Hannah’s favorite book is *Lord of the Flies* by William Golding, and as part of a project she was doing, she wanted to find some quantitative information to support her critique.

Spoiler alert: for those who don’t know it, the book tells the story of a group of schoolboys shipwrecked on an island. Written as a reaction to *The Coral Island*, an optimistic and uplifting book with a similar initial premise, *Lord of the Flies* instead relates the boys’ descent into savagery once they are separated from societal influence.

The principle data that Hannah asked for was a timeline of the appearance of the characters. This is a pretty straightforward bit of counting. For a given character name, I can search the text for the positions it appears in, and while I am at it, label the data with `Legended` so that it looks nicer when plotted.

The variable `$lotf` contains the text of the book (there is some discussion later about how to get that). By dividing the string position by the length of the book, I am rescaling the range to 0–1 to make alignment with later work easier. Now I simply create a `Histogram` of the data. I used a `SmoothHistogram`, as it looks nicer. The smoothing parameter of 0.06 is rather subjective, but gave this rather pleasingly smooth overview without squashing all the details.

Already we can see some of the narrative arc of the book. The protagonist, Ralph, makes an early appearance, closely followed by the antagonist, Jack. The nonexistent Beast appears as a minor character early in the book as the boys explore the island before becoming a major feature in the middle of the book, preceding Jack’s rise as Jack exploits fear of the Beast to take power. Ralph becomes significant again toward the end as the conflict between he and Jack reaches its peak.

But most of Hannah’s critique was about meaning, not plot, so we started talking about the tone of the book. To quantify this, we can use a simple machine learning classifier on the sentences and then do basic statistics on the result.

By breaking the text into sentences and then using the built-in sentiment analyzer, we can hunt out the sentence most likely to be a positive one.

The classifier returns only `"Positive"`, `"Negative"` and `"Neutral"` classes, so if we map those to numbers we can take a moving average to produce an average sentiment vector with a window of 500 sentences.

Putting that together with the character occurrences allows some interesting insights.

We can see that there is an early negative tone as the boys are shipwrecked, which quickly becomes positive as they explore the island and their newfound freedom. The tone becomes more neutral as concerns rise about the Beast, and turn negative as Jack rises to power. There is a brief period of positivity as Ralph returns to prominence before the book dives into bleak territory as everything goes bad (especially for Piggy).

Digital humanities is a growing field, and I think the relative ease with which the Wolfram Language can be applied to text analysis and other kinds of data science should help computation make useful contributions to many fields that were once considered entirely subjective.

Because I wanted to avoid needing to OCR my hard copy of the book, I used a digital copy. However, the data was corrupted by some page headers, artifacts of navigation hyperlinks and an index page. So, here is some rather dirty string pattern work to strip out those extraneous words and numbers to produce a clean string containing only narrative:

This is the code for labeling the plot with key moments in the book:

It doesn’t provide much insight, but the conch word cloud at the top of the article was generated with this code:

Reference—conch shell: Source image from pngimg.com Creative Commons 4.0 BY-NC.

]]>People are used to producing prose—and sometimes pictures—to express themselves. But in the modern age of computation, something new has become possible that I’d like to call the computational essay.

I’ve been working on building the technology to support computational essays for several decades, but it’s only very recently that I’ve realized just how central computational essays can be to both the way people learn, and the way they communicate facts and ideas. Professionals of the future will routinely deliver results and reports as computational essays. Educators will routinely explain concepts using computational essays. Students will routinely produce computational essays as homework for their classes.

Here’s a very simple example of a computational essay:

There are basically three kinds of things here. First, ordinary text (here in English). Second, computer input. And third, computer output. And the crucial point is that these all work together to express what’s being communicated.

The ordinary text gives context and motivation. The computer input gives a precise specification of what’s being talked about. And then the computer output delivers facts and results, often in graphical form. It’s a powerful form of exposition that combines computational thinking on the part of the human author with computational knowledge and computational processing from the computer.

But what really makes this work is the Wolfram Language—and the succinct representation of high-level ideas that it provides, defining a unique bridge between human computational thinking and actual computation and knowledge delivered by a computer.

In a typical computational essay, each piece of Wolfram Language input will usually be quite short (often not more than a line or two). But the point is that such input can communicate a high-level computational thought, in a form that can readily be understood both by the computer and by a human reading the essay.

It’s essential to all this that the Wolfram Language has so much built-in knowledge—both about the world and about how to compute things in it. Because that’s what allows it to immediately talk not just about abstract computations, but also about real things that exist and happen in the world—and ultimately to provide a true computational communication language that bridges the capabilities of humans and computers.

Let’s use a computational essay to explain computational essays.

Let’s say we want to talk about the structure of a human language, like English. English is basically made up of words. Let’s get a list of the common ones.

Generate a list of common words in English:

✕
WordList[] |

How long is a typical word? Well, we can take the list of common words, and make a histogram that shows their distribution of lengths.

Make a histogram of word lengths:

✕
Histogram[StringLength[WordList[]]] |

Do the same for French:

✕
Histogram[StringLength[WordList[Language -> "French"]]] |

Notice that the word lengths tend to be longer in French. We could investigate whether this is why documents tend to be longer in French than in English, or how this relates to quantities like entropy for text. (Of course, because this is a computational essay, the reader can rerun the computations in it themselves, say by trying Russian instead of French.)

But as something different, let’s compare languages by comparing their translations for, say, the word “computer”.

Find the translations for “computer” in the 10 most common languages:

✕
Take[WordTranslation["computer", All], 10] |

Find the first translation in each case:

✕
First /@ Take[WordTranslation["computer", All], 10] |

Arrange common languages in “feature space” based on their translations for “computer”:

✕
FeatureSpacePlot[First /@ Take[WordTranslation["computer", All], 40]] |

From this plot, we can start to investigate all sorts of structural and historical relationships between languages. But from the point of view of a computational essay, what’s important here is that we’re sharing the exposition between ordinary text, computer input, and output.

The text is saying what the basic point is. Then the input is giving a precise definition of what we want. And the output is showing what’s true about it. But take a look at the input. Even just by looking at the names of the Wolfram Language functions in it, one can get a pretty good idea what it’s talking about. And while the function names are based on English, one can use “code captions” to understand it in another language, say Japanese:

✕
FeatureSpacePlot[First /@ Take[WordTranslation["computer", All], 40]] |

But let’s say one doesn’t know about `FeatureSpacePlot`. What is it? If it was just a word or phrase in English, we might be able to look in a dictionary, but there wouldn’t be a precise answer. But a function in the Wolfram Language is always precisely defined. And to know what it does we can start by just looking at its documentation. But much more than that, we can just run it ourselves to explicitly see what it does.

And that’s a crucial part of what’s great about computational essays. If you read an ordinary essay, and you don’t understand something, then in the end you really just have to ask the author to find out what they meant. In a computational essay, though, there’s Wolfram Language input that precisely and unambiguously specifies everything—and if you want to know what it means, you can just run it and explore any detail of it on your computer, automatically and without recourse to anything like a discussion with the author.

How does one actually create a computational essay? With the technology stack we have, it’s very easy—mainly thanks to the concept of notebooks that we introduced with the first version of Mathematica all the way back in 1988. A notebook is a structured document that mixes cells of text together with cells of Wolfram Language input and output, including graphics, images, sounds, and interactive content:

In modern times one great (and very hard to achieve!) thing is that full Wolfram Notebooks run seamlessly across desktop, cloud and mobile. You can author a notebook in the native Wolfram Desktop application (Mac, Windows, Linux)—or on the web through any web browser, or on mobile through the Wolfram Cloud app. Then you can share or publish it through the Wolfram Cloud, and get access to it on the web or on mobile, or download it to desktop or, now, iOS devices.

Sometimes you want the reader of a notebook just to look at it, perhaps opening and closing groups of cells. Sometimes you also want them to be able to operate the interactive elements. And sometimes you want them to be able to edit and run the code, or maybe modify the whole notebook. And the crucial point is that all these things are easy to do with the cloud-desktop-mobile system we’ve built.

Computational essays are great for students to read, but they’re also great for students to write. Most of the current modalities for student work are remarkably old. Write an essay. Give a math derivation. These have been around for millennia. Not that there’s anything wrong with them. But now there’s something new: write a computational essay. And it’s wonderfully educational.

A computational essay is in effect an intellectual story told through a collaboration between a human author and a computer. The computer acts like a kind of intellectual exoskeleton, letting you immediately marshall vast computational power and knowledge. But it’s also an enforcer of understanding. Because to guide the computer through the story you’re trying to tell, you have to understand it yourself.

When students write ordinary essays, they’re typically writing about content that in some sense “already exists” (“discuss this passage”; “explain this piece of history”; …). But in doing computation (at least with the Wolfram Language) it’s so easy to discover new things that computational essays will end up with an essentially inexhaustible supply of new content, that’s never been seen before. Students will be exploring and discovering as well as understanding and explaining.

When you write a computational essay, the code in your computational essay has to produce results that fit with the story you’re telling. It’s not like you’re doing a mathematical derivation, and then some teacher tells you you’ve got the wrong answer. You can immediately see what your code does, and whether it fits with the story you’re telling. If it doesn’t, well then maybe your code is wrong—or maybe your story is wrong.

What should the actual procedure be for students producing computational essays? At this year’s Wolfram Summer School we did the experiment of asking all our students to write a computational essay about anything they knew about. We ended up with 72 interesting essays—exploring a very wide range of topics.

In a more typical educational setting, the “prompt” for a computational essay could be something like “What is the typical length of a word in English” or “Explore word lengths in English”.

There’s also another workflow I’ve tried. As the “classroom” component of a class, do livecoding (or a live experiment). Create or discover something, with each student following along by doing their own computations. At the end of the class, each student will have a notebook they made. Then have their “homework” be to turn that notebook into a computational essay that explains what was done.

And in my experience, this ends up being a very good exercise—that really tests and cements the understanding students have. But there’s also something else: when students have created a computational essay, they have something they can keep—and directly use—forever.

And this is one of the great general features of computational essays. When students write them, they’re in effect creating a custom library of computational tools for themselves—that they’ll be in a position to immediately use at any time in the future. It’s far too common for students to write notes in a class, then never refer to them again. Yes, they might run across some situation where the notes would be helpful. But it’s often hard to motivate going back and reading the notes—not least because that’s only the beginning; there’s still the matter of implementing whatever’s in the notes.

But the point is that with a computational essay, once you’ve found what you want, the code to implement it is right there—immediately ready to be applied to whatever has come up.

What can computational essays be about? Almost anything! I’ve often said that for any field of study X (from archaeology to zoology), there either is now, or soon will be, a “computational X”. And any “computational X” can immediately be explored and explained using computational essays.

But even when there isn’t a clear “computational X” yet, computational essays can still be a powerful way to organize and present material. In some sense, the very fact that a sequence of computations are typically needed to “tell the story” in an essay helps define a clear backbone for the whole essay. In effect, the structured nature of the computational presentation helps suggest structure for the narrative—making it easier for students (and others) to write essays that are easy to read and understand.

But what about actual subject matter? Well, imagine you’re studying history—say the history of the English Civil War. Well, conveniently, the Wolfram Language has a lot of knowledge about history (as about so many other things) built in. So you can present the English Civil War through a kind of dialog with it. For example, you can ask it for the geography of battles:

✕
GeoListPlot[\!\(\* NamespaceBox["LinguisticAssistant", DynamicModuleBox[{Typeset`query$$ = "English Civil War", Typeset`boxes$$ = TemplateBox[{"\"English Civil War\"", RowBox[{"Entity", "[", RowBox[{"\"MilitaryConflict\"", ",", "\"EnglishCivilWar\""}], "]"}], "\"Entity[\\\"MilitaryConflict\\\", \ \\\"EnglishCivilWar\\\"]\"", "\"military conflict\""}, "Entity"], Typeset`allassumptions$$ = {{ "type" -> "Clash", "word" -> "English Civil War", "template" -> "Assuming \"${word}\" is ${desc1}. Use as \ ${desc2} instead", "count" -> "3", "Values" -> {{ "name" -> "MilitaryConflict", "desc" -> "a military conflict", "input" -> "*C.English+Civil+War-_*MilitaryConflict-"}, { "name" -> "Word", "desc" -> "a word", "input" -> "*C.English+Civil+War-_*Word-"}, { "name" -> "HistoricalEvent", "desc" -> "a historical event", "input" -> "*C.English+Civil+War-_*HistoricalEvent-"}}}, { "type" -> "SubCategory", "word" -> "English Civil War", "template" -> "Assuming ${desc1}. Use ${desc2} instead", "count" -> "4", "Values" -> {{ "name" -> "EnglishCivilWar", "desc" -> "English Civil War (1642 - 1651)", "input" -> "*DPClash.MilitaryConflictE.English+Civil+War-_*\ EnglishCivilWar-"}, { "name" -> "FirstEnglishCivilWar", "desc" -> "English Civil War (1642 - 1646)", "input" -> "*DPClash.MilitaryConflictE.English+Civil+War-_*\ FirstEnglishCivilWar-"}, { "name" -> "SecondEnglishCivilWar", "desc" -> "Second English Civil War", "input" -> "*DPClash.MilitaryConflictE.English+Civil+War-_*\ SecondEnglishCivilWar-"}, { "name" -> "ThirdEnglishCivilWar", "desc" -> "Third English Civil War", "input" -> "*DPClash.MilitaryConflictE.English+Civil+War-_*\ ThirdEnglishCivilWar-"}}}}, Typeset`assumptions$$ = {}, Typeset`open$$ = {1, 2}, Typeset`querystate$$ = { "Online" -> True, "Allowed" -> True, "mparse.jsp" -> 1.305362`6.5672759594240935, "Messages" -> {}}}, DynamicBox[ToBoxes[ AlphaIntegration`LinguisticAssistantBoxes["", 4, Automatic, Dynamic[Typeset`query$$], Dynamic[Typeset`boxes$$], Dynamic[Typeset`allassumptions$$], Dynamic[Typeset`assumptions$$], Dynamic[Typeset`open$$], Dynamic[Typeset`querystate$$]], StandardForm], ImageSizeCache->{265., {7., 17.}}, TrackedSymbols:>{ Typeset`query$$, Typeset`boxes$$, Typeset`allassumptions$$, Typeset`assumptions$$, Typeset`open$$, Typeset`querystate$$}], DynamicModuleValues:>{}, UndoTrackedVariables:>{Typeset`open$$}], BaseStyle->{"Deploy"}, DeleteWithContents->True, Editable->False, SelectWithContents->True]\)["Battles"]] |

You could ask for a timeline of the beginning of the war (you don’t need to say “first 15 battles”, because if one cares, one can just read that from the Wolfram Language code):

