How do you approach utilizations? Since it's a short talk, I don't know how much time I will take to go into the course. But what we can do is, as I looked at one or two examples of how to generate graphs, mainly in Python, I'm going to use MaxRotlib to actually show that one of the different kinds of graphs that we can draw and how do you actually figure out what's going on in the data. So in my talk in the morning, for those of you who attended to me, actually can use a good for any of the big data systems to actually pull data out from and pull and aggregate data and get it to a level that we can visualize it. But there's so much information that is inherent in the data, inherent in the data. I think Anand is here. He gave an extra graph in the after-missile. I will go a little more deep into some of these things and especially in the emphasis on Python. So let's start. So we all know that data resolution, everybody, everybody we have, we are confronted with data. There's financial data, there's data from social networks. So the first step is struggling to make sense of the data. And though we have the tools, and though we have the tools, and for actually taking and analyzing the data, what we don't have is how do we actually find out more than just the magnitude of certain damage. We also probably want to look at the relationship in the data. And that is what visualization is going towards. It's a fairly interesting field, and it's kind of a picking up speed. And you can see some of the, I will go into some of the details like how we have to take out the data and start visualizing it. So this is the catalog data. This talks about, there are four different countries. India, Greece, Germany, and US. And this is carrying the month-on-month inflation. And this is representing a tableau format. So if you read this particular slide, maybe you can figure out if it has been in the month of April, inflation pattern is quite high in Greece. It's typically low, but there are certain months. For example, in March, and in September, it has increased a lot. So it still takes a lot of time for humans to actually look at the tableau format and make sense of the data. So what is probably better to do is probably to start working this data. This is an example of bad graph. But I've probably put one in so that we can find out what's the difference between a good visualization path. Probably I think the colors is not very good. But at least, even if you look from a trend perspective, you can figure out those two points kind of stand out in Greece. US has been fairly consistent. There has been a big jump here. Then if you look at, again, India, I mean, there was a peak and then the trend is upward again. So this was drawn using Excel, which is a fairly decent tool for actually doing visualization. A lot of people do not look Excel as a way of actually representing and analyzing and actually plotting data. But that is a fairly decent tool. So that should be used visualization. What are the different parameters along which visualization can make a good difference? I'm going to cover examples of each of these. But I mean, everybody who has seen DVDs and has grasped on this, people actually use the wrong charts all the time. They use pie charts when you don't need to have an explanation of composition. People use maybe line charts to sometimes show distribution. But what visualization should do is actually along this time because what are you trying to do with, what are you trying to show with that? Are you trying to show a comparison? Then maybe the best way to do it is maybe use a line chart and maybe not something like a pie chart. But we see that a lot of people do keep on mixing each of them. The other thing that you want to find out with data is what is the relationship between two sets of data. So if the visualization actually shows the relationship between two sets of data, then it becomes fairly easy to actually discern what's going on in the background rather than just looking at a complex text. Third is distribution, over a certain axis. Typically it's type or other certain other dimensions. You can actually use a histogram to show the distribution of data along a certain axis. And finally, composition. Example that composition could be extremely useful is we constantly as humans try to categorize stuff. For example, in the stock market, we have different kinds of stocks. We have tech stocks, we have FMCG stocks, we have maybe like the shipping sector, the industrial sector, so we have different. And under each of these, you have different companies. So if you want to find examples of the relationship between each of these companies and what sector they take, then maybe a composition based graph makes sense. So we'll go into an example in each of these. So this is like a line chart. So this was done using a very simple example. Very easy to draw. This is the code for it. So actually all the code is up on GitHub. You can actually, it's already uploaded, you can go to GitHub.com, you can go to