 Hi, my name is Jane Krause. I'm a big fan of project-based learning, and I'm always looking for tools that help students engage in deep investigations. In my K-12 online presentation, I'll show folks how their kids can use the computational knowledge engine Wolfram Alpha and visualization tools such as ManyEyes and Tableau Public to construct and represent new knowledge. We'll do this in three acts. First, though, let me situate myself in the world for you. See the star over to the left? That's where I live, Eugene, Oregon. This is my town on a beautiful fall day, and this is me. I was in India for six weeks earlier in the year, and I chose this picture just because I was doing work in India around the topic of project-based learning, and I'd just like you to know from my view, it's taking off all over the world, and it was lovely to be able to go to India and introduce them to new methods, and I've done the same in Belgium, Egypt, all across the U.S., Mexico, a few different places, so I've enjoyed my travels and meeting teachers around the world on the topic of project-based learning. So we'll get started. As I said, we'll be looking at Wolfram Alpha and making meaning in three acts. Act one, thinking critically and constructing knowledge in PBL. We'll just talk about how this is different from typical instruction and how when we drive deep investigations, what kinds of activities we engage kids in. Act two, exploring knowledge construction. Wolfram Alpha will dig into the tool and see how you can use Wolfram Alpha to generate new information from which to make meaning. Act three, representing new knowledge with infographics. We'll look at several tools and one little quick trick for representing what you might derive from Wolfram Alpha into a graphical representation that you can share with others. Let's get started. So act one, thinking critically and constructing knowledge in PBL. In project-based learning, we talk a lot about helping kids do investigations where they make new meaning, but it's challenging to design learning experiences where they actually do this. So what do we mean by constructing knowledge or making meaning? Basically, it means coming to new understanding through direct experience. So examining a photograph for clues about an event would be knowledge construction. Reading about the same event in a textbook written by someone else would be knowledge attainment, but it's secondhand and often less powerful. In PBL, students ask original questions and their investigations yield information they have to interpret to construct meaning. Wolfram Alpha, which we'll get to in a moment, is great for this. Here's a list, a short list of approaches that get at critical thinking. If your projects have students doing these things, it's more likely they'll be operating in the higher reaches of Bloom's taxonomy, analyzing, synthesizing, evaluating, and constructing new ideas. When I consult with teachers, I keep this list handy and when we're looking at their project ideas, we try to amp them up by incorporating these kinds of activities within the project. So, for instance, let's think about a project where kids might be looking for identifying patterns or trends over time. Imagine a student is looking for trends and transportation in a state and this could, by the way, be a great dive into systems thinking. He might want to find out whether there's a relationship between gas prices and the number of cars on the roads in Minnesota. In Wolfram Alpha, the student might write this. In this query would say, Minnesota passenger cars in use versus price of a gallon of gasoline. To which Wolfram Alpha responds with data in a graph showing the relationship of those data over time. So that's just one way that Wolfram Alpha works with data and represents it to you. So we'll get started with Wolfram Alpha. Some compare it to Google, a search engine, and it shares some similarities, but it differs in two fundamental ways, both which are in that descriptor computational knowledge engine. So first of all, it computes. Wolfram Alpha is based on the computational software program called Mathematica that's used in scientific engineering and mathematical fields and other areas of technical computing. So we'll dig in. And here we are in Wolfram Alpha. So they have, as of this summer, have this really nifty homepage full of examples of the kinds of queries that you can do with Wolfram Alpha. So we'll just look at one. As I said, it's used a lot for mathematical and scientific purposes. For instance, you might be wanting to think about the area of a triangle with certain characteristics. You can use the scientific keyboard with the mathematical scientific notation in Wolfram Alpha to derive results like this. We'll just see what comes up. So the first thing that Wolfram Alpha does is it takes your query and says how it interprets it, which is very helpful to know, gives you a result and often some kind of visual representation and lots more information about radiuses, corresponding quantities, area, interior angle sums, different things like that. So we'll go back to the homepage. And I'd just like to say a little bit about Wolfram Alpha. It is free. It's a free version. But of course, like any of these kind of Web 2.0 apps, you get more when you go with a pro account. You can get started with the free account. A teacher, an adult account is $5.99 a month, and a student account is $2.99. And what I might do in a classroom is set up a little kiosk with one or two computers with the student version running on it because it's the kind of thing you drop in. You do your query and you take off again. So it wouldn't have to be on every computer. So like I said, one side of Wolfram Alpha has to do with this computational side, right? It's really useful for science and math. You can get a sense of that here from what I showed. I've known very little about this side of Wolfram Alpha. If you're familiar with its computational functions, I'd love to hear more from you. The other side of Wolfram Alpha has to do with that knowledge part. Wolfram Alpha draws from deep, rich databases of all kinds, and then you're able to mash up those data with different kinds of analyses. When I was teaching world studies back in the day, I'd have students search in the CIA World Factbook, and that's just one of the thousands of databases Wolfram Alpha draws from. The assistant Siri on the iPhone pulls from Wolfram Alpha's databases. The good thing is that everything you find out when you do your query where the data come from and they're all refereed sites. So it's good data. These data sets are where I found the most value for kids anytime they're investigating topics that involve data. So let's just give it a spin. Let's start by, let's say kids are doing some kind of project having to do with diet. And let's look at per capita consumption of sugar in the U.S. So as you see, the first thing it does is it does an interpretation of your input. And this is really helpful because you want kids to make good queries and if you get an interpretation that obviously didn't understand the query, you can improve on that, right? So it's really good for kids to be able to get this result so that they can continue improving the searches that they do. So this is what you get. You get a result here, 152.5 pounds a