✕
TimelinePlot[Take[\!\(\* NamespaceBox["LinguisticAssistant", DynamicModuleBox[{Typeset`query$$ = "English Civil War", Typeset`boxes$$ = TemplateBox[{"\"English Civil War\"", RowBox[{"Entity", "[", RowBox[{"\"MilitaryConflict\"", ",", "\"EnglishCivilWar\""}], "]"}], "\"Entity[\\\"MilitaryConflict\\\", \\\"EnglishCivilWar\\\"]\ \"", "\"military conflict\""}, "Entity"], Typeset`allassumptions$$ = {{ "type" -> "Clash", "word" -> "English Civil War", "template" -> "Assuming \"${word}\" is ${desc1}. Use as \ ${desc2} instead", "count" -> "3", "Values" -> {{ "name" -> "MilitaryConflict", "desc" -> "a military conflict", "input" -> "*C.English+Civil+War-_*MilitaryConflict-"}, { "name" -> "Word", "desc" -> "a word", "input" -> "*C.English+Civil+War-_*Word-"}, { "name" -> "HistoricalEvent", "desc" -> "a historical event", "input" -> "*C.English+Civil+War-_*HistoricalEvent-"}}}, { "type" -> "SubCategory", "word" -> "English Civil War", "template" -> "Assuming ${desc1}. Use ${desc2} instead", "count" -> "4", "Values" -> {{ "name" -> "EnglishCivilWar", "desc" -> "English Civil War (1642 - 1651)", "input" -> "*DPClash.MilitaryConflictE.English+Civil+War-_\ *EnglishCivilWar-"}, { "name" -> "FirstEnglishCivilWar", "desc" -> "English Civil War (1642 - 1646)", "input" -> "*DPClash.MilitaryConflictE.English+Civil+War-_\ *FirstEnglishCivilWar-"}, { "name" -> "SecondEnglishCivilWar", "desc" -> "Second English Civil War", "input" -> "*DPClash.MilitaryConflictE.English+Civil+War-_\ *SecondEnglishCivilWar-"}, { "name" -> "ThirdEnglishCivilWar", "desc" -> "Third English Civil War", "input" -> "*DPClash.MilitaryConflictE.English+Civil+War-_\ *ThirdEnglishCivilWar-"}}}}, Typeset`assumptions$$ = {}, Typeset`open$$ = {1, 2}, Typeset`querystate$$ = { "Online" -> True, "Allowed" -> True, "mparse.jsp" -> 1.305362`6.5672759594240935, "Messages" -> {}}}, DynamicBox[ToBoxes[ AlphaIntegration`LinguisticAssistantBoxes["", 4, Automatic, Dynamic[Typeset`query$$], Dynamic[Typeset`boxes$$], Dynamic[Typeset`allassumptions$$], Dynamic[Typeset`assumptions$$], Dynamic[Typeset`open$$], Dynamic[Typeset`querystate$$]], StandardForm], ImageSizeCache->{275., {7., 17.}}, TrackedSymbols:>{ Typeset`query$$, Typeset`boxes$$, Typeset`allassumptions$$, Typeset`assumptions$$, Typeset`open$$, Typeset`querystate$$}], DynamicModuleValues:>{}, UndoTrackedVariables:>{Typeset`open$$}], BaseStyle->{"Deploy"}, DeleteWithContents->True, Editable->False, SelectWithContents->True]\)["Battles"], 15]] |

You could start looking at how armies moved, or who won and who lost at different points. At first, you can write a computational essay in which the computations are basically just generating custom infographics to illustrate your narrative. But then you can go further—and start really doing “computational history”. You can start to compute various statistical measures of the progress of the war. You can find ways to quantitatively compare it to other wars, and so on.

Can you make a “computational essay” about art? Absolutely. Maybe about art history. Pick 10 random paintings by van Gogh:

✕
EntityValue[RandomSample[\!\(\* NamespaceBox["LinguisticAssistant", DynamicModuleBox[{Typeset`query$$ = "van gogh", Typeset`boxes$$ = TemplateBox[{"\"Vincent van Gogh\"", RowBox[{"Entity", "[", RowBox[{"\"Person\"", ",", "\"VincentVanGogh::9vq62\""}], "]"}], "\"Entity[\\\"Person\\\", \\\"VincentVanGogh::9vq62\\\"]\"", "\"person\""}, "Entity"], Typeset`allassumptions$$ = {{ "type" -> "Clash", "word" -> "van gogh", "template" -> "Assuming \"${word}\" is ${desc1}. Use as \ ${desc2} instead", "count" -> "4", "Values" -> {{ "name" -> "Person", "desc" -> "a person", "input" -> "*C.van+gogh-_*Person-"}, { "name" -> "Movie", "desc" -> "a movie", "input" -> "*C.van+gogh-_*Movie-"}, { "name" -> "SolarSystemFeature", "desc" -> "a solar system feature", "input" -> "*C.van+gogh-_*SolarSystemFeature-"}, { "name" -> "Word", "desc" -> "a word", "input" -> "*C.van+gogh-_*Word-"}}}}, Typeset`assumptions$$ = {}, Typeset`open$$ = {1, 2}, Typeset`querystate$$ = { "Online" -> True, "Allowed" -> True, "mparse.jsp" -> 0.472412`6.125865914333281, "Messages" -> {}}}, DynamicBox[ToBoxes[ AlphaIntegration`LinguisticAssistantBoxes["", 4, Automatic, Dynamic[Typeset`query$$], Dynamic[Typeset`boxes$$], Dynamic[Typeset`allassumptions$$], Dynamic[Typeset`assumptions$$], Dynamic[Typeset`open$$], Dynamic[Typeset`querystate$$]], StandardForm], ImageSizeCache->{227., {7., 17.}}, TrackedSymbols:>{ Typeset`query$$, Typeset`boxes$$, Typeset`allassumptions$$, Typeset`assumptions$$, Typeset`open$$, Typeset`querystate$$}], DynamicModuleValues:>{}, UndoTrackedVariables:>{Typeset`open$$}], BaseStyle->{"Deploy"}, DeleteWithContents->True, Editable->False, SelectWithContents->True]\)["NotableArtworks"], 10], "Image"] |

Then look at what colors they use (a surprisingly narrow selection):

✕
ChromaticityPlot[%] |

Or maybe one could write a computational essay about actually creating art, or music.

What about science? You could rediscover Kepler’s laws by looking at properties of planets:

✕
\!\(\* NamespaceBox["LinguisticAssistant", DynamicModuleBox[{Typeset`query$$ = "planets", Typeset`boxes$$ = TemplateBox[{"\"planets\"", RowBox[{"EntityClass", "[", RowBox[{"\"Planet\"", ",", "All"}], "]"}], "\"EntityClass[\\\"Planet\\\", All]\"", "\"planets\""}, "EntityClass"], Typeset`allassumptions$$ = {{ "type" -> "Clash", "word" -> "planets", "template" -> "Assuming \"${word}\" is ${desc1}. Use as \ ${desc2} instead", "count" -> "4", "Values" -> {{ "name" -> "PlanetClass", "desc" -> " referring to planets", "input" -> "*C.planets-_*PlanetClass-"}, { "name" -> "ExoplanetClass", "desc" -> " referring to exoplanets", "input" -> "*C.planets-_*ExoplanetClass-"}, { "name" -> "MinorPlanetClass", "desc" -> " referring to minor planets", "input" -> "*C.planets-_*MinorPlanetClass-"}, { "name" -> "Word", "desc" -> "a word", "input" -> "*C.planets-_*Word-"}}}}, Typeset`assumptions$$ = {}, Typeset`open$$ = {1, 2}, Typeset`querystate$$ = { "Online" -> True, "Allowed" -> True, "mparse.jsp" -> 0.400862`6.054539882441674, "Messages" -> {}}}, DynamicBox[ToBoxes[ AlphaIntegration`LinguisticAssistantBoxes["", 4, Automatic, Dynamic[Typeset`query$$], Dynamic[Typeset`boxes$$], Dynamic[Typeset`allassumptions$$], Dynamic[Typeset`assumptions$$], Dynamic[Typeset`open$$], Dynamic[Typeset`querystate$$]], StandardForm], ImageSizeCache->{171., {7., 17.}}, TrackedSymbols:>{ Typeset`query$$, Typeset`boxes$$, Typeset`allassumptions$$, Typeset`assumptions$$, Typeset`open$$, Typeset`querystate$$}], DynamicModuleValues:>{}, UndoTrackedVariables:>{Typeset`open$$}], BaseStyle->{"Deploy"}, DeleteWithContents->True, Editable->False, SelectWithContents->True]\)[{"DistanceFromSun", "OrbitPeriod"}] |

✕
ListLogLogPlot[%] |

Maybe you could go on and check it for exoplanets. Or you could start solving the equations of motion for planets.

You could look at biology. Here’s the first beginning of the reference sequence for the human mitochondrion:

✕
GenomeData[{"Mitochondrion", {1, 150}}] |

You can start off breaking it into possible codons:

✕
StringPartition[%, 3] |

There’s an immense amount of data about all kinds of things built into the Wolfram Language. But there’s also the Wolfram Data Repository, which contains all sorts of specific datasets. Like here’s a map of state fairgrounds in the US:

✕
GeoListPlot[ ResourceData["U.S. State Fairgrounds"][All, "GeoPosition"]] |

And here’s a word cloud of the constitutions of countries that have been enacted since 2010:

✕
WordCloud[ StringJoin[ Normal[ResourceData["World Constitutions"][ Select[#YearEnacted > \!\(\* NamespaceBox["LinguisticAssistant", DynamicModuleBox[{Typeset`query$$ = "year 2010", Typeset`boxes$$ = RowBox[{"DateObject", "[", RowBox[{"{", "2010", "}"}], "]"}], Typeset`allassumptions$$ = {{ "type" -> "MultiClash", "word" -> "", "template" -> "Assuming ${word1} is referring to \ ${desc1}. Use \"${word2}\" as ${desc2}.", "count" -> "2", "Values" -> {{ "name" -> "PseudoTokenYear", "word" -> "year 2010", "desc" -> "a year", "input" -> "*MC.year+2010-_*PseudoTokenYear-"}, { "name" -> "Unit", "word" -> "year", "desc" -> "a unit", "input" -> "*MC.year+2010-_*Unit-"}}}}, Typeset`assumptions$$ = {}, Typeset`open$$ = {1}, Typeset`querystate$$ = { "Online" -> True, "Allowed" -> True, "mparse.jsp" -> 0.542662`6.186074404594303, "Messages" -> {}}}, DynamicBox[ToBoxes[ AlphaIntegration`LinguisticAssistantBoxes["", 4, Automatic, Dynamic[Typeset`query$$], Dynamic[Typeset`boxes$$], Dynamic[Typeset`allassumptions$$], Dynamic[Typeset`assumptions$$], Dynamic[Typeset`open$$], Dynamic[Typeset`querystate$$]], StandardForm], ImageSizeCache->{86., {7., 18.}}, TrackedSymbols:>{ Typeset`query$$, Typeset`boxes$$, Typeset`allassumptions$$, Typeset`assumptions$$, Typeset`open$$, Typeset`querystate$$}], DynamicModuleValues:>{}, UndoTrackedVariables:>{Typeset`open$$}], BaseStyle->{"Deploy"}, DeleteWithContents->True, Editable->False, SelectWithContents->True]\) &], "Text"]]]] |

Quite often one’s interested in dealing not with public data, but with some kind of local data. One convenient source of this is the Wolfram Data Drop. In an educational setting, particular databins (or cloud objects in general) can be set so that they can be read (and/or added to) by some particular group. Here’s a databin that I accumulate for myself, showing my heart rate through the day. Here it is for today:

✕
DateListPlot[TimeSeries[YourDatabinHere]] |

Of course, it’s easy to make a histogram too:

✕
Histogram[TimeSeries[YourDatabinHere]] |

What about math? A key issue in math is to understand why things are true. The traditional approach to this is to give proofs. But computational essays provide an alternative. The nature of the steps in them is different—but the objective is the same: to show what’s true and why.

As a very simple example, let’s look at primes. Here are the first 50:

✕
Table[Prime[n], {n, 50}] |

Let’s find the remainder mod 6 for all these primes:

✕
Mod[Table[Prime[n], {n, 50}], 6] |

But why do only 1 and 5 occur (well, after the trivial cases of the primes 2 and 3)? We can see this by computation. Any number can be written as 6n+k for some n and k:

✕
Table[6 n + k, {k, 0, 5}] |

But if we factor numbers written in this form, we’ll see that 6n+1 and 6n+5 are the only ones that don’t have to be multiples:

✕
Factor[%] |

What about computer science? One could for example write a computational essay about implementing Euclid’s algorithm, studying its running time, and so on.

Define a function to give all steps in Euclid’s algorithm:

✕
gcdlist[a_, b_] := NestWhileList[{Last[#], Apply[Mod, #]} &, {a, b}, Last[#] != 0 &, 1] |

Find the distribution of running lengths for the algorithm for numbers up to 200:

✕
Histogram[Flatten[Table[Length[gcdlist[i, j]], {i, 200}, {j, 200}]]] |

Or in modern times, one could explore machine learning, starting, say, by making a feature space plot of part of the MNIST handwritten digits dataset:

✕
FeatureSpacePlot[RandomSample[Keys[ResourceData["MNIST"]], 50]] |

If you wanted to get deeper into software engineering, you could write a computational essay about the HTTP protocol. This gets an HTTP response from a site:

✕
URLRead["https://www.wolfram.com"] |

And this shows the tree structure of the elements on the webpage at that URL:

✕
TreeForm[Import["http://www.wolframalpha.com", {"HTML", "XMLObject"}], VertexLabeling -> False, AspectRatio -> 1/2] |

Or—in a completely different direction—you could talk about anatomy:

✕
AnatomyPlot3D[Entity["AnatomicalStructure", "LeftFoot"]] |

As far as I’m concerned, for a computational essay to be good, it has to be as easy to understand as possible. The format helps quite a lot, of course. Because a computational essay is full of outputs (often graphical) that are easy to skim, and that immediately give some impression of what the essay is trying to say. It also helps that computational essays are structured documents, that deliver information in well-encapsulated pieces.

But ultimately it’s up to the author of a computational essay to make it clear. But another thing that helps is that the nature of a computational essay is that it must have a “computational narrative”—a sequence of pieces of code that the computer can execute to do what’s being discussed in the essay. And while one might be able to write an ordinary essay that doesn’t make much sense but still sounds good, one can’t ultimately do something like that in a computational essay. Because in the end the code is the code, and actually has to run and do things.

So what can go wrong? Well, like English prose, Wolfram Language code can be unnecessarily complicated, and hard to understand. In a good computational essay, both the ordinary text, and the code, should be as simple and clean as possible. I try to enforce this for myself by saying that each piece of input should be at most one or perhaps two lines long—and that the caption for the input should always be just one line long. If I’m trying to do something where the core of it (perhaps excluding things like display options) takes more than a line of code, then I break it up, explaining each line separately.

Another important principle as far as I’m concerned is: be explicit. Don’t have some variable that, say, implicitly stores a list of words. Actually show at least part of the list, so people can explicitly see what it’s like. And when the output is complicated, find some tabulation or visualization that makes the features you’re interested in obvious. Don’t let the “key result” be hidden in something that’s tucked away in the corner; make sure the way you set things up makes it front and center.

Use the structured nature of notebooks. Break up computational essays with section headings, again helping to make them easy to skim. I follow the style of having a “caption line” before each input. Don’t worry if this somewhat repeats what a paragraph of text has said; consider the caption something that someone who’s just “looking at the pictures” might read to understand what a picture is of, before they actually dive into the full textual narrative.

The technology of Wolfram Notebooks makes it straightforward to put in interactive elements, like `Manipulate`, into computational essays. And sometimes this is very helpful, and perhaps even essential. But interactive elements shouldn’t be overused. Because whenever there’s an element that requires interaction, this reduces the ability to skim the essay.

Sometimes there’s a fair amount of data—or code—that’s needed to set up a particular computational essay. The cloud is very useful for handling this. Just deploy the data (or code) to the Wolfram Cloud, and set appropriate permissions so it can automatically be read whenever the code in your essay is executed.

Notebooks also allow “reverse closing” of cells—allowing an output cell to be immediately visible, even though the input cell that generated it is initially closed. This kind of hiding of code should generally be avoided in the body of a computational essay, but it’s sometimes useful at the beginning or end of an essay, either to give an indication of what’s coming, or to include something more advanced where you don’t want to go through in detail how it’s made.

OK, so if a computational essay is done, say, as homework, how can it be assessed? A first, straightforward question is: does the code run? And this can be determined pretty much automatically. Then after that, the assessment process is very much like it would be for an ordinary essay. Of course, it’s nice and easy to add cells into a notebook to give comments on what’s there. And those cells can contain runnable code—that for example can take results in the essay and process or check them.

