of a city of ten million people.

Nations and States Doing Their Own Thing

As we write this, national efforts to formally require K-12 computer science vary widely. Because education is mostly a state-controlled issue, it means that effecting change at scale requires fifty different state governments taking coordinated action. It just doesn’t happen often. Some states are responding to grassroots calls from parents, schools, and the private sector to take computer science more seriously as a formal content-area. New York, for instance, has a proposal on the books to certify computer science teachers at the K-12 level. As of 2018, twenty-two states had academic standards that framed K-12 computer science education. Fifteen states required high schools to make computer science an offered course. Thirty-three states had computer science teaching certification. And fourteen states had dedicated supervisors at the state-level for computer science (EdWeek) [ADD CITATION]. In addition, the College Board revamped its Advanced Placement Computer Science course to de-emphasize narrow coding skills and to underscore core computational principles and practices. Again, what is essential to understand is that there is no one way K-12 computer science looks–certainly not nationally, not within individual states, and not within schools.

Compare what K-12 computer science looks like in the United States to what it looks like in other countries like the United Kingdom, Israel, and New Zealand. In those countries, as in most others, public education is administered nationally from a centralized office. The result is that when a determines that computer science is a necessary subject for school-age children, they develop standards and implement the requirements nationwide. In 2016, Ireland had decided to make computer science compulsory in its schools. The education department conducted a landscape review of other countries that implemented computer science in schools nationally, including the countries named above [ADD CITATION]. They found that despite tight administrative execution, those countries were not seeing an uptick in the number of young people going into computer science. Nor did they see the expected rise in women going into the field. When Tom gave a talk at Trinity College in Dublin that year, he suggested to the group that part of the reason multiple countries are all seeing the same result is–you guessed it–because they are teaching it too narrowly. Rather than just thinking about computer science as a separate subject, countries also needed to acknowledge computational methods as applicable to all content-areas. Do that and you might see different results. A rich discussion ensued with professors from education and computer science weighing in. Ultimately, though, the Irish government proceeded to go about compulsory computer science the same way other nations did. The results are predictable.

In the United States, the decentralized nature of our national school system is a blessing and a curse. It is a blessing because, if other nations’ approaches are representative, we are avoiding going down a very disappointing road where great funding is earmarked for something that doesn’t ultimately manifest. However, the curse is that it means there is no “right” way to implement computer science in K-12 settings. That can be liberating for some schools and daunting for others. There are many ways to do this work, limited only by one’s creativity and resources (strictly in that order), and there are other more research-driven books that might complement the practices discussed here (Kafai & Burke, 2014; Margolis, Estrella, Goode, Holme, & Nao, 2008; Resnick, 2017). In the chapters that follow, we will share with you some of our insights on what we have done and what we might do differently in the futures. Both Tom and Gerald are education professors with expertise in technology in schools. Jennifer is a technology directory for school district north of New York City. And Pam is a high school English teacher in Georgia with some informal background in computer science. Together, we hope to share stories from our teaching that surface insights and principles of practice that can be applied systematically in your school or district. Here’s a breakdown of what to expect.

How To Read the Book

This is Chapter 1 where we frame what we mean by computer science, assert some beliefs about K-12 computer science education, offer some background context, and set the tone for the rest of the book. In Chapter 2, Jennifer provides an overview of the challenges and opportunities of embedding computer science in elementary school. Using on-the-ground anecdotes from her years of experience, she provides key principles of practice for thinking about computer science in elementary settings, one that approaches computer science through notions of play and hands-on learning experiences. Chapter 3 Gerald, who shares the way he frames computer science at the secondary level, drawing heavily on his experiences as a middle school science teacher and directing K-12 computer science workshops around the world. His framework places the unique needs of secondary content-areas first, creatively embedding computer science into instructional settings in ways that deepens and expands disciplinary knowledge. Chapter 4 brings back Jennifer who offers a series of illustrations from her classroom practice, telling rich stories about how computer science comes to life in elementary school, sharing curricular resources, and offering instructional tips. Gerald returns in Chapter 5 to offer us illustrative examples from his work with middle school students and teachers that show what the framing principles look like in action–sharing curricular resources, and offering instructional tips. In Chapter 6, Pam offers the innovative story of how she adapted a college robots project to help her English students learn computer science through Shakespeare–sharing curricular resources, and offering instructional tips. In Chapter 7, Tom and Pam team up to propose a blueprint for how to take the frameworks and lessons shared in the book and operationalize them in schools and districts. They provide suggestions for organizational structures, professional learning, and resources meant to help readers envision and sustain embedded computer science for years to come–sharing templates and other planning instruments. And in Chapter 8, Jennifer and Gerald conclude by offering their tips for how to sustain your computer science education program over time. They offer both principles of practices as well as direct advice.

While you can jump around in the book, we recommend reading the chapters in order. It might be tempting for a secondary teacher to want to skip chapters that focus more on elementary school, but we would discourage you from doing so. There is so much to learn from the work that happens across grades. And, the principles of practice that emerge from different chapters can always–always–be adapted for other grades and content-areas than the specific ones illustrated. You will get the most out of the book by reading each chapter in order and always translating for your own setting what can be adopted and adapted.  

Conclusion

Bringing K-12 computer science to your classroom, school, and district is uncharted territory. You can see that whether you look at how it plays out in different schools, how different states are attempting to respond to growing demand, or how other countries implement national programs. There is no one way to do it. We think that with such openness comes phenomenal potential to create K-12 computer science models that are truly responsive to the needs of each individual community. As you prepare to learn more from our experiences, remember two key points made above:

1. Computer science is not a STEM subject. It can deepen and expand learning in all grades and disciplines.
2. Computer science is more than a technical subject. It’s about inquiry, logic, and language.

With that in mind, let’s hear from Jennifer about what computer science looks like in her district and uncover the principles of practice that might be adopted and adapted in yours.

 

References (Partial)

Finley, K. (2014, December 8). Obama becomes first president to write a computer program. Retrieved March 22, 2015, from https://www.wired.com/2014/12/obama-becomes-first-president-write-computer-program/

Frabetti, F. (2015). Software theory: A cultural and philosophical study. New York: Rowan & Littlefield Education.

Google Inc., & Gallup Inc. (2016). Trends in the state of computer science in US K-12 schools. Online: Google. Retrieved from http://services.google.com/fh/files/misc/trends-in-the-state-of-computer-science-report.pdf

Kafai, Y. B., & Burke, Q. (2014). Connected code: Why children need to learn programming. Cambridge, MA: MIT Press.

Lynch, T. L. (2017). How English teachers will save the future: Re-imagining computer science as the language art it is. STEM Journal, 18(4), 163–180.

Margolis, J., Estrella, R., Goode, J., Holme, J. J., & Nao, K. (2008). Stuck in the shallow end: Education, race, and computing. Cambridge, MA: MIT Press.

Resnick, M. (2017). Lifelong kindergarten: Cultivating creativity through projects, passion, peers, and play. Cambridge, MA: MIT Press.

Taylor, K., & Miller, C. C. (2015, September 15). De Blasio to announce a 10-year deadline to offer computer science to all students. New York Times. Retrieved from http://www.nytimes.com/2015/09/16/nyregion/de-blasio-to-announce-10-year-deadline-to-offer-computer-science-to-all-students.html?_r=0

Vee, A. (2017). Coding literacy: How computer programming is changing writing. Cambridge, MA: MIT Press.