GitHub.com, there's a message and then under the top, it's just closed and this top is completely updated. So this is the code that has generated it. I have given a range and then I have taken random functions and different kinds of random functions. And typically in Mac, you just have to import a pipeline under which there are a lot of libraries. You can go and check on the web that 2D libraries, the 3D libraries for which you can do interactive visualizations. So you take all of these parameters, maybe manage the data and then eventually just plot using each of these functions. There are the ways you can actually set the title, the labels, and there is a interactive mode as well. So currently, so it's a very simple example. You can actually plot this even in a, and the map one is actually just static blocks. But you can also embed it in Django and other Python related stuff to actually drop block. So this is an example of comparisons that you can use. Then if you want to see relationships, especially in data where it is not very apparent, maybe on two or three access, scatter blocks can not necessarily be 2D, but you can do this even. Now you have to use a random kind of generator to get a plot. But typically, if you, for example, find the, want to find a distance between, say, two people, what you can see is you can actually use, you can see that there will be clusters of data and some things that are easily correlated. So that cannot be easily seen when data is in a tableau format or a textual format. So when you want to actually explore the relationship between different things, then copy, or what simple way of doing charting that everybody, every library has is to use a scatter chart. One of the things is when you use PyLab, which is like an easy, interactive way to do, you can either use the same thing with chandrets or you can use the show function. You can see that I have commented on things that hold most of the show function because it brings up the, it brings up the data blocks. Same thing with distribution, I'll skip over this. And composition, which you find PyChart, you can use a standard for train composition. Typically, the example that I gave, like different types of industries and components, in each of these industries can easily show much capture. PyChart is useful when you want to see the relationship only on one axis. So it actually reduces the damage value. But it is very useful for saying the percentage composition that is there in each of these. So these are like the basics, composition, relationship, and all the other parameters that I talked about. This is how you compare some relationship, distribution, and composition. So these are the four different things that you look at when you're actually going to show any data. And then, like a very basic stuff that these are the charts that you can use. But what do you want to go beyond that, right?  Is this 2D charts, in some sense, or two-dimensional, not just in terms of axis, but even in terms of other aspects, how do you bring out other aspects which show the relationship? So, typically use the S and Y axis. You can use the Z axis for plotting surfaces. For example, the function over on a surface over a period of time. You can see what is the maxima, the minima, very easy, especially if you're doing machine learning or any of these things, you want to see how the surface looks and what to do on gradient descent, for example. You can see how the surface looks. You can also use color. Color is a different setup. Typically, you don't use the whole palette. You can still use the whole palette, but typically what you see in most places is that you use the gradient to actually show the higher intensity for mean something and the lower intensity for mean something else. Signs of the elements also can be an indicator. I'll give an example of this further down the, further down the, and also the compositions, because you can actually show, like for example, using a dream app, how the elements are correlated to each other and how much of that part we, for example, if you want to figure out how much of the tech industry is, how much of the tech industry that we want to, which we want to use in the US, how Google wants to use the US, in terms of market cap. You can use the composition and use a box and then on top of it, you can actually use the different elements. And finally, you can use animations. I don't have examples of animations in any of my slides, but yesterday I found out there's an extremely good library called Chaco, C-H-A-C-O, which can be used for visualization, track visualization. So on the web, I have come across Infobis and Protobis, now I think Infobis is for D3. It's an excellent library for doing data visualization on the web. We actually used a string graph from D3 to do something during the hacking of the app. So the library is very good, especially if you want to look at hierarchical data sets, or you want to look at graph structures, that is very good. If you want to look at relationships and networks, then