Are there principles of good computational essays? Here are a few candidates:

0. Understand what you’re talking about (!)

1. Find the most straightforward and direct way to represent your subject matter

2. Keep the core of each piece of Wolfram Language input to a line or two

3. Use explicit visualization or other information presentation as much as possible

4. Try to make each input+caption independently understandable

5. Break different topics or directions into different subsections

At the core of computational essays is the idea of expressing computational thoughts using the Wolfram Language. But to do that, one has to know the language. Now, unlike human languages, the Wolfram Language is explicitly designed (and, yes, that’s what I’ve been doing for the past 30+ years) to follow definite principles and to be as easy to learn as possible. But there’s still learning to be done.

One feature of the Wolfram Language is that—like with human languages—it’s typically easier to read than to write. And that means that a good way for people to learn what they need to be able to write computational essays is for them first to read a bunch of essays. Perhaps then they can start to modify those essays. Or they can start creating “notes essays”, based on code generated in livecoding or other classroom sessions.

As people get more fluent in writing the Wolfram Language, something interesting happens: they start actually expressing themselves in the language, and using Wolfram Language input to carry significant parts of the narrative in a computational essay.

When I was writing An Elementary Introduction to the Wolfram Language (which itself is written in large part as a sequence of computational essays) I had an interesting experience. Early in the book, it was decently easy to explain computational exercises in English (“Make a table of the first 10 squares”). But a little later in the book, it became a frustrating process.

It was easy to express what I wanted in the Wolfram Language. But to express it in English was long and awkward (and had a tendency of sounding like legalese). And that’s the whole point of using the Wolfram Language, and the reason I’ve spent 30+ years building it: because it provides a better, crisper way to express computational thoughts.

It’s sometimes said of human languages that the language you use determines how you think. It’s not clear how true this is of human languages. But it’s absolutely true of computer languages. And one of the most powerful things about the Wolfram Language is that it helps one formulate clear computational thinking.

Traditional computer languages are about writing code that describes the details of what a computer should do. The point of the Wolfram Language is to provide something much higher level—that can immediately talk about things in the world, and that can allow people as directly as possible to use it as a medium of computational thinking. And in a sense that’s what makes a good computational essay possible.

Now that we have full-fledged computational essays, I realize I’ve been on a path towards them for nearly 40 years. At first I was taking interactive computer output and Scotch-taping descriptions into it:

By 1981, when I built SMP, I was routinely writing documents that interspersed code and explanations:

But it was only in 1986, when I started documenting what became Mathematica and the Wolfram Language, that I started seriously developing a style close to what I now favor for computational essays:

And with the release of Mathematica 1.0 in 1988 came another critical element: the invention of Wolfram Notebooks. Notebooks arrived in a form at least superficially very similar to the way they are today (and already in many ways more sophisticated than the imitations that started appearing 25+ years later!): collections of cells arranged into groups, and capable of containing text, executable code, graphics, etc.

At first notebooks were only possible on Mac and NeXT computers. A few years later they were extended to Microsoft Windows and X Windows (and later, Linux). But immediately people started using notebooks both to provide reports about they’d done, and to create rich expository and educational material. Within a couple of years, there started to be courses based on notebooks, and books printed from notebooks, with interactive versions available on CD-ROM at the back:

So in a sense the raw material for computational essays already existed by the beginning of the 1990s. But to really make computational essays come into their own required the development of the cloud—as well as the whole broad range of computational knowledge that’s now part of the Wolfram Language.

By 1990 it was perfectly possible to create a notebook with a narrative, and people did it, particularly about topics like mathematics. But if there was real-world data involved, things got messy. One had to make sure that whatever was needed was appropriately available from a distribution CD-ROM or whatever. We created a Player for notebooks very early, that was sometimes distributed with notebooks.

But in the last few years, particularly with the development of the Wolfram Cloud, things have gotten much more streamlined. Because now you can seamlessly store things in the cloud and use them anywhere. And you can work directly with notebooks in the cloud, just using a web browser. In addition, thanks to lots of user-assistance innovations (including natural language input), it’s become even easier to write in the Wolfram Language—and there’s ever more that can be achieved by doing so.

And the important thing that I think has now definitively happened is that it’s become lightweight enough to produce a good computational essay that it makes sense to do it as something routine—either professionally in writing reports, or as a student doing homework.

The idea of students producing computational essays is something new for modern times, made possible by a whole stack of current technology. But there’s a curious resonance with something from the distant past. You see, if you’d learned a subject like math in the US a couple of hundred years ago, a big thing you’d have done is to create a so-called ciphering book—in which over the course of several years you carefully wrote out the solutions to a range of problems, mixing explanations with calculations. And the idea then was that you kept your ciphering book for the rest of your life, referring to it whenever you needed to solve problems like the ones it included.

Well, now, with computational essays you can do very much the same thing. The problems you can address are vastly more sophisticated and wide-ranging than you could reach with hand calculation. But like with ciphering books, you can write computational essays so they’ll be useful to you in the future—though now you won’t have to imitate calculations by hand; instead you’ll just edit your computational essay notebook and immediately rerun the Wolfram Language inputs in it.

I actually only learned about ciphering books quite recently. For about 20 years I’d had essentially as an artwork a curious handwritten notebook (created in 1818, it says, by a certain George Lehman, apparently of Orwigsburg, Pennsylvania), with pages like this:

I now know this is a ciphering book—that on this page describes how to find the “height of a perpendicular object… by having the length of the shadow given”. And of course I can’t resist a modern computational essay analog, which, needless to say, can be a bit more elaborate.

Find the current position of the Sun as azimuth, altitude:

✕
SunPosition[] |

Find the length of a shadow for an object of unit height:

✕
1/Tan[SunPosition[][[2]]] |

Given a 10-ft shadow, find the height of the object that made it:

✕
Tan[SunPosition[][[2]]]10ft |

I like writing textual essays (such as blog posts!). But I like writing computational essays more. Because at least for many of the things I want to communicate, I find them a purer and more efficient way to do it. I could spend lots of words trying to express an idea—or I can just give a little piece of Wolfram Language input that expresses the idea very directly and shows how it works by generating (often very visual) output with it.

When I wrote my big book A New Kind of Science (from 1991 to 2002), neither our technology nor the world was quite ready for computational essays in the form in which they’re now possible. My research for the book filled thousands of Wolfram Notebooks. But when it actually came to putting together the book, I just showed the results from those notebooks—including a little of the code from them in notes at the back of the book.

But now the story of the book can be told in computational essays—that I’ve been starting to produce. (Just for fun, I’ve been livestreaming some of the work I’m doing to create these.) And what’s very satisfying is just how clearly and crisply the ideas in the book can be communicated in computational essays.

There is so much potential in computational essays. And indeed we’re now starting the project of collecting “topic explorations” that use computational essays to explore a vast range of topics in unprecedentedly clear and direct ways. It’ll be something like our Wolfram Demonstrations Project (that now has 11,000+ Wolfram Language–powered Demonstrations). Here’s a typical example I wrote:

Computational essays open up all sorts of new types of communication. Research papers that directly present computational experiments and explorations. Reports that describe things that have been found, but allow other cases to be immediately explored. And, of course, computational essays define a way for students (and others) to very directly and usefully showcase what they’ve learned.

There’s something satisfying about both writing—and reading—computational essays. It’s as if in communicating ideas we’re finally able to go beyond pure human effort—and actually leverage the power of computation. And for me, having built the Wolfram Language to be a computational communication language, it’s wonderful to see how it can be used to communicate so effectively in computational essays.

It’s so nice when I get something sent to me as a well-formed computational essay. Because I immediately know that I’m going to get a straight story that I can actually understand. There aren’t going to be all sorts of missing sources and hidden assumptions; there’s just going to be Wolfram Language input that stands alone, and that I can take out and study or run for myself.

The modern world of the web has brought us a few new formats for communication—like blogs, and social media, and things like Wikipedia. But all of these still follow the basic concept of text + pictures that’s existed since the beginning of the age of literacy. With computational essays we finally have something new—and it’s going to be exciting to see all the things it makes possible.

]]>In our continued efforts to make it easier for students to learn and understand math and science concepts, the Wolfram|Alpha team has been hard at work this summer expanding our step-by-step solutions. Since the school year is just beginning, we’re excited to announce some new features.

We’re continuously working to expand our list of step-by-step topics in Wolfram|Alpha; in fact, we’ve nearly doubled the number of areas covered. We also continue to add more—over 60 topics have step-by-step coverage in domains such as algebra, calculus, geometry, linear algebra, discrete math, statistics and chemistry. Be sure to check out our examples page to see more areas that have step-by-step solutions. And with the new intermediate steps feature, expect the coverage to grow over the next few months.

It’s always nice to see a Wolfram|Alpha query covered by a sea of orange step-by-step solution buttons—something you’ll be seeing a lot more frequently as we continue to expand our collection of solution topics.

In addition to new areas of coverage, all step-by-step topics have been improved by adding more detail through expandable intermediate steps. Let’s use local extrema of *x*^3–10*x* + 1 as an example. Right off the bat, you’ll notice the new appearance.

In this new redesign, the steps are broken into their own blocks, the hints have a new look and there’s a new type of button that gives you the ability to drill down further and see the detailed math involved in arriving at the result of the step. In the example above, steps 3, 4 and 5 have such a button.

Let’s expand step 4:

This functionality is important for keeping the step-by-step solutions readable, while still providing all relevant information. In these steps for finding extrema, it’s important to know how to find *f*′(*x*), its roots and where it doesn’t exist. If these steps were laid out in a linear fashion, it would be easy to get lost in the steps pertinent to finding the extrema. The main steps are now the outline of how to find the extrema, and the intermediate steps provided by the new button give the specific details used in each step.

It’s sometimes the case that there are multiple details one would want within a step. In cases like this, only one set of intermediate steps is shown at a time, and clicking another will replace the one currently expanded.

Intermediate steps open a new door on the types of step-by-step solutions we can provide. In the coming months, we’ll be rolling out more content that utilizes this new feature.

So what are step-by-step solutions exactly? Wolfram|Alpha has pioneered step-by-step solutions for nearly 10 years, and we continue to be the industry standard. These solutions show how to get to an answer—not just what the answer is. Let’s ask for an integral. The main results shown are calculations, i.e. the answer to the query, supplementary information and even open code. But we can go further and see how one could find the answer. Let’s click the step-by-step solution button to see.

One might think these are precomputed solutions we grab from a large table, but that’s not the case. A curated table of solutions wouldn’t be feasible because there are an infinite number of math problems. Instead we start from scratch, building a stack of functionality meant to handle any query thrown at it. The Wolfram Language is the perfect language for a project like this. Under the hood we make use of the language’s full suite of mathematical capability, along with its highly expressive, symbolic paradigm.

If the Wolfram Language can compute something, can’t we just construct the step-by-step solutions by tracing through the algorithms used? In theory, yes. For example, the Wolfram Language computes most first-order derivatives similar to the way humans do—by continually using a large table of identities. Most of the time, however, there are faster and more sophisticated algorithms a human wouldn’t possibly execute by hand. When computing an integral, for example, most likely the Risch algorithm or a Mellin convolution of Meijer G-functions is being used. Instead, our step-by-step solutions take the approach a human would most likely take—that is, using heuristics to look for substitutions, integrate by parts, etc.

The use of the Wolfram Language makes it possible to aim for the highest quality step-by-step solutions. Furthermore, our solutions are reviewed by a team of PhD’s who critique the accuracy, readability and didacticism of the solutions. As a result, Wolfram|Alpha can be thought of as a high-end virtual tutor—one with in-depth explanations that don’t miss a detail, for only $5/month. Over 80 major universities (including nearly the entire Ivy League) trust our solutions enough to have site licenses for Wolfram|Alpha Pro. This automatically puts this tutor in the palm of many students’ hands.

We’re excited to see step-by-step solutions grow, and we hope you are too. As always, your feedback is appreciated as we strive to make Wolfram|Alpha even more useful for students everywhere. Let us know what areas you’d like to see step-by-step solutions for!

]]>How far can one get in teaching computational thinking to high-school students in two weeks? Judging by the results of this year’s Wolfram High-School Summer Camp the answer is: remarkably far.

I’ve been increasingly realizing what an immense and unique opportunity there now is to teach computational thinking with the whole stack of technology we’ve built up around the Wolfram Language. But it was a thrill to see just how well this seems to actually work with real high-school students—and to see the kinds of projects they managed to complete in only two weeks.

We’ve been doing our high-school summer camp for 5 years now (as well as our 3-week Summer School for more experienced students for 15 years). And every time we do the camp, we figure out a little more. And I think that by now we really have it down—and we’re able to take even students who’ve never really been exposed to computation before, and by the end of the camp have them doing serious computational thinking—and fluently implementing their ideas by writing sometimes surprisingly sophisticated Wolfram Language code (as well as creating well-written notebooks and “computational essays” that communicate about what they’ve done).

Over the coming year, we’re going to be dramatically expanding our Computational Thinking Initiative, and working to bring analogs of the Summer Camp experience to as many students as possible. But the Summer Camp provides fascinating and important data about what’s possible.

So how did the Summer Camp actually work? We had a lot of applicants for the 40 slots we had available this year. Some had been pointed to the camp by parents, teachers, or previous attendees. But a large fraction had just seen mention of it in the Wolfram|Alpha sidebar. There were students from a range of kinds of schools around the US, and overseas (though we still have to figure out how to get more applicants from underserved populations). Our team had done interviews to pick the final students—and I thought the ones they’d selected were terrific.

The students’ past experience was quite diverse. Some were already accomplished programmers (almost always self-taught). Others had done a CS class or two. But quite a few had never really done anything computational before—even though they were often quite advanced in various STEM areas such as math. But almost regardless of background, it was striking to me how new the core concepts of computational thinking seemed to be to so many of the students.

How does one take an idea or a question about almost anything, and find a way to formulate it for a computer? To be fair, it’s only quite recently, with all the knowledge and automation that we’ve been able to build into the Wolfram Language, that it’s become realistic for kids to do these kinds of things for real. So it’s not terribly surprising that in their schools or elsewhere our students hadn’t really been exposed to such things before. But it’s now possible—and that means there’s a great new opportunity to seriously teach computational thinking to kids, and to position them to pursue the amazing range of directions that computational thinking is opening up.

It’s important, by the way, to distinguish between “computational thinking” and straight “coding”. Computational thinking is about formulating things in computational terms. Coding is about the actual mechanics of telling a computer what to do. One of our great goals with the Wolfram Language is to automate the process of coding as much as possible so people can concentrate on pure computational thinking. When one’s using lower-level languages, like C++ and Java, there’s no choice but to be involved with the detailed mechanics of coding. But with the Wolfram Language the exciting thing is that it’s possible to teach pure high-level computational thinking, without being forced to deal with the low-level mechanics of coding.

What does this mean in practice? I think it’s very empowering for students: as soon as they “get” a concept, they can immediately apply it, and do real things with it. And it was pretty neat at the Summer Camp to see how easily even students who’d never written programs before were able to express surprisingly sophisticated computational ideas in the Wolfram Language. Sometimes it seemed like students who’d learned a low-level language before were actually at a disadvantage. Though for me it was interesting a few times to witness the “aha” moment when a student realized that they didn’t have to break down their computations into tiny steps the way they’d been taught—and that they could turn some big blob of code they’d written into one simple line that they could immediately understand and extend.

The Summer Camp program involves several hours each day of lectures and workshops aimed at bringing students up to speed with computational thinking and how to express it in the Wolfram Language. But the real core of the program is every student doing an individual, original, computational thinking project.

And, yes, this is a difficult thing to orchestrate. But over the years we’ve been doing our Summer School and Summer Camp we’ve developed a very successful way of setting this up. There are a bunch of pieces to it, and the details depend on the level of the students. But here let’s talk about high-school students, and this year’s Summer Camp.

Right before the camp we (well, actually, I) came up with a list of about 70 potential projects. Some are quite specific, some are quite open-ended, and some are more like “metaprojects” (e.g. pick a dataset in the Wolfram Data Repository and analyze it). Some are projects that could at least in some form already have been done quite a few years ago. But many projects have only just become possible—this year particularly as a result of all our recent advances in machine learning.