person. You get a graph of change over time, which is interesting. You find out other kinds of data, then it compares it to other foods. But we were just focused mainly on this query. Let's make it more interesting though. Let's say this is actually a project across three countries and kids were comparing their diet with UK and Australia. All you have to do is add in the other countries and it should come up with a comparison. Okay, it corrected my spelling, which is nice and interpreted Australia without the R and put the R in, but then it shows here's the comparison. And then in the same order that we put the information in, it gives us these results. Now, these little tools down at the bottom are really great. What they will do is tell you, you can enlarge the screen, you can download the data, you can customize and save this image, you can copy plain text, which is really useful, and there's some interactivity tools that you could look into and you can just copy and paste this information as well. But one of the other things I like and we'll come back to this later is it also gives you a ratio. So it takes the largest number, which is the United States, and it calls it one and then shows in rank order proportionate consumption. So Australia's next at about 70% the ratio and the UK at about 53% of the US's consumption. We're going to come back to this later when we talk about infographics and how we might use these data. So that was just a quick peek at one query that you can do in Wolfram Alpha. I'd recommend that you browse around here graphically if you'd like or go to the examples page to get a sense of all the different subject matter that is represented in Wolfram Alpha. Let's look at music for a minute, the musical instrument. So it says, imagine all these different queries that you could do related to musical instruments. You could compare instruments and look at the sonic features of it, you know, looking at the physics of sound in relation to these instruments. So here we're looking at a violin and a trumpet. It pulls up some images, talks about the pitch range, it's a frequency range. The range of music notes that each of these plays compares it to the ranges on a piano. There's lots of nice graphical representations and a lot of data. So again, get started with Wolfram Alpha, give it a look, understand its limitations, start figuring out what kinds of queries work best in it and you'll have a good time. So let's get on with Act 3. Representing new knowledge within for graphics. Remember our quick query we did about sugar consumption in the US, UK and Australia? I was able to make an infographic, or at least part of an infographic, using those data and the ratios that we found. So this is what I ended up with, but let me show you how I started. And we'll just make one really quickly. In PowerPoint, and I'll enter a show here, in PowerPoint I got this image out of the Creative Commons, of course, and I decided, well, I'll just copy and paste 100%. And then everything else was in relation to that. So let's take the second one, which was the second highest consumption was Australia. And we're just going to go in and change the scale, keep the aspect ratio the same, and it was at 70%. And the UK was at 52.5%. So I'll say 53. And I get my three items, and just so that we don't have any visual funniness going on, I'm going to align the bottoms and distribute the space between them. There. And I'm done. So it's a really easy way, when you know those ratios, to come up with a graphical representation that shows that. And so here's back where we were. And that's just a real nice little way to use PowerPoint really easily. So we'll go to Many Eyes. It's easier just to search it because it has a funny URL. It's an IBM URL. It's kind of a demonstration project of IBM. It's pretty neat, but we'll just search Many Eyes and you'll see the URL that comes up for it. It's kind of goofy. So there's the URL up there. But Many Eyes lets you do all kinds of different, you can explore visualizations, you can create your own visualizations by uploading data sets. All of this is free. And then I like to just come and see everything that gets made in Many Eyes gets posted also to Many Eyes. So you get to see all kinds of interesting visualizations that people have made. Here's one, for instance, some most popular dog breeds based on the American Kennel Association. So the discs are colored by dog breed. Labrador Retriever. When you roll over, you can see the data that are behind the representation. Mature Snauzer. Shih Tzu is getting a little bit alive. But these are the top 10. You can go in and you can manipulate the data if they are your own. But this is just one representation. Again, like I said, is the visualization types is really nice. So it's really helpful to say, oh, are you comparing a set of values? Then a bar chart or a block histogram might be best for you. Are you tracking change over time? Consider these tools. Seeing parts of a whole, are these sounding familiar? They're a little bit like our list of ways to get at critical thinking. Analyzing a text, seeing the world. All kinds of different tools you can use with many eyes. Lastly, Tableau Public. This is also free. You can buy Tableau itself and it is a much more amped up tool for making graphical representations. But one of the things they have here that's great is a gallery and you can see what other people have visualized that you started. And they have a nice little training element too. So we'll see what happens here. And there was one representation here. So here's the different kinds of things you can do. But there was one in business and real estate that I saw the other day that I thought, buy or rent. So this kid, I don't know why I think it's a cool kid, but somebody decided to ask the question, if you buy a home, how long would it take before buying that house is cheaper than renting the same house? And then they did this representation. And again, we should be able to see where the data came from and it's interactive. So we can mouse over and learn about the... Well, there's my town, Eugene Springfield. The break-even years would be 3.4. And then it ranks cities by that breakdown. But this is just one of the kinds of representation you can make in Tableau Public. So those are the kinds of tools that you can use. This last slide I have, it provides the links to all of the tools. And I hope you'll get started with Wolfram Alpha. I'd love to know what you do with it, particularly on that scientific and computational side. And good luck. And finally, here are the websites that we use today. Wolfram Alpha, Many Eyes, and Tableau Public. I'm Jane Krause. You can find me at J. Krause on Twitter and at my blog, ReinventingPBL.blogspot.com. I share this blog with Susie Boss. She and I are authors of Reinventing Project-Based Learning and ISTI publication that's been out for several years. We have a new book coming out in March 2013 called Thinking Through Projects, which is about driving deep investigations. So much of the work around project-based learning is about setting up projects. And we dig in. So setting up good projects that will get at deep thinking and then how you encourage that kind of thinking through the project cycle. So I hope you'll be looking for that too. Thank you for your time.