for visualization, there's JT, but I won't go into that right now because I'm talking about Python, what's available. Stream graphs. So stream graphs is a concept that is based on the work that was done by Leigh Byron. So one of the axis, the base axis is typically type. And what this graph could do, is what is the intensity that can be used here. The other thing that is typically used here is the composition, right? So if you look at it closely, it's actually a stack graph. And you can see the stack. So what can something like this represent? Now you look at a listening history, or someone. Last but not the best, I don't know how many of you use last but not the best. Last but not the best has a streaming graph. You can look at your own listening history. And depending on either genre, like maybe rock or pop or world music, you can see what kind of genres you're missing, but how did they, what are you missing, what did you miss in over a period of time? So that is something that you can use. Other thing that can be used, that can be used very successfully by the New York Times is, you can see the movie collections and what is the genre of the movie. You can actually plot it over a period of time. And each of these can actually represent the movie. So streaming graph is extremely useful when you're looking at a period of data and then you have different components of it. Color line, different types of color, and composition can be shown by the standard kind of characteristics there. This is quite an intent diagram. So in machine learning, typically what you do is you have two matrices. And typically they're positive and negative elements. And they kind of vary in time. So this is like a 20 by 20 matrix. And the size, the size of the individual elements, tells us what is the magnitude of that element. And black indicates negative and white indicates positive. So you can actually visualize the whole matrix and see if that clusters or how does the space kind of pack out. So it's very easy to see from a graph like this. But what we can actually show you the, and these are the elements of a tree have a certain weight or could have multiple weights. And you can show the composition by what parent it belongs to. And also you can show it by the magnitude you can show by the color weight. So it's actually a tree that's represented, unfortunately, for each of the graph it is not showing up. Speed match is another thing that, which is very similar to some of the other things that you have seen before. It's like we can actually have multiple axes. And on two axes you can show different kinds of data. I mean you can use composition as well as the gradient in this. Example where we have been using this, I want to take more pictures. So for example, on one side we have set up publishers. On the other side we have the category that they belong to. And we can actually, the color can actually show how much revenue we made from it, how many clicks we got, how many add books we got. So we can have a three key to match. Side by side we can see what was the ratio which gave. And what are the areas that are kind of hot? I mean, are giving us more revenue or less revenue or more number of clicks based on certain, for other metrics, based on certain parameters that we have. So this also can be done using macro click. The old is also fairly easy for doing something like this. So it's kind of built out of the box. This is a polar plot, typically used in scientific computing but it's not necessarily, you can actually use the circular to actually show the revenue over that as well as. So you can use space as one of the dimensions and you can also use this, the radial distance as one of the intensities. Again, you can use gradient arrows. We are showing like, we have four different parameters on the same, it's easily possible to do multi dimension analysis doing something like a flow level. Finally, I'm coming to contour maps. You again want to plot a function. This one looks like a sombrero. And this is one we are showing it. It can also be shown in a pudding manner. There are ways in which you can interact with it also so that you can see from the top, how it looks from the side, how it looks. You have maps like this. So you can see on one side, you're seeing the characteristics of this, which is the distribution, on the other side, you're seeing the histogram. Seeing that how are they concentrated on two different axes. So you can use projections or you can use something like a surface also to look at the city side of things. We can actually look at three or four dimensions. Again, you can see the gradient is used to show the intensity, which anyway is shown, but it kind of accentuates and emphasizes based on the color. That's it. Unfortunately, I can't show much more, but you can, most of the code is kind of self-explanatory and not only is extremely good for being static plots. It also has very good integration. You can go to Macroflip. There's a whole gallery of visualizations that you can do using Macroflip.