I tried to have a range of nominal difficulty levels for the projects. I say “nominal” because even a project that can in principle be done in an easy way can also always be done in a more elaborate and sophisticated way. I wanted to have projects that ranged from the extremely well defined and precise (implement a precise algorithm of this particular type), to ones that involved wrangling data or machine learning training, to ones that were basically free-form and where the student got to define the objective.

Many of the projects in this list might seem challenging for high-school students. But my calculation (which in fact worked out well) was that with the technology we now have, all of them are within range.

It’s perhaps interesting to compare the projects with what I suggested for this year’s Summer School. The Summer School caters to more experienced students—typically at the college, graduate school or postdoc level. And so I was able to suggest projects that require deeper mathematical or software engineering knowledge—or are just bigger, with a higher threshold to achieve a reasonable level of success.

Before students start picking projects, it’s important that they understand what a finished project should look like, and what’s involved in doing it. So at the very beginning of the camp, the instructors went through projects from previous camps, and discussed what the “output” of a project should be. Maybe it’ll be an active website; maybe an interactive Demonstration; maybe it’ll be a research paper. It’s got to be possible to make a notebook that describes the project and its results, and to make a post about it for Wolfram Community.

After talking about the general idea of projects, and giving examples of previous ones, the instructors did a quick survey of this year’s suggestions list, filling in a few details of what the imagined projects actually were. After this, the students were asked to pick their top three projects from our list, and then invent two more potential projects of their own.

It’s always an interesting challenge to find the right project for each student—and it’s something I’ve personally been involved in at our Summer Camp for the past several years. (And, yes, it helps that I have decades of experience in organizing professional and research projects and figuring out the best people to do them.)

It’s taken us a few iterations, but here’s the approach we’ve found works well. First, we randomly break the students up into groups of a dozen or so. Then we meet with each group, going around the room and asking each student a little about themselves, their interests and goals—and their list of projects.

After we’re finished with each group, we meet separately and try to come up with a project for each student. Sometimes it’ll be one of the projects straight from our list. Sometimes it’ll be a project that the student themself suggested. And sometimes it’ll be some creative combination of these, or even something completely different based on what they said they were interested in.

After we think we’ve come up with a good project, the next step is to meet individually with each student and actually suggest it to them. It’s very satisfying that a lot of the time the students seem really enthused about the projects we end up suggesting. But sometimes it becomes clear that a project just isn’t a good fit—and then sometimes we modify it in real time, but more often we circle back later with a different suggestion.

Once the projects are set, we assign an appropriate mentor to each student, taking into account both the student and the subject of the project. And then things are off and running. We have various checkpoints, like that students have to write up descriptions of their projects and post them on the internal Summer Camp site.

I personally wasn’t involved in the actual execution of the projects (though I did have a chance to check in on a few of them). So it was pretty interesting for me to see at the end of the camp what had actually happened. It’s worth mentioning that our scheme is that mentors can make suggestions about projects, but all the final code in a project should be created by the student. And if one version of the project ends up being too difficult, it’s up to the mentor to simplify it. So however the final project comes out, it really is the student’s work.

Much of the time, the Summer Camp will be the first time students have ever done an original project. It could potentially seem daunting. But I think the fact that we give so many examples of other projects, and that everyone else at the camp is also doing a project, really helps. And in the end experiencing the whole process of going from the idea for a project to a real, finished project is incredibly educational—and seems to have a big effect on many of our students.

OK, so that’s the theory. So what actually happened at this year’s Summer Camp? Well, here are all the projects the students did, with the titles they gave them:

It’s a very interesting, impressive, and diverse list. But let me pick out a few semi-randomly to discuss in a bit more detail. Consider these as “case studies” for what high-school students can accomplish with the Wolfram Language in a couple of weeks at a summer camp.

One young man at our camp had quite a lot of math background, and told me he was interested in airplanes and flying, and had designed his own remote-control plane. I started thinking about all sorts of drone survey projects. But he didn’t have a drone with him—and we had to come up with a project that could actually be done in a couple of weeks. So I ended up suggesting the following: given two points on Earth, find how an airplane can get from one to the other by the shortest path that never needs to go above a certain altitude. (And, yes, a small-scale version of this would be relevant to things like drone surveying too.)

Here’s how the student did this project. First, he realized that one could think of possible flight paths as edges on a graph whose nodes are laid out on a grid on the Earth. Then he used the built-in `GeoElevationData` to delete nodes that couldn’t be visited because the elevation at that point was above the cutoff. Then he just used `FindShortestPath` to find the shortest path in the graph from the start to the end.

I thought this was a pretty clever solution. It was a nice piece of computational thinking to realize that the elements of paths could be thought of as edges on a graph with nodes removed. Needless to say, there were some additional details to get a really good result. First, the student added in diagonal connections on the grid, with appropriate weightings to still get the correct shortest path computation. And then he refined the path by successively merging line segments to better approximate a great-circle path, at each step using computational geometry to check that the path wouldn’t go through a “too-high” region.

You never know what people are going to come to Summer Camp with. A young man from New Zealand came to our camp with some overnight audio recordings from outside his house featuring occasional periods of (quite strange-sounding) squawking that were apparently the calls of one or more kiwi birds. What the young man wanted to do was automatic “kiwi voice recognition”, finding the calls, and perhaps distinguishing different birds.

I said I thought this wouldn’t be a particularly easy project, but he should try it anyway. Looking at what happened, it’s clear the project started out well. It was easy to pull out all intervals in his audio that weren’t just silence. But that broke up everything, including kiwi calls, into very small blocks. He solved that by the following interesting piece of code, that uses pattern matching to combine symbolic audio objects:

At this point it might just have worked to use unsupervised machine learning and `FeatureSpacePlot` to distinguish kiwi from non-kiwi sound clips. But machine learning is still quite a hit-or-miss business—and in this case it wasn’t a hit. So what did the student do? Well, he built himself a tiny lightweight user interface in a notebook, then started manually classifying sound clips. (Various instructors commented that it was fortunate he brought headphones…)

After classifying 200 clips, he used `Classify` to automatically classify all the other clips. He did a variety of transformations to the data—applying signal processing, generating a spectrogram, etc. And in the end he got his kiwi classifier to 82% accuracy: enough to make a reasonable first pass on finding kiwi calls—and going down a path to computational ornithology.

One young woman said she’d recently gotten a stress fracture in her foot that she was told was related to the force she was putting on it while running. She asked if she could make a computational model of what was going on. I have to say I was pessimistic about being able to do that in two weeks—and I suggested instead a project that I thought would be more manageable, involving studying possible gaits (walk, trot, etc.) for creatures with different numbers of legs. But I encouraged her to spend a little time seeing if she could do her original project—and I suggested that if she got to the stage of actually modeling bones, she could use our built-in anatomical data.

The next I knew it was a day before the end of the Summer Camp, and I was looking at what had happened with the projects… and I was really impressed! She’d found a paper with an appropriate model, understood it, and implemented it, and now she had an interactive demonstration of the force on a foot during walking or running. She’d even used the anatomical data to show a 3D image of what was happening.

She explained that when one walks there are two peaks in the force, but when one runs, there’s only one. And when I set her interactive demonstration for my own daily walking regimen I found out that (as she said was typical) I put a maximum force of about twice my weight on my foot when I walk.

At first I couldn’t tell if he was really serious… but one young man insisted he wanted to use machine learning to tell when a piece of fruit is ripe. As it happens, I had used pretty much this exact example in a blog post some time ago discussing the use of machine learning in smart contracts. So I said, “sure, why don’t you try it”. I saw the student a few times during the Summer Camp, curiously always carrying a banana. And what I discovered at the end of the camp was that that very banana was a key element of his project.

At first he searched the web for images of bananas described as “underripe”, “overripe”, etc., then arranged them using `FeatureSpacePlot`:

Then he realized that he could get more quantitative by first looking at where in color space the pixels of the banana image lay. The result was that he was actually able to define a “banana ripeness scale”, where, as he described it: “A value of one was assigned to bananas that were on the brink of spoilage. A value of zero was assigned to a green banana fresh off a tree. A value of 0.5 was assigned to the ‘perfect’ banana.” It’s a nice example of how something everyday and qualitative can be made computational.

For his project, the student made a “Banana Classifier” app that he deployed through the Wolfram Cloud. And he even had an actual banana to test it on!

One of my suggested projects was to implement “international or historical numeral systems”—the analogs of things like Roman numerals but for different cultures and times. One young woman fluent in Korean said she’d like to do this project, starting with Korean.

As it happens, our built-in `IntegerName` function converts to traditional Korean numerals. So she set herself the task of converting from Korean numerals. It’s an interesting algorithmic exercise, and she solved it with some nice, elegant code.

By that point, she was on a roll… so she decided to go on to Burmese, and Thai. She tried to figure out Burmese from web sources… only to discover they were inconsistent… with the result that she ended up contacting a person who had an educational video about Burmese numerals, and eventually unscrambled the issue, wrote code to represent it, and then corrected the Wikipedia page about Burmese numerals. All in all, a great example of real-world algorithm curation. Oh, and she set up the conversions as a Wolfram Language microsite on the web.

Can machine learning tell if something is funny? One young man at the Summer Camp wanted to find out. So for his project he used our Reddit API connection to pull jokes from the Jokes subreddit, and (presumably) non-jokes from the AskReddit subreddit. It took a bit of cleanup and data wrangling… but then he was able to feed his training data straight into the `Classify` function, and generated a classifier from which he then built a website.

It’s a little hard to know how well it works outside of “Reddit-style humor”—but his anecdotal study at the Summer Camp suggested about a 90% success rate.

Different projects involve different kinds of challenges. Sometimes the biggest challenge is just to define the project precisely enough. Other times it’s to get—or clean—the data that’s needed. Still other times, it’s to find a way to interpret voluminous output. And yet other times, it’s to see just how elegantly some particular idea can be implemented.

One math-oriented young woman at the camp picked “implementing checksum algorithms” from my list. Such algorithms (used for social security numbers, credit card numbers, etc.) are very nicely and precisely defined. But how simply and elegantly can they be implemented in the Wolfram Language? It’s a good computational thinking exercise—that requires really understanding both the algorithms and the language. And for me it’s nice to be able to immediately read off from the young woman’s code just how these checksum algorithms work…

How should one plot a function in 4D? I had a project in my list about this, though I have to admit I hadn’t really figured out how it should be done. But, fortunately, a young man at the Summer Camp was keen to try to work on it. And with an interesting mixture of computational and mathematical thinking, he created `ParametricPlot4D`—then did a bunch of math to figure out how to render the results in what seemed like two useful ways: as an orthogonal projection, and as a stereographic projection. A `Manipulate` makes the results interactive—and they look pretty neat…

In addition to my explicit list of project suggestions, I had a “meta suggestion”: take any dataset, for example from the new Wolfram Data Repository, and try to analyze and understand it. One student took a dataset about meteorite impacts; another about the recent Ebola outbreak in Africa. One young woman said she was interested in actuarial science—so I suggested that she look at something quintessentially actuarial: mortality data.

I suggested that maybe she could look at the (somewhat macabrely named) Death Master File. I wasn’t sure how far she’d get with it. But at the end of the camp I found out that she’d processed 90 million records—and successfully reduced them to derive aggregate survival curves for 25 different states and make an interactive Demonstration of the results. (Letting me conclude, for example, that my current probability of living to age 100 is 28% higher in Massachusetts than in Indiana…)

Each year when I make up a list of projects for the Summer Camp I wonder if there’ll be particular favorites. My goal is actually to avoid this, and to have as uniform a distribution of interest in the projects as possible. But this year “Use Machine Learning to Identify Polyhedra” ended up being a minor favorite. And one consequence was that a student had already started working on the project even before we’d talked to him—even though by that time the project was already assigned to someone else.

But actually the “recovery” was better than the original. Because we figured out a really nice alternative project that was very well suited to the student. The project was to take images of regular tilings, say from a book, and to derive a computational representation of them, suitable, say, for `LatticeData`.

The student came up with a pretty sophisticated approach, largely based on image processing, but with a dash of computational geometry, combinatorics and even some cluster analysis thrown in. First, he used fairly elaborate image processing to identify the basic unit in the tiling. Then he figured out how this unit was arranged to form the final tiling. It ended up being about 102 lines of fairly dense algorithmic code—but the result was a quite robust “tiling OCR” system, that he also deployed on the web.

In my list I had a project “Identify buildings from satellite images”. A few students thought it sounded interesting, but as I thought about it some more, I got concerned that it might be really difficult. Still, one of our students was a capable young man who already seemed to know a certain amount about machine learning. So I encouraged him to give it a try. He ended up doing an impressive job.

He started by getting training data by comparing satellite images with street maps that marked buildings (and, conveniently, starting with the upcoming version of the Wolfram Language, not only streets maps but also satellite images are built in):

Then he used `NetChain` to build a neural net (based on the classic LeNet network, but modified). And then he started trying to classify parts of images as “building” or “not building”.

The results weren’t at all bad. But so far they were only answering the question “is there a building in that square?”, not “where is there a building?”. So then—in a nice piece of computational thinking—the student came up with a further idea: just have a window pan across the image, at each step estimating the probability of building vs. not-building. The result was a remarkably accurate heat map of where buildings might be.

It’d be a nice machine learning result for anyone. But as something done by a high-school student in two weeks I think it’s really impressive. And another great example of what’s now possible at an educational level with our whole Wolfram Language technology stack.

OK, so our Summer Camp was a success, and, with luck, the students from it are now successfully “launched” as independent computational thinkers. (The test, as far as I’m concerned, is whether when confronted with something in their education or their lives, they routinely turn to computational thinking, and just “write a program to solve the problem”. I’m hopeful that many of them now will. And, by the way, they immediately have “marketable skills”—like being able to do all sorts of data-science-related things.)

But how can we scale up what we’ve achieved with the Summer Camp? Well, we have a whole Computational Thinking Initiative that we’ve been designing to do just that. We’ll be rolling out different parts over the next little while, but one aspect will be doing other camps, and enabling other people to also do camps.

We’ve now got what amounts to an operations manual for how to “do a camp”. But suffice it to say that the core of it is to have instructors with good knowledge of the Wolfram Language (e.g. to the level of our Certified Instructor program), access to a bunch of great students, and use of a suitable venue. Two weeks seems to be a good length, though longer would work too. (Shorter will probably not be sufficient for students without prior experience to get to the point of doing a real project.)

Our camp is for high-school students (mainly aged 15 through 17). I think it would also be possible to do a successful camp for advanced middle-school students (maybe aged 12 and 13). And, of course, our long-running Summer School provides a very successful model for older students.

Beyond camps, we’ve had for some time a mentorships program which we will be streamlining and scaling up—helping students to work on longer-term projects. We’re also planning a variety of events and venues in which students can showcase their computational thinking work.

But for now it’s just exciting to see what was achieved in two weeks at this year’s Summer Camp. Yes, with the tech stack we now have, high-school students really can do serious computational thinking—that will make them not only immediately employable, but also positioned for what I think will be some of the most interesting career directions of the next few decades.

*To comment, please visit the copy of this post at the Stephen Wolfram Blog »*

This year’s Wolfram Summer School will be held at Bentley University in Waltham, Massachusetts, from June 18 to July 7, 2017.

Maybe you’re a researcher who wants to study the dynamics of galaxies with cellular automata. Perhaps you’re an innovator who wants to create a way to read time from pictures of analog clocks or build a new startup with products that use RFID (radio-frequency identification) to track objects. You might be an educator who wants to build an algebra feedback system or write a textbook that teaches designers how to disinvent the need for air conditioning. These projects show the diversity and creativity of some of our recent Summer School students. Does this sound like you? If so, we want you to join us this summer!

We offer three tracks: Science, Technology & Innovation and Educational Innovation. As a student, you will design and execute a challenging, practical project and acquire an incredible amount of knowledge along the way. At the Summer School, you will gain skills that are useful universally and network with a community of smart, creative people from all over the world. Many of our students go on to work for us in some capacity, whether that be as an employee, intern, ambassador, etc.

If you’re accepted to the Summer School, you will be mentored by top-notch staff who have made powerful and important contributions to the Wolfram Language, Mathematica, Wolfram|Alpha and more—including the inventor of the Wolfram Language, Stephen Wolfram.

The Wolfram Language has a vast depth of built-in algorithms and knowledge. Recent feature additions include a dynamic and ever-growing set of machine learning capabilities (including neural networks), as well as advancements in the areas of image processing, visualization, 3D printing, geographic information systems, natural language processing and computational linguistics and database and web frameworks, among many others.

Whatever your background, profession or future plans, the Wolfram Summer School can equip you with the computational tools you need to bring any idea to reality.

For more information, visit our website.

]]>Computational thinking needs to be an integral part of modern education—and today I’m excited to be able to launch another contribution to this goal: Wolfram|Alpha Open Code.

Every day, millions of students around the world use Wolfram|Alpha to compute answers. With Wolfram|Alpha Open Code they’ll now not just be able to get answers, but also be able to get code that lets them explore further and immediately apply computational thinking.

It takes a lot of sophisticated technology to make this possible. But to the user, it’s simple. Do a computation with Wolfram|Alpha. Now in almost every section of the output you’ll see an “Open Code” link. Click it and Wolfram|Alpha will generate code for you, then open it in a fully runnable and editable notebook that you can immediately use in the Wolfram Open Cloud:

The sections of the notebook parallel the sections of your Wolfram|Alpha output. But now each section contains not results, but instead core Wolfram Language code needed to get those results. You can run any piece of code by clicking the [>] button (or typing Shift+Enter):

But the really important thing is that right there on the web you can change and extend the code, and then instantly run it again:

If all someone wants is a single, quick result, then classic Wolfram|Alpha should be all they’ll need. But as soon as they want to go further—that’s where Wolfram|Alpha Open Code comes in.

Let’s say you just got a mathematical result from Wolfram|Alpha:

But then you wonder: “what happens for a whole range of exponents?” Well, it’s going to get pretty complicated to tell Wolfram|Alpha what you want just using natural language. But it’s easy to say what to do by giving a tiny bit of Wolfram Language code (and, yes, you can interactively spin those 3D surfaces around):

You could give code to interactively change the parameters too:

Starting with Wolfram|Alpha, then extending using the Wolfram Language, is very powerful. Here’s what happens with some real-world data. Start in Wolfram|Alpha, then get the underlying Wolfram Language code (it can be made shorter, but then it’s a little less clear what’s going on):

Evaluate the code to get a time series. Then plot it. And divide by the corresponding result for the US:

An important feature of notebooks is that they’re full, computable documents—and you can add whatever you want to them. You can do a whole series of computations. You can put in text to annotate what you’re doing. You can add section headings. You can edit out parts you don’t need. And so on. And of course you can do all of this in the cloud, using any modern web browser.

Wolfram|Alpha Open Code is going to be really useful to a lot of people—not just students. But when I invented it my immediate objective was very much educational: I wanted to be able to give the millions of students who use Wolfram|Alpha every day a taste of the power of code, and what can be achieved if one learns about code and computational thinking.

Computational thinking is a critically important skill for the future. And after 30 years of development we’re at the exciting point with the Wolfram Language of being able to directly teach serious computational thinking to a very wide range of students. I see Wolfram|Alpha Open Code as opening a window into the world of computational thinking for all the students who use Wolfram|Alpha.

There’s no learning curve to climb with Wolfram|Alpha: you just type in your question, directly in natural language. But now with Wolfram|Alpha Open Code you can explicitly see how your question gets interpreted computationally. And as soon as you want to go further, you’re immediately doing computational thinking, and writing code. You’re not doing an abstract coding exercise, or creating code in some toy context. You’re immediately using code to formulate computational ideas and get results about something you’re working on.

Of course, what makes this feasible is the character of the Wolfram Language—and its uniquely high-level knowledge-based nature. Because that’s what allows real computations that you want to do to be expressed in small amounts of code that can readily be understood and modified or extended.

Yes, the Wolfram Language has a definite structure and syntax, based on definite principles. But that’s a lot of what makes it easy to understand and to write. And in a notebook you’re always getting suggestions about what to type—and if your browser language is set to something other than English you’ll often get annotations in that language too. And the code you get from using Wolfram|Alpha Open Code will continually illustrate the core principles of the Wolfram Language.

Over the course of the past year, we’ve introduced two important paths into computational thinking, both supported by Wolfram Programming Lab, and available free in the Wolfram Open Cloud.

The first path is to start from Explorations: small projects created using code, that a student can immediately dive into, and then modify and interact with. The second path is to systematically learn the Wolfram Language, for example using my book *An Elementary Introduction to the Wolfram Language*.

And now Wolfram|Alpha Open Code provides a third path: start from a question that a student has asked, and then automatically generate custom code that provides a starting point for further work and thinking.

It’s a nice complement to the other two paths—and perhaps it’ll often provide encouragement to pursue one or the other of them. But it’s a perfectly good path all by itself—and students can go a long way following it.

Of course, under the hood, there’s a remarkable amount of sophisticated technology that’s being used. There’s the whole natural-language understanding system of Wolfram|Alpha that’s understanding the original question. There’s the Wolfram|Alpha computational knowledge system that’s formulating what pieces of code to generate. Then there’s the Wolfram Open Cloud, providing an interactive notebook environment on the web capable of running the code. And at the center of all of it is the Wolfram Language, with its whole integrated design and vast built-in capabilities and knowledge.

It’s taken 30 years of development to get to this point. But now we’ve been able to put everything together to create a very powerful path for students to get into computational thinking.

And I have to say that for me it’s exciting to think about kids out there using Wolfram|Alpha just for homework, but then pressing the Open Code button, and suddenly being transported into the world of code and computational thinking—and perhaps starting on a lifelong journey.

I’m thrilled to be able to provide the tools that make this possible. Try it out. Tell us what you think. Share what you do, and show others what’s possible.

]]>Computational thinking can be integrated across the curriculum. It is not just the purview of the math teacher or the computer club, but a key instructional tool for educators from all disciplines. For example, using the Wolfram Language to teach computational thinking, English teachers can explore palindromes, history students can explore main concepts from the Gettysburg Address and science teachers can examine dinosaurs’ weights.

How does a busy teacher apply computational thinking in the classroom? Easily: computational thinking provides a framework for learning, which makes concepts easier for students to understand. It incorporates real-world math into students’ everyday lives.

For instance, using the Wolfram Programming Lab during Computer Science Education Week, you can teach students to think computationally about geography. The “Miles around You” starter Exploration will allow your students to see what exists in their vicinity. Students can make a map of the location, then draw a disk of any size around it and zoom in and out to gain perception. How many sites show on the map at a radius of 100 miles? What about 150 miles?

This exercise requires no knowledge of the Wolfram Language. The activity can last as long or as short as the students and teacher desire. Yet it introduces in a relevant way how computational thinking can answer questions. As students advance with their Wolfram Language engagement, they can complete Wolfram challenges on a variety of subjects, from basketball scores to Pig Latin.

Students are often bored with math in school because they do not see the real-world applications of their lessons. Computer-Based Math education lets students use computers at school the same way they would in their everyday lives: with the computers, not the humans, performing rote calculations. Computational thinking helps students discern which calculations the computer needs to solve so they can explore higher concepts. For instance, if your students are basketball fans, they can take a Wolfram challenge to discover how a basketball team can reach a certain score. The many applications of computational thinking make it easy to incorporate math throughout the curriculum.

Computational thinking in the classroom encourages student engagement when students see the results of their efforts. Maybe your students are excited about the upcoming holidays. Why not let them create a unique decoration in the Wolfram Demonstrations Project?

This example and other Wolfram Demonstrations are accessible ways to explore computational thinking at any level of classroom. Once you’ve played with a few interactive examples, contributing your own Demonstration might be a fun and informative way for you and your students to spend an Hour of Code.

The Wolfram Summer Programs are one example of a place where students learn computational thinking through achieving their personal goals. This year at the Wolfram Summer School in Armenia, students developed their original ideas into working prototypes. Prior to the Armenian and other Wolfram camps, students prepare by completing homework assignments. Students can do the same in a flipped classroom, where they experience material before coming to class and arrive at the in-person lesson ready to engage with an activity.

If you’ve flipped your classroom, then computational thinking can be easily integrated into this environment by introducing your students to the Wolfram Language and using it to work with real-world data. An Elementary Introduction to the Wolfram Language Training Series will provide the pre-class materials for your students. Using this video series, you and your students can learn the basics of the Wolfram Language. Maybe you’re teaching an astronomy lesson this week. The Real-World Data video can introduce your students to computational thinking by using the Wolfram Language to explore planets, stars, galaxies and more—perhaps during the Hour of Code.

With computational thinking, students will learn by doing. Allow students to follow their own interests. Let them choose projects that intrigue them or relate to something they are already undertaking in class. Work computational thinking into the syllabus. Computational thinking is part of the learning process, not a single lesson.

Computational thinking can lead students to answer big questions. Are your students interested in public health? Teaming up with each other—and perhaps members of Wolfram Community—they can use the Wolfram Language to model the spread of a global disease outbreak.

Professors can teach computational thinking too. Perhaps you’re a humanities faculty member. Why not flex your own computational thinking by learning to analyze your data with the Wolfram Language? You and your students may be surprised by what you discover.

Here at Wolfram, there are more plans to help educators teach a generation of students computational thinking. For Computer Science Education Week, we will be hosting another Hour of Code event: middle- and high-school students will go on a computation adventure. If you’re unable to join us in person, why not host your own event?

If you’ve decided to have an Hour of Code, perhaps spend your time having students create tweetable programs—code that fits into 140 characters. Or analyze sea level rise, like Anush Mehrabyan did during the 2015 Wolfram High School Summer Camp. Or create a camera-controlled musical instrument. The examples are inspiring; the possibilities are exciting.

Whatever you decide to do with your students, don’t confine computational thinking and Computer-Based Math to Computer Science Education Week or an Hour of Code. Have fun exploring—and please let us know what you and your students create and learn.

]]>Matt shared how he was pretty new to coding before MHacks V. That was before Olivia, a web cartoonist who also studies mathematics, introduced him to hackathons and the Wolfram Language. They read our blog post about creating popular curves with Fourier series, and realized they could use the same idea to create drawing guides on the fly. The Wolfram Language, with built-in cloud technology and over 5,000 functions, proved perfect not only for bringing their hackathon idea to life but also, as Matt says, “for making it so easy to get in there and not be scared of programming.” Watch the video below as Olivia and Matt describe their journey to victory at MHacks V.

Since DrawAnything and MHacks V, Matt has continued to expand his programming abilities, while both Olivia and Matt have grabbed more hackathon prizes, including a trip to compete in Taiwan for HackNTU. And they have no doubts about keeping Wolfram technology at the top of their coding toolbox for future competitions.

Are you interested in doing a hackathon with Wolfram technology, need an idea or want to see some of the other winning hacks? Then visit this page to learn more about using Wolfram tech at hackathons.

Be sure to check out other Wolfram Language stories like Olivia and Matt’s on our Customer Stories pages.

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Throughout the two weeks, students learned the Wolfram Language from faculty and a variety of guest speakers. They had the opportunity to see Stephen Wolfram give a “live experiment” and speak about the company, entrepreneurialism and the future of technology. Students also heard from guest speakers such as Etienne Bernard and Christopher Wolfram, who showed off other aspects of the Wolfram Language.

Although students spent a vast amount of time hard at work on their projects, they also had many laughs throughout the program. They participated in group activities such as the human knot, the Spikey photo scavenger hunt and the toothpick-and-gumball building contest, as well as weekend field trips to the Boston Museum of Science and the New England Aquarium.

The students completed phenomenal projects on a wide range of topics, ranging from geospatial analysis, textual analysis, machine learning and neural nets, physical simulations, pure math and much more. Here are just a few projects:

“Where Is Downtown?” by Kaitlyn Wang. This project uses cluster analysis and data from Yelp and Wikipedia obtained with ServiceConnect to estimate the polygon of a city’s downtown.

“Where Will Your Balloon Go?” by Cyril Gliner. This project uses WindVectorData to simulate where a balloon would travel when let go at a given time and location on Earth.

“Tiling Polyominoes Game,” by Jared Wasserman. This drag-and-drop game asks the user to place the polyominoes on the right to cover all the gray areas on the left without overlapping the tiles.

“Automatic Emoji Annotator!” by Marco Franco. This project imported over 50,000 tweets to create a neural network that gives the emojis that best represent a sentence.

“Automated Face Anonymizer,” by Max Lee. This is perhaps the project I found to be the most fun, only because it involved me. It anonymizes an image by replacing faces with my head.

This word cloud represents the most common Wolfram Language symbols the students collectively used in their projects:

Here are frequencies of the 30 most commonly used symbols by the students. The first few symbols were used so frequently, a log scale is used:

How do these frequencies compare with normal, everyday usage of the Wolfram Language? We can answer this with the `WolframLanguageData` property `"Frequencies"`. It turns out the usage frequencies from camp versus normal usage have a correlation coefficient of about 0.8. Here’s how the first few symbols compare:

Lastly, we can use the `WolframLanguageData` property `"RelatedSymbols"` and `CommunityGraphPlot` to group the symbols used by the students into clusters based on topic. It shows how eclectic this group of 39 students’ projects were:

Computational thinking is going to be a defining feature of the future—and it’s an incredibly important thing to be teaching to kids today. There’s always lots of discussion (and concern) about how to teach traditional mathematical thinking to kids. But looking to the future, this pales in comparison to the importance of teaching computational thinking. Yes, there’s a certain amount of traditional mathematical thinking that’s needed in everyday life, and in many careers. But computational thinking is going to be needed everywhere. And doing it well is going to be a key to success in almost all future careers.

Doctors, lawyers, teachers, farmers, whatever. The future of all these professions will be full of computational thinking. Whether it’s sensor-based medicine, computational contracts, education analytics or computational agriculture—success is going to rely on being able to do computational thinking well.

I’ve noticed an interesting trend. Pick any field X, from archeology to zoology. There either is now a “computational X” or there soon will be. And it’s widely viewed as the future of the field.

Try the example computations from this blog post in the Wolfram Open Cloud »

So how do we prepare the kids of today for this future? I myself have been involved with computational thinking for nearly 40 years now—building technology for it, applying it in lots of places, studying its basic science—and trying to understand its principles. And by this point I think I have a clear view of what it takes to do computational thinking. So now the question is how to educate kids about it. And I’m excited to say that I think I now have a good answer to that—that’s based on something I’ve spent 30 years building for other purposes: the Wolfram Language. There have been ways to teach the mechanics of low-level programming for a long time, but what’s new and important is that with all the knowledge and automation that we’ve built into the Wolfram Language we’re finally now to the point where we have the technology to be able to directly teach broad computational thinking, even to kids.

I’m personally very committed to the goal of teaching computational thinking—because I believe it’s so crucial to our future. And I’m trying to do everything I can with our technology to support the effort. We’ve had Wolfram|Alpha free on the web for years now. But now we’ve also launched our Wolfram Open Cloud—so that anyone anywhere can start learning computational thinking with the

Wolfram Programming Lab, using the Wolfram Language. But this is just the beginning—and as I’ll discuss here, there are many exciting new things that I think are now possible.

But first, let’s try to define what we mean by “computational thinking”. As far as I’m concerned, its intellectual core is about formulating things with enough clarity, and in a systematic enough way, that one can tell a computer how to do them. Mathematical thinking is about formulating things so that one can handle them mathematically, when that’s possible. Computational thinking is a much bigger and broader story, because there are just a lot more things that can be handled computationally.

But how does one “tell a computer” anything? One has to have a language. And the great thing is that today with the Wolfram Language we’re in a position to communicate very directly with computers about things we think about. The Wolfram Language is knowledge based: it knows about things in the world—like cities, or species, or songs, or photos we take—and it knows how to compute with them. And as soon as we have an idea that we can formulate computationally, the point is that the language lets us express it, and then—thanks to 30 years of technology development—lets us as automatically as possible actually execute the idea.

The Wolfram Language is a programming language. So when you write in it, you’re doing programming. But it’s a new kind of programming. It’s programming in which one’s as directly as possible expressing computational thinking—rather than just telling the computer step-by-step what low-level operations it should do. It’s programming where humans—including kids—provide the ideas, then it’s up to the computer and the Wolfram Language to handle the details of how they get executed.

Programming—and programming education—have traditionally been about telling a computer at a low level what to do. But thanks to all the technology we’ve built in the Wolfram Language, one doesn’t have to do that any more. One can express things at a much higher level—so one can concentrate on computational thinking, not mere programming.

Yes, there’s certainly a need for some number of software engineers in the world who can write low-level programs in languages like C++ or Java or JavaScript—and can handle the details of loops and declarations. But that number is tiny compared to the number of people who need to be able to think computationally.

The Wolfram Language—particularly in the form of Mathematica—has been widely used in technical research and development around the world for more than a quarter of a century, and endless important inventions and discoveries have been made with it. And all these years we’ve also been progressively filling out my original vision of having an integrated language in which every possible domain of knowledge is built in and automated. And the exciting thing is that now we’ve actually done this across a vast range of areas—enough to support all kinds of computational thinking, for example across all the fields traditionally taught in schools.

Seven years ago we released Wolfram|Alpha—which kids (and many others) use all the time to answer questions. Wolfram|Alpha takes plain English input, and then uses sophisticated computation from the Wolfram Language to automatically generate pages of results. I think Wolfram|Alpha is a spectacular illustration—for kids and others—of what’s possible with knowledge-based computation in the Wolfram Language. But it’s only intended for quick “drive by” questions that can be expressed in fairly few words, or maybe a bit of notation.

So what about more complicated questions and other things? Plain English doesn’t work well for these. To get enough precision to be able to get definite results one would end up with something like very elaborate and incomprehensible legalese. But the good news is that there’s an alternative: the Wolfram Language—which is built specifically to make it easy to express complex things, yet is always precise and definite.

It doesn’t take any skill to use Wolfram|Alpha. But if one wants to go further in taking advantage of what computation makes possible, one has to learn more about how to formulate and structure what one wants. Or, in other words, one needs to learn to do computational thinking. And the great thing is that the Wolfram Language finally provides the language in which one can do that—because, through all the work we’ve put into it, it’s managed to transcend mere programming, and as directly as possible support computational thinking.

So what’s it like when kids are first exposed to the Wolfram Language? As part of my effort to understand how to teach computational thinking, I’ve spent quite a bit of time in the last few years using the Wolfram Language with kids. Sometimes it’s with large groups, sometimes with small groups—and sometimes I’ll notice a kid at some grown-up event I’m at, and end up getting out my computer and spending time with the kid rather than the grown ups. I’ve worked with high-school-age kids, and with middle-school-age (11–14) ones.

If it’s one kid, or a small group, I’ll always insist that the kids do the typing. Usually I’ll start off with something everyone knows. Get the computer to compute 2+2. They type it in, and they can see that, yes, the computer gives them the result they know:

They’ll often then try some other basic arithmetic. It’s very important that the Wolfram Language lets them just enter input, and immediately see output from it. There are no extra steps.

After they’ve done some basic arithmetic, I’ll usually suggest they try something that generates more digits:

Often they’ll ask if it’s OK, or if somehow the long number will break the computer. I encourage them to try other examples, and they’ll often do computations that instantly generate pages and pages of numbers. These kinds of big-number computations are something we’ve been able to do for decades, but kids still always seem to get very excited by them. I think the point is that it lets them see that, yes, a computer really can compute nontrivial things. (Just think how long it would take you to compute all those digits…)

After they’ve done some basic arithmetic, it’s time for them to try some other functions. The most common function that I end up starting with is `Range`:

`Range` is good because it’s easy for kids to see what it does—and they quickly get the sense that, yes, they can tell the computer to do something, and it will do it. `Range` is also good because it’s easy to use it to generate something satisfyingly big. Often I’ll suggest they try `Range[1000]`. They’ll ask if `Range[10000]` is OK too. I tell them to try it…

I think I do something different with every kid or group of kids I deal with. But a pretty common next step is to see how to visualize the list we’ve made:

If the kids happen to be into math, I might try next making a table of primes:

And then plotting them:

For kids who perhaps don’t think they like math—or tech in general—I might instead make some colors:

Maybe we’d try blending red and blue to make purple:

Maybe we’d pick up the current image from the camera:

And we’d find all the “edges” in it:

We might also get a bit more sophisticated with color:

Perhaps then we’d go in another direction, getting a list of common words in English (I’d also try another language if any of the kids know one):

If the kids are into language arts, we might try generating some random words:

We might see how to use `StringTake` to take the first letter of each word:

Then use `WordCloud` to make a word cloud and see the relative frequencies of first letters:

Some kid might ask “what about the first two letters?”. Then we’d be off trying that (yes, there’s some computational thinking involved in that `UpTo`):

We might talk for a bit about how many words start with “un-” etc. And maybe we’d investigate some of those words. We could go on and look at translations of words:

Actually, it’d be easy to go on for hours just doing things with what I’ve talked about so far. But let’s look at some other examples. A big thing about the Wolfram Language is that it knows about lots of real-world data. I’d typically build this up through a bunch of steps, but here’s an example of making a collage of flags of countries in Europe, where the size of each flag is determined by the current population of the country:

Since we happen to have talked about color, it’s fun to see where in color space the flags lie (apparently not many “pink countries”, for example):

A big theme is that the Wolfram Language lets one do not just abstract computation, but computation based on real-world knowledge. The Wolfram Language covers a huge range of areas, from traditional STEM-like areas to art, history, music, sports, literature, geography and so on. Kids often like doing things with maps.

We might start from where we are (`Here`). Or from some landmark. Like here’s a map with a 100-mile-radius disk around the Eiffel tower:

Here’s a “powers of 10” sequence of images:

So what about history, for example? How can the Wolfram Language engage with that? Actually, it’s full of historical knowledge. About countries (plot the growth and decline of the Roman Empire), or movies (compare movie posters over time), or, for example, words. Like here’s a comparison of the use of “horse” and “car” in books over the last 300 years:

Try the same thing for names of countries, or inventions, or whatever; there’s always lots of history to discuss.

There are so many different directions to go. Here’s another one: graphics. Let’s make a 3D sphere:

It’s always fun for kids that they can make something like this in 3D and move it around. If they’re on the more sophisticated end, we might build up 3D graphics like this from 100 random spheres with random colors:

Kids of all ages like making interactive stuff. Here’s a simple “adjustable Cyclops eye” that one can easily build up to in stages:

Another thing I sometimes do is have the Wolfram Language make sound. Here’s a random sequence of musical notes:

There are so many directions to go. For the budding medical person, there’s anatomy in 3D—and you can pick out the geometry of a bone and 3D print it. And so on and so on.

I’d never seriously tried working with kids (though, yes, I do have four kids of my own) before launching into my recent efforts on computational thinking. So I didn’t know quite what to expect. People I talked to seemed somewhat amused about the contrast to my usual life of hard-driving technology development. And they kept on bringing up a couple of issues they thought might be crippling to what I wanted to do. The first was that they were skeptical that kids would actually be able to type raw code in the Wolfram Language; they thought they’d just get too confused and tangled up with syntax and so on. And the second issue is that they didn’t think kids would be motivated to do anything with code unless it led to creating a game they could play.

One of the nice features of working with kids is that if you give them the chance, they’ll very quickly make it very clear to you what works with them and what doesn’t. So what actually happens? Well, it turns out that in my experience neither of the potential problems people brought up ends up being a real issue at all. But the reasons for this are quite interesting, and not particularly what I would have expected.

About typing code, one thing to realize is that in today’s world, most middle-school-age kids are quite used to typing, or at least typing text. Sometimes when they start typing code they at first have to look where the [ ] keys are, or even where the + is. But they don’t have any fundamental problem with typing. They’re also quite used to learning precise rules for how things work (“i comes before e …” in English spelling; the order of operations in math; etc.). So learning a few rules like “functions use square brackets” or “function names start with capital letters” isn’t a big deal. And of course in the Wolfram Language there’s nothing like all those irregularities that exist in a natural language like English.

When I watch kids typing code, the automatic hints we provide are quite important (brackets being purple until they’re matched; things turning red if they’re in the wrong place; autocompletions being suggested for everything; etc.). But the bottom line is that despite the theoretical concerns of adults, actual kids seem to find it extremely easy to type syntactically correct code in the Wolfram Language. In fact, I’ve been amazed at how quickly many kids “get it”. Having seen just a few examples, they immediately generalize. And the great thing is that because the Wolfram Language is designed in a very consistent way, the generalizations they come up with actually work. It’s heartwarming for me as the language designer to see this. Though of course, to the kids it’s just obvious that something must work this-or-that way, and they don’t imagine that it took effort to design it that way.

OK, so kids can type Wolfram Language code. But do they want to? Lots of kids like playing games on computers, and adults often think that’s all they’ll be interested in creating on computers too. But in my observation, this simply isn’t true. The most important thing for most kids about the Wolfram Language is that they can immediately “do something real” with it. They can type whatever code they want, and immediately get the computer to do something for them. They can create pictures or sounds or text. They can make art. They can do science. They can explore human languages. They can analyze Pokémon (yes, the Wolfram Language has extensive Pokémon data). And yes, if they really want to, they can make games.

In my experience, if you ask kids before they’ve seen the Wolfram Language what they might be interesting in programming they’ll often say games. But as soon as they’ve actually seen what’s possible in the Wolfram Language, they’ll stop talking about games, and they’ll want to do something “real” instead.

It’s only very a recent thing (and it’s basically taken 30 years of work) that the Wolfram Language has got to the point where I think it provides an instantly compelling way for kids to learn computational thinking. And actually, it’s not just the raw language—and all the knowledge it contains—that’s important: it’s also the environment.

The first point is that the Wolfram Notebook concept that we invented nearly 30 years ago is a really good way for kids (and others) to interact with the language. The idea of a notebook is to have an interactive document that freely mixes code, results, graphics, text and everything else. One can build up a computation in a notebook, typing code and getting results right there in the document. The results can be dynamic—with their own automatically generated user interfaces. And one can read—or write—explanations or instructions directly in the notebook. It’s taken decades to polish all aspects of notebooks. But now we’ve got an extremely efficient and wonderful environment in which to work and think—and learn computational thinking.

For many years, notebooks and the Wolfram Language were basically available only as desktop software. But now—after a huge software engineering effort—they’re also available in the cloud, directly in a web browser, or on mobile devices. So that means that any kid can just go to a web browser, and immediately start interacting with the Wolfram Language—creating or editing a notebook, and writing whatever code they want.

It takes a big stack of technology to make this possible. And building it has taken a big chunk of my life. It’s been very satisfying to see so many great leading-edge achievements made over the years with our technology. And now I’m really excited to see what’s possible in using it to spread computational thinking to future generations.

I made the decision when we created Wolfram|Alpha to make it available free on the web to the world. And it’s been wonderful to see so many people—and especially kids—using it every day. So a few months ago, when the technology was ready, I made the decision also to provide free access to the whole Wolfram Language in our Wolfram Open Cloud—and to set it up so kids (and others) could learn computational thinking there.

Wolfram|Alpha is set up so anyone can ask it questions, in plain English. And it’s turned out to be great—among other things—as a way to support education in lots of fields. But if one wants to learn true computational thinking for the future, then one’s got to go beyond asking questions in plain English. And that’s where the Wolfram Language comes in.

So what’s the best way to get started with the Wolfram Language, and the computational thinking it makes possible? There are probably many answers to this, that, among other things, depend on the details of the environment and resources that different kids have available. I’d like to think I’ve personally done a decent job working directly with kids—and for example at our Wolfram Summer Camp for high-school students I’ve seen very good things achieved with direct personal mentoring.

But it’s also important to have “self service” solutions—and one thing I’ve done to contribute to that is to write a book called *An Elementary Introduction to the Wolfram Language*. It’s really a book about computational thinking. It doesn’t assume any previous knowledge of programming, or, for example, of math. But in the course of the book it gets people to the point where they can routinely write real programs that do things they’re interested in.

The book is available free online. And it’s also got exercises—which are automatically graded in the cloud. I originally intended the book for high school and up. But it’s turned out that there’s ended up being quite a collection of middle-school students (aged 11 and up) who have enthusiastically worked their way through it—even as the book has also turned out to be used for things like graduate math courses, trainings at banks, and educating professional software developers.

There’s a (free) online course based on my book that will be available soon, and I know there are quite a few courses under development that use the book to teach modern programming and computational thinking.

But, OK, when a kid walks up to their web browser to learn computational thinking and the Wolfram Language, where can they actually go? A few months ago we launched Wolfram Programming Lab as an answer to this. There’s a version in the Wolfram Open Cloud that’s free (and doesn’t even require login so long as you don’t want to save your work).

Wolfram Programming Lab has two basic branches. The first is a collection of Explorations. Each Exploration is a notebook that’s set up to contain code you can edit and run to do something interesting. After you’ve gone through the code that’s already there, the notebook then suggests ways to go further, and to explore on your own.

Explorations let you get a taste of the Wolfram Language and computational thinking. Kids can typically get through the basics of several in an hour. In a sense they’re like “immersion language learning”: You start from code that “fluent speakers” might write, then you interact with it.

But Wolfram Programming Lab provides a second branch too: an interactive version of my book, that lets people go step-by-step, building up from a very simple start, and progressively creating more and more sophisticated code.

You can use Wolfram Programming Lab entirely through a web browser, in the cloud. But there’s also a desktop version that runs on any standard computer—and lets you get really zippy local interactivity, as well as letting you do bigger computations if you want. And if you have a Raspberry Pi computer, the desktop version of Wolfram Programming Lab comes bundled right with the operating system, including special features for getting data from sensors connected to the Raspberry Pi.

I’ve wanted to make sure that Wolfram Programming Lab is suitable for any kid, anywhere, whether or not they’re embedded in an educational environment that can support what they’re doing. And from what we can tell, this seems to be working nicely—though it certainly helps when kids have actual people they can work with. We plan to set up the structure for informal networks to support this, among other things using the existing, very active Wolfram Community. But we’re also setting things up so Wolfram Programming Lab can easily fit into existing, organized, educational settings—not least by using the Wolfram Language to create some of the world’s best educational analytics to analyze student progress.

It’s worth mentioning that one of the great things about our whole Wolfram Cloud infrastructure is that it lets anyone—whether they’re students or teachers—directly publish things on the web for the world to use. And in Wolfram Programming Lab, for example, it’s routine to end up deploying an app on the web as part of an Exploration.

We’re still in the early days of understanding all the nuances of actually deploying Wolfram Programming Lab in every possible learning environment—and we’re steadily advancing on many fronts. A little while ago I happened to be talking to some kids at a school in Korea, and asked them whether they thought they’d be able to learn the Wolfram Language. One of the kids responded that she thought it looked easy—except for having to read all the English in the names of the functions.

Well, that got me thinking. And the result was that we introduced multilingual code captions, that annotate code in a whole range of different languages. You still type Wolfram Language code using standard function names, but you get an instant explanation in your native language. (By the way, there are also versions of my book that will be available in various languages.)

OK, so I’ve talked a bit about the mechanics of teaching computational thinking. But where does computational thinking fit into the standard educational curriculum? The answer, I think, is simple: everywhere!

One might think that computational thinking was somehow only relevant to STEM education. But it’s not true. Computational thinking is relevant across the whole curriculum. To social studies. To language arts. To music. To art. Even to sports. People have tried to make math relevant to all these areas. But you just can’t do enough with traditional hand-calculation-based math to make this realistic. But with computation and computational thinking it’s a completely different story. In every one of these areas there are very powerful—and often very clarifying—things that can be done with computation and computational thinking. And the great thing is that it’s all accessible to kids. The Wolfram Language takes care of all the internal technicalities—so one can really focus on the pure computational thinking and understanding, without the mechanics getting in the way.

One way to get to this is to redefine what one imagines “math” education to be—and that’s one of the things that’s being achieved in the Computer-Based Math initiative. But another approach is just to think about inserting computational thinking directly into every other area of the curriculum. I’ve noticed that in practice—particularly at the grade school level—the teachers who get enthusiastic about teaching computational thinking may or may not have obvious technical backgrounds. It’s like with the current generation of kids: you don’t have to be a techie to be into knowledge-based programming and computational thinking.

In the past, with low-level computer languages like C++ and Java, you really did have to be a committed, engineering-oriented person to be teaching with them. But it’s a completely different story with the Wolfram Language. Yes, there’s plenty to learn if one wants to know the language well. But one is learning about general computational thinking, not the engineering details of computer systems.

So how should computational thinking be fitted into the school curriculum? Something I hear quite a lot is that teachers already have a hard time fitting everything they’re supposed to teach into the available time. So how can anything else be added? Well, here’s the surprising thing that I’m only just beginning to understand: adding computational thinking actually makes it easier to teach lots of things, so even with the time spent on computational thinking, the total time can actually go down, even though there’s more being learned.

How can this be? The main point is that computational thinking provides a framework that makes things more transparent and easier to understand. When you formulate something computationally, everyone can try it out and explicitly see how it works. There’s nothing hidden that the student somehow has to infer from some comment the teacher made.

Here’s a story from years ago, when the Wolfram Language—in the form of Mathematica—was first being used to teach calculus. It’s pretty common for calculus students to have trouble understanding the concept of a function. But professors told me that they started noticing that when they were learning calculus through Mathematica, somehow none of the students ended up being confused about functions. And the reason was that they had learned about functions through computational thinking—through seeing them explicitly and computationally in the Wolfram Language, rather than hearing about them more indirectly and abstractly as in standard calculus teaching.

Particularly in past decades there was a great tendency for textbooks in almost every subject to “stand on ceremony” in explaining things—so the best explanations often had to be sought out in semi-illicit outline publications. But somehow, with things like MathWorld and Wikipedia, a more direct style of presenting information has become commonplace—and has come to be taken for granted by today’s students. I see the application of computational thinking across every field as being a kind of dramatic continuation of this trend: taking things which could only be talked around, and turning them into things that can be shown through computation directly and explicitly.

You talk about a Shakespeare play and try to get a general sense of the flow in it. Well, with computational thinking you can imagine creating a social network for the play (who “knows” who through being in the same scene, etc.). And pretty soon you have a nice summary, that’s a place to launch from in talking about the nuances of the play and its themes.

Imagine you’re talking about different language families. Well, you can just take some words and use `WordTranslation` to translate them into hundreds of languages. Then you could make a dendrogram to show how the forms of those words cluster in different languages—and you can discover the Indo-European language family.

You could be talking about styles of art—and pull up lots of images of famous paintings that are built into the Wolfram Language. Then you could start comparing the use of color in different paintings—maybe making a plot of how it changed over time, seeing if one can tell when different styles came in.

You could be talking about the economics of different countries—and you could immediately create your own infographics, working with students to see how best to present what’s important. You could be talking about history, and you could use the historical map data in the Wolfram Language to compare the conquests of Alexander the Great and Julius Caesar. Or you could ask about US presidents, make a timeline showing their administrations, and compare them using economic or cultural indicators.

Let’s say you’re teaching English grammar. Well, it certainly helps that the Wolfram Language can automatically diagram sentences. But you can also let students try their own rules for generating sentences—so they can see what generates something they think is grammatically correct, and what doesn’t. How about spelling? Can computational thinking help with that? I’m not sure. It’s certainly easy to take all the common words in English, and start trying out different rules one might think of. And it’s fun to discover exceptions (does “u” always follow “q”: it’s trivial in the Wolfram Language to find out).

It’s an interesting exercise to take standard pieces of the curriculum for different subjects and ask “can this be helped by applying computational thinking?”. Sometimes the first thing one thinks of may be a gimmick. But what I’ve found is that if one really asks what the point of that piece of the curriculum is, there will end up being a way that computational thinking can help, right from the foundations.

Over time, there will be a larger and larger inventory of great examples of all this. In the past, with math (the non-computer-based version), it’s been rather disappointing: there just aren’t that many examples that work. Yes, there are things like exponential growth that show up in a bunch of places, but by the time one realizes that the examples in the calculus books are in many cases the same as they were in the 1700s, it’s not looking so good. And with standard programming the picture isn’t much better: there are only so many places that the Fibonacci sequence shows up. But with knowledge-based programming in the Wolfram Language the picture is completely different. Because the language immediately connects to the data and computations that are relevant across essentially every domain.

OK, so if one’s going to be teaching computational thinking, how should it be organized? Should one for example have a Computational Thinking class? At the college level, I think Computational Thinking 101 is a good idea. In fact, it might well be the single most important course many students take. At the high-school level, I think it’s less obvious what should be done, and though I’m certainly no expert, my tendency is to think that computational thinking is better inserted into lots of different modules within different classes.

One obvious question is: what’s the startup cost to having students engage with computational thinking? My feeling is that with the technology we’ve got now, it’s extremely low. With Wolfram|Alpha, it’s zero. With Explorations in the Wolfram Language, it’s very close to zero. With free-form code in the Wolfram Language, there’s a small amount to know, and perhaps it’s better for this to be taught in one go, a little like a miniature version of what would be a “service math course” at the college level.

It’s worth mentioning that computational thinking is rather unique in its breadth of applicability across the curriculum. Everyone would like what’s learned in one class to be applied in others, but it doesn’t happen all that often. I’ve already mentioned the difficulties with traditional math. The situation is a bit better with writing, where one would at least hope that students use what they’ve learned in producing essays in other subjects. But most fields are taught in intellectual silos, with nothing learned in one even being referenced in others. With computational thinking, though, there’s vastly more cross-connection. The social network for the Shakespeare play involves the same computational ideas as a network for international trade, or a diagram of the relations between words in different languages. The visualization technique one might use for economic performance is the same as for sports results. And so on.

Every day lots of top scientists and technologists use the Wolfram Language to do lots of sophisticated things. But of course the big thing in recent times is that the Wolfram Language has got to the point where it can also readily be used by kids. And I’m not talking about some watered-down toy version. I’m talking about the very same Wolfram Language that the fanciest professionals use. (Yes, just like the English language where there are obscure words kids won’t typically use, so there are obscure functions in the Wolfram Language that kids won’t typically use.)

So what’s made this possible? It’s basically the layers and layers of automation that we’ve built into the Wolfram Language over the past thirty years. The goal is to automate as much as possible—so that the humans who use the Wolfram Language, whether they’re sophisticated professionals or middle-school kids, just have to provide the concepts and the computational thinking, and then the language takes over and automates the details of actually getting things done.

In the past, there always had to be separate systems for kids and professionals to use. But thanks to all this automation, they’ve converged. It’s happened before, in other fields. For example, in video editing. Where there used to be simple systems for amateurs and complicated systems for professionals—but now everyone, from kids to makers of the world’s most expensive movies, uses the very same systems.

It’s probably more difficult to achieve this in computational thinking and programming—but that’s what the past thirty years of work on the Wolfram Language has, I think, now definitively achieved.

In many standard curriculum subjects, kids in school only get to do pale shadows of what professionals do. But when it comes to computational thinking, they’ve now got the same tools—and it’s now realistic for them to do the same professional-grade kinds of things.

Most of what kids get to do in school has, in a sense, little visible leverage. Kids spend a lot of effort to produce one answer in math or chemistry or whatever. If kids write essays, they have to explicitly write out each word. But with computational thinking and the Wolfram Language, it’s a different story. Once a kid understands how to formulate something computationally, and how to write it to the Wolfram Language, then the language takes over to build what’s potentially a big and sophisticated result.

A student might have some idea about the growth and decay of historical empires, and might figure out how to formulate the idea in terms of time series of geographic areas of historical countries. And as soon as they write this idea in the Wolfram Language, the language takes over, and pretty soon the student has elaborate tables and infographics and whatever—from which they can then draw all sorts of conclusions.

But what do kids learn from writing things in the Wolfram Language? Well, first and foremost, they learn computational thinking. Computational thinking is really a new way of thinking. But it’s got certain similarities in its character to other things kids do. Like math, for example, it forces a certain precision and clarity of thinking. But like writing, it’s fundamentally about communicating ideas. And also like writing, it’s a fundamentally creative activity. Good code in the Wolfram Language, like good writing, is clear and elegant—and can readily be read and understood. But unlike ordinary writing, humans aren’t the only target audience: it’s also for computers, to tell them what to automatically do.

When students do problems in math or chemistry or other subjects, the only way they can typically tell if they’ve got the right answer is for their teacher to tell them, or for them to “look it up in the back of the book”. But it’s a whole different story with Wolfram Language code. Because kids themselves can tell if they’re on the right track. The code was supposed to make a honeycomb-like array. Well, did it?

The whole process of creating code is a little different from anything else kids normally do. There’s formulating the code, and then there’s debugging it. Debugging is a very interesting intellectual exercise. The mechanics of it are vastly easier in the Wolfram Language than they’ve ever been before—because the Wolfram Language is symbolic, so any fragment of code can always be run on its own, and separately studied.

But debugging is ultimately about understanding, and problem solving. It’s a very pure form of what comes up in a great many things in life. But what’s really nice about it—particularly in the Wolfram Language—is the instant feedback. You changed something; did it help? Or do you have to dive in and figure out something else?

Part of debugging is just about getting a piece of code to produce something. But the other part is understanding if it produces the right thing. Is that really a sensible social network for the Shakespeare play? Why are there lots of characters who don’t seem to connect to anyone else? Let’s understand how we defined “connectivity”. Does it really make sense? Is there a better definition?

This is the kind of thing computational thinking is about. It’s not so much about programming: it’s about what should be programmed; it’s about the overall problem of formulating things so they can be put into computational form. And now—with today’s Wolfram Language—we have an environment for taking what’s been formulated, and actually turning it into something real.

When I show computational thinking and the Wolfram Language to kids, I’ll usually try to figure out what the kids are interested in. Are they into art? Or science? Or history? Or videogames? Or what? Then—and it’s always fun for me to do this—I’ll come up with an example that relates to their interest. And we’ll run it. And it’ll produce some result, maybe some image or visualization. And then the kids will look at it, and think about it based on what they already know. And then, almost always, they’ll ask question. “How does this extend to that?” “What about doing this instead?” And this is where things get really good. Because when the kids are asking their own questions, you can tell they’re getting seriously engaged; they’re really thinking about what’s going on.

Most subjects that are taught in school are somewhat tightly constrained. Questions can be asked, but they’re more like typical “tech support”: help me to understand this existing feature. They’re not like “let’s talk about something new”. A few times I’ve done “ask me anything” sessions about science with kids. It’s an interesting experience. There’ll be a question where, yes, it can easily be answered from college-level physics. Then another question that might require graduate-level knowledge. And then—whoosh—there’ll be an obvious-sounding question which I know simply hasn’t been answered, even by the latest leading-edge research. Or maybe one where, yes, I know the answer, but only because just last month I happened to talk to the world expert who recently figured out. Before I tried these kinds of “ask me anything” sessions I didn’t really appreciate how hard it can be when kids ask “free-range” questions. But now I understand why unless one has teachers with broad research-level knowledge there’s little choice but to make traditional school subjects much more tightly constrained.

But there’s something new that’s possible using the Wolfram Language as a tool. Because with the Wolfram Language a teacher doesn’t have to know the whole answer to a question: they just have to be able to formulate the question in a computational way, so the Wolfram Language can compute the answer. Yes, there’s skill required on the part of the teacher to be able to write in the Wolfram Language. But it’s really fun—and educational—for student and teacher together to be getting the answers to questions.

I’ve often done what I call “live experiments”. I take some topic—either suggested by the audience, or that I thought of just before I start—and then I explore that topic live with the Wolfram Language, and see what I can discover about it. It’s gotten a lot easier over the years, as the capabilities and level of automation in the Wolfram Language have increased. I usually open our Wolfram Summer School by doing a live experiment. And I’ll make the claim that over the course of an hour or so, we’ll build up a notebook where we’ve discovered something new and interesting enough that it could be the seed for an academic paper or the like. It can be quite nerve-wracking for me. But in almost all cases it works out extremely well. And I think it’s an educational and empowering thing to watch. Because most people don’t realize that it’s even faintly possible to go from zero to a publishable discovery in an hour. But that’s what the modern Wolfram Language makes possible. And while it obviously helps that I personally have a lifetime of experience in computational thinking and discovering things, it’s surprisingly easy for anyone with decent knowledge of computational thinking and the Wolfram Language to do a very compelling live experiment.

When I was a kid I was never a fan of exercises in textbooks. I always took the point of view that it wasn’t very exciting to do the same thing lots of people had already done. And so I always tried to think of different questions that I could explore, and where I could potentially see things that nobody had seen before. Well, in modern times with the Wolfram Language, doing things that have never been done before has become vastly easier. Not every kid has the same motivation structure as I had. But for many people there’s extra satisfaction in being able to make something that’s really their own creation—and not just a re-run of what’s been made before. And at a practical level, it’s great that with the Wolfram Cloud it’s easy to share what’s made—and for example to create one’s own active website or app, that one can show to one’s class, one’s friends, or the world.

So where are there discoveries that can be made by kids? Everywhere! Even in a technical, well-developed area like math, there’s endless experimental mathematics to be done, where discoveries can be made. In the sciences, there’s a small additional hurdle, because one’s typically got to deal with actual data. Of course, there’s lots of data built right into the Wolfram Language. And it’s easier than ever to get more data. Perhaps one just uses a camera or a microphone, or, more elaborately, one gets sensors connected through Raspberry Pi or Arduino, or whatever.

So what about the humanities? Well, here again one needs data. But again there’s lots of it that’s built right into the Wolfram Language (images of famous artworks, texts of famous books, information on historical countries, and so on and so on). And in today’s world, it’s become extremely easy to find more data on the web—and to import it into the Wolfram Language. Sometimes there’s some data curation involved (which itself is interesting and educational), but it’s amazing in modern times how easy it’s become to find, for example, even obscure documents from centuries ago on the web. (And, yes, that’s one of the things that’s really helped my own hobby of studying history.)

Computational thinking is an area that really lends itself to project-based learning. Every year for our summer programs, I come up with hundreds of ideas for projects that are accessible to kids. And with a little help, the kids themselves come up with even more. For our summer programs, we have kids work on projects on their own, but it’s easy for kids to collaborate on these projects too. We typically have a definite end point for projects: create a Demonstration, or a web app, and write a description, perhaps to post on the Wolfram Community. (Particularly with Demonstrations for our Wolfram Demonstrations Project, the actual process of review and publication tends to be educational too.)

Of course, even when a particular project has been “done before”, it’ll usually be different if it’s done again. At the very simplest level, writing code is a creative process and different people will write it differently. And if there are visualizations or user interfaces as part of the project, each person can creatively invent new ways to do these.

But, OK, all this creative stuff is well and good. But in practice a lot of education has to be done in more of a production-line mode, with large numbers of students in some sense always doing the same thing. And even with this constraint, there’s something good about computational thinking, and coding in the Wolfram Language. One of the convenient features of math is that when people do exercises, they get definite answers, which are easy to check (well, at least up to issues of equivalence of algebraic expressions, which basically needs our whole math technology stack to get right). When people write essays, there’s basically no choice but to have actual humans read them (yes, one can determine some things with natural language processing and machine learning, but the real point of essays is to communicate with humans, and ultimately to tell if that’s working you really need humans in the loop).

Well, when one writes a piece of code, it’s a creative act, like writing an essay. But now one’s making something that’s set up to be communicated to a computer. And so it makes perfect sense to have a computer read it and assess it. It’s still not a trivial task, though. Because, for example, one wants to check that the student didn’t in effect just put the final answer right into the code they wrote—and that the code really did express, preferably with clarity, a computational idea. It gets pretty high tech, but by using the symbolic character of the Wolfram Language, plus some automated theorem proving and machine learning, it seems to be possible to do very well on this in practice. And that’s for example what’s allowed us to put automatically graded versions of the exercises from my *Elementary Introduction* book on the web.

At one level one can assess what’s going on by looking at the final code students write. Even though there may be an infinite number of different possible programs, one can assess which ones are correct, and even which ones satisfy particular efficiency or elegance criteria. But there’s much further one can go. Because unlike an area like math where students tend to do their thinking on scratch paper, in coding each step in the process of writing a program tends to be done on the computer, with every keystroke able to be captured. I myself have long been an enthusiast of personal analytics, and occasionally I’ve done at least a little analysis on the process by which I write and debug programs. But there’s a great opportunity in education for this, first in producing elaborate educational analytics (for which the Wolfram Language and Wolfram Cloud are a perfect fit), and then for creating deep ways of adapting to the actual behavior and learning process of each individual student.

Ultimately what we presumably want is an accurate computational model of every student. And with the current machine learning technology that we have in the Wolfram Language I think we’re beginning to have what’s needed to build it. Given this model what we’d then presumably do is in effect to run lots of simulations of what would happen if the student were told this or that, trying to determine what the optimal thing to explain, or optimal exercise to give, would be at any given time.

In helping with an area like basic math, this kind of personalization is fairly easy to do with simple heuristics. When it comes to helping with coding and computational thinking, the problem is considerably more complicated. But it’s a place where, with good computational thinking, and sophisticated computation inside the system, I think it’ll be possible to do something really good.

I might mention that there’s always a question of what one should assess to find out if someone has really understood a particular thing. With a good computational model of every student, one could have a very sophisticated answer to this. But somewhere one’s still going to have to invent types of exercises or tests to give (well, assuming that one doesn’t just go for the arguably much better scheme of just assessing whole projects).

One fundamental type of exercise—of which my *Elementary Introduction* is full—is of the form “write a piece of code to do X”. But there are others too. One is “simplify this piece of code”, or “find an input where this function will fail”. Of course, there are exercises like “what will this piece of code do?”. But in some sense exercises like that seem silly: after all, one can just run the code to find out.

Now, I have to say I think it’s useful for people to do a bit of “acting like a computer”. It’s helpful in understanding what computation is, and how the process of computation works. But it’s not something to do a lot of. The real focus, I think, should be on educating people about what they themselves actually need to do. There is technology and automation in the world, and there’ll be more of it over time. There’s no point in teaching people to do a computer’s job; one should teach them to do what only they can do, working with the computer as a tool and partner, in the best possible way.

(I’ve heard arguments about teaching kids how to do arithmetic without calculators that go along the lines of “what if you were on a desert island without a calculator?”. And I can hear it now having someone make the same argument about teaching kids how to work out what programs do by hand. But, er, if you’re on a desert island without a computer, why exactly are you writing code? [Of course, when code literacy becomes more universal, it might be a different story, because humans on a desert island might be writing code to read themselves...])

OK, so what are the important things to teach? Computational thinking is really about thinking. It’s about formulating ideas in a structured way, that, conveniently enough, can in the modern world be communicated to a computer, which can then do interesting things.

There are facts and ideas to know. Some of them are about the abstract process of computation. But some of them are about how things in the world get made systematic. How is color represented? How are points on the Earth specified? How does one represent the glyphs of different human languages? And so on. We made a poster a few years ago of the history of the systematic representation of data. Just the content of that poster would make an interesting course.

But, OK, so if one knows something about how to represent things, and about the processes of computation, what should one learn how to figure out? The fundamental goal is to get to the point where one’s able to take something one wants to know or do, and be able to cast it into computational form.

Often that’s about “inventing an algorithm”, or “inventing a heuristic”. What’s a good way to compare the growth of the Roman Empire with the spread of the Mongols? What’s the right thing to compute? The right thing to display? How can one tell if there are really more craters near the poles of the Moon? What’s a good way to identify a crater from an image anyway?

It’s the analog of things like this that are at the core of making progress in basically every “computational X” field. And it’s people who learn to be good at these kinds of things who will be the most successful in these fields. Around our company, many of these “invent an algorithm; invent a heuristic” kinds of problems are solved every day—and that’s a large part of what’s gone into building up the Wolfram Language, and Wolfram|Alpha, all these years.

Yes, once the algorithm or the heuristic is invented, it’s up to the computer to execute it. But inventing it is typically first and foremost about understanding what’s wanted in a clear and structured enough way that it can be made computational. With effort, one can invent disembodied exercises that are as abstract as possible. But what’s much more common—and useful—is to have questions that connect to the outside world.

Even a question like “Given a bunch of x,y pairs, what’s a good algorithm for deciding if one should plot them as separate points, or with a line joining them?” is really a question that depends on thinking about the way the world is. And from an educational point of view, what’s really nice about questions of computational thinking is that they almost inevitably involve input from other domains of knowledge. They force a certain kind of broad, general thinking, and a certain application of common sense, that is incredibly valuable for so much of what people need to do.

Teaching “coding” is something that’s been talked about quite a lot in the past few years. Of course, “coding” isn’t the same as computational thinking. It’s a little bit like the relation of handwriting or typing to essay writing. You (normally) need handwriting or typing to be able to actually produce an essay, but it’s not the intellectual core of the activity. But, OK, so how should one teach “coding”?

Well, in the Wolfram Language the idea is that one should be able to take ideas as humans formulate them with computational thinking, and convert them as directly as possible into code in the language. In some small cases (and they’ll gradually get a bit bigger) it’s possible to just specify what one wants in English. But normally one’s writing directly in the Wolfram Language. Which means at some level one’s doing coding, otherwise known as programming.

It’s a much higher-level form of programming, though, than most programmers are used to. And that’s precisely why it’s now accessible to a much broader range of people, and why it makes sense to inject it on a large scale into education.

So how does it relate to “traditional” programming education? There are really two types of programming education that have been tried: what one might call the “high-school version” and the “elementary-school version”. These days the high-school version is mostly about C++ and Java. The elementary-school version is mostly about derivatives of Logo like Scratch. I’ve been shocked, though, that even among technically-oriented kids educated at sophisticated schools in the US, it’s still surprisingly rare to find ones who’ve learned any serious amount of programming in school.

But when they do learn about “programming”, say in high school, what do they actually learn? There’s usually a lot of syntactic detail, but the top concepts tend to be conditionals, loops and variables. As someone who’s spent most of his life thinking about computation, this is really disappointing. Yes, these concepts are certainly part of low-level computer languages. But they’re not central to what we now broadly understand as computation—and in computational thinking in general they’re at best side shows.

What is important? In practice, probably the single most important concept is just that everything (text, images, networks, user interfaces, whatever) can be represented in computational form. Ideas like functions and lists are also important. And if one’s being intellectual, the notion of universal computation (which is what makes software possible) is important too.

But the problem is that what’s being taught now is not only not general computational thinking, it’s not even general programming. Conditionals, loops and variables were central to the very first practical computer languages that emerged in the 1960s. Today’s computer languages—like C++ and Java—have much better ways to manage large volumes of code. But their underlying computational structure is remarkably similar to the 1960s languages. And in fact kids—who are typically writing very small amounts of code—end up really just dealing with computing as it was in the 1960s (though perhaps with a mechanisms aimed at large codebases making it more complicated).

The Wolfram Language is really a language of modern times. It wouldn’t have been practical at all in the 1960s: computers just weren’t big and fast enough, and there wasn’t anything like the cloud in which to maintain a large knowledgebase. (As it happens, there were languages like LISP and APL even in the early 1960s that had higher-level ideas reminiscent of the Wolfram Language, but it took decades before those ideas could really be used in practice.)

So what of loops and conditionals and variables? Well, they all exist in the Wolfram Language. They just aren’t front and center concepts. In my *Elementary Introduction* book, for example, it’s Chapter 38 before I talk about assigning values to variables, and it happens after I’ve discussed deploying sophisticated knowledge-based apps to the web.

To give an example, let’s say one wants to make a table of the first 10 squares. In the Wolfram Language one could do this very simply, with:

But if one’s working in C for example, it’d be roughly:

A non-programmer might ask: “What the heck is all that stuff?” Well, instead of just saying directly what we want, what it’s doing is telling the computer at a low level exactly what it should do. We’re telling it to allocate memory to store the integer value of n. We’re saying to start with n=1, and keep incrementing n until it gets to 10. And then we’re saying in each case to the computer that it should print the square. There’s a lot of detail. (To be fair, in a more modern language like Python or JavaScript, some of this goes away, but in this example we’re still left dealing with an explicit loop and its variable.)

Now, the crucial point is that the loops and conditionals and variables aren’t the real point of the computation; they’re just details of the particular implementation in a low-level language. I’ve heard people say it’s simpler for kids to understand what’s going on when there are explicit loops and conditionals and variables. From my observations this simply isn’t true. Maybe it’s something that’s changed over the years, as people have gotten more exposed to computation and computational ideas in their everyday lives. But as of now, talking about the details of loops and conditionals and variables just seems to make it harder for kids to understand the concepts of computation.

Is it useful to learn about loops and conditionals and variables at some point? Definitely. They’re part of the whole story of computation and computational thinking. They’re just not the most important part, or the first part to learn. Oh, and by the way, if one’s going to start talking about doing computation with images or networks or whatever, concepts like loops really aren’t what one wants at all.

One important feature of the Wolfram Language is that in its effort to cover general computational thinking it integrates a large number of different computational paradigms. There’s functional programming. And procedural programming. And list-based programming. And symbolic programming. And machine learning and example-based programming. And so on. So when people learn the Wolfram Language, they’re immediately getting exposed to a broad spectrum of computational ideas, conveniently all consistently packaged together.

But what happens when someone who’s learned programming in the Wolfram Language wants to do low-level programming in C++ or Java? I’ve seen this a few times, and it’s been quite charming. They seem to have no difficulty at all grasping how to do good programming in these lower-level languages, but they keep on exclaiming about all the quaint things they have to do, and all the things that don’t work. “Oh my gosh, I actually have to allocate memory myself”. “Wow, there’s a limit on the size of an integer”. And so on.

The transition from the Wolfram Language to lower-level languages seems to be easy. The other way around it’s sometimes a little more challenging. And I must say that I often find it easier to teach computational thinking to kids who know nothing about programming: they pick up the concepts very quickly, and they don’t have to unlearn the idea that everything must turn into loops and conditionals and so on.

When I started considering teaching computational thinking and the Wolfram Language to kids, I imagined it would mostly be high-school kids. But particularly when my *Introduction* book came out, I was surprised to learn that all sorts of 11- and 12-year-olds were going through it. And my current conclusion is that what we’ve got with Wolfram Programming Lab and so on is suitable for kids down to about age 11 or 12.

What about younger kids? Well, in today’s world, all of them are using computers or smartphones, and are getting exposed to all sorts of computational activities. Maybe they’re making and editing videos. Maybe they’re constructing assets for a game. And all of these kinds of activities are good precursors to computational thinking.

Back in the 1960s, a bold experiment was started in the form of Logo. I’m told the original idea was to construct 50 “microworlds” where kids could experiment with computers. The very first one involved a “turtle” moving around on the screen—and over the course of a half-century this evolved into things like Scratch (which has an orange cat rather than a turtle). Unfortunately, however, the other 49 microworlds never got built. And while the turtle (or cat) is quite cute (and an impressive idea for the 1960s), it seems disappointingly narrow from the point of view of today’s understanding and experience of computation.

Still, lots of kids are exposed to things like Scratch in elementary school—even if sometimes only for a single “hour of code” in a year. In past years, there was clear value in having younger kids get the idea that they could make a computer do what they want at all. But the proliferation of other ways young kids use computation and computational ideas has made this much less significant. And yes, teaching loops and conditionals to elementary-school kids does seem a bit bizarre in modern times.

I strongly suspect that there are some much better ways to teach ideas of computational thinking at young ages—making use of all the technology and automation we have now. One feature of systems like Scratch is that their programs are assembled visually out of brick-like blocks, rather than having to be typed. Usually in practice the programs are quite linear in their structure. But the blocks do two things. First, they avoid the need for any explicit syntax (instead it’s just “does the block fit or not?”). And second, by having a stack of possible blocks on the side of the screen, they immediately document what’s possible.

And perhaps even more important: this whole setup forces one to have only a small collection of possible blocks, in effect a microworld. In the full Wolfram Language, there are over 5000 built-in functions, and just turning them all into blocks would be overwhelming and unhelpful. But the point is to select out of all these possible functions several (50?) microworlds, each involving only a small set of functions, but each chosen so that rich and interesting things can be done with them.

With our current technology, those microworlds can readily involve image computation, or natural language understanding, or machine learning—and, most importantly, can immediately relate to the real world. And I strongly suspect that by including some of these far-past-the-1960s things, we’ll be able to expose young kids much more directly and successfully to ideas about computational thinking that they’ll be able to take with them when they come to learn more later.

The process of educating kids—and the world—about computational thinking is only just beginning. I’m very excited that with the Wolfram Language and the systems around it, we’ve finally got tools that I think solve the core technological problems involved. But there are lots of structural, organizational and other issues that remain.

I’m trying to do my part, for example, by writing my *Elementary Introduction to the Wolfram Language*, releasing Wolfram Programming Lab, and creating the free Wolfram Open Cloud. But these are just first steps. There need to be lots of books and courses aimed at different populations. There need to be online and offline communities and activities defined. There need to be ways to deliver what’s now possible to students. And there need to be ways to teach teachers how to help.

We’ve got quite a few basic things in the works. A packaged course based on the *Elementary Introduction*. A Wolfram Challenges website with coding and computational thinking challenges. A more structured mentorship program for individual students doing projects. A franchisable version of our Wolfram Summer Camp. And more. Some of these are part of Wolfram Research; some come from the Wolfram Foundation. We’re considering a broader non-profit initiative to support delivering computational thinking education. And we’ve even thought about creating a whole school that’s centered around computational thinking—not least to show at least one model of how it can be done.

But beyond anything we’re doing, what I’m most excited about is that other people, and other organizations, are starting to take things forward, too. There are in-school programs, after-school programs, summer programs. There are the beginnings of very large-scale programs across countries.

Our own company and foundation are fairly small. To be able to educate the world about computational thinking, many other people and organizations need to be involved. Thanks to three decades of work we are at the point where have the technology. But now we have to actually get it delivered to kids all over the world in the right way.

Computational thinking is something that I think can be successfully taught to a very wide range of people, regardless of their economic resources. And because it’s so new, countries or regions with more sophisticated educational setups, or greater technological prowess, don’t really have any great advantage over anyone else in doing it.

Eventually, much of the world’s population will be able to do computational thinking and be able to communicate with computers using code—just as they can now read and write. But today we’re just at the beginning of making this happen. I’m pleased to be able to contribute technology and a little more to this. I look forward to seeing what I hope will be rapid progress on this in the next year or so, and in the years to come.

*Try the example computations from this blog post in the Wolfram Open Cloud »*

*To comment, please visit the copy of this post at the Stephen Wolfram Blog »*