Welcome to Going 1-1. My presentation is part of K-12 online learning conference's visioning new curriculum strand. I'll be talking about the what's, why's and how's of going 1-1 in educational settings and hopefully giving you some reasons to consider doing so. I want to start by thanking Wesley Pryor and the other people at the conference for having me today and allowing me this time to present and I want to thank you for watching. If you happen to be in the greater New York City area on November 16th, please consider attending my school's first-ever education technology conference. We're calling it X-Tech. Registration is free and we'll have presentations from talented educators and major innovators like Apple Switch Technologies and Baricoded Networks. To share a little bit about my professional background, I'm currently the Faculty Advisor of Instructional Technology at Zivarian High School in Brooklyn, New York, one of the first Apple 1-1 schools in the United States. I'm excited to say my book Going 1-1, iPads and Mobile Computing in the Classroom should be out this November so if you find any what I say useful today, please consider checking that out as well. I also teach at the college level and I utilize a 1-1 model there and I don't say that because I think it's important you know it, but I think it's important you know I use laptops in the 1-1 model there. So all the information I'm giving here is going to be platform neutral. It should work across just about any device, any laptop, any netbook that you're working with. I've been a classroom teacher for well over a decade and now I've moved into more of a hybrid position at Zivarian as the working in instructional technology and that new work informs a lot of what I'll be talking about here today. I'd love to hear your thoughts on anything I say, anything that interests you or anything you want to dispute. The best way to reach me is via Twitter at 42022. You can also email me but Twitter is a really great way to get in touch with me. And again thanks for having me and thanks for watching. So let's get started. My presentation is divided into three distinct pieces. The first aims to define the nature of 1-1 computing. 1-1 computing really has become something that everyone talks about but not everyone can elucidate a clear idea behind or an idea of. So I'd like to be able to maybe give you a little bit of the history and some of the inspiration that led us down the road. Also going to talk to you a little bit about why you should consider going 1-1. This is the research that made me a true believer and I think it's going to at least give you food for thought about ubiquitous computing deployments. There have been quite a few studies on these early 1-1 programs done at this point so what do they say? What's the early verdict? Third section is all about how we did it with a focus on how our teachers change their approaches to lesson planning and professional development. A 1-1 deployment involves some major changes to how everything from seating to test giving is done. Using a host of apps like Edmodo, Education, Dropbox, Iannotake, Khan Academy and Ustream, we were allowed as progressive educators to create living breathing classrooms that don't get locked at the end of the day. So I think talking about reshaping the classrooms a big part of this presentation as well. So first question is what does it mean to go 1-1? A simple definition of going 1-1 would be that one internet-enabled computing device is provided to every student and then other definitions might include the idea that a technologically progressive student-centered approach to education is a component of 1-1 education. The goal behind it is to move students from consumers to curators and ultimately creators. I also want to take this all the way back to 1975 actually is our starting point because that's when Apple donated some of the first Apple Ones to schools and other device deployments slowly followed. There were computers in the classroom usually they were unwieldy and then around 1985 Apple began developing and deploying its classroom of tomorrow in a handful of schools.  The classroom computing was slow to take off partially because of the physical barriers created by the cumbersome machines of the 80s and 90s and partially because the use of the tech was cost prohibitive for a lot of schools. That brings us to Greenock Scotland. You might not know much of this lovely city but it's where our story really begins. Cedar School of Excellence in Greenock became the world's first iPad 1-1 school just a few months after the tablet was released. So the idea traveled fast from Scotland to Brooklyn. We got the idea from Frazier Spears and the people at Cedars and we were quick to seize on that idea. So quick that we asked Frazier Spears to come speak at our Brooklyn school. He spoke to us in February 2011 and his evangelical approach to one-to-one innovation and education really inspired us. We came away proud to be trying something different and also empowered by the possibilities. There was little doubt after his visit that we would try to our very best to make it work and I think we all knew we would make it work. We also knew we were following in some pretty big footsteps. Schools like the Dalton School in Manhattan and many of the best colleges in the country had begun moving towards individual laptop models. The release of the iPad moved schools without serious network infrastructure closer to being able to launch one-to-one programs. Our school actually does have this serious infrastructure required for such a massive undertaking but even one-to-one cart programs were made possible by the affordable nature of the iPad. By following the footsteps of other innovative creative people we created a more active engaging learning environment. Professional development was an essential part of how we made it work but so were apps and websites like the aforementioned Edmodo, Evernote and Dropbox. We also started asking ourselves some tough questions like firstly if internet whiteboards did we really believe they had fulfilled their promise. If slideshows and power points were really the best way to deliver content we weren't sure and even if we should be delivering content in that sense at all anymore. Student-centered learning along a mantra we repeated became essential to everything we did. After all ideas age pretty quickly and the notion of the teacher as font of all knowledge was hopelessly outdated but maybe some of our other ideas were too. Our students' workplaces won't resemble the classrooms of the present or the offices of the past and it's up to us as progressive educators to look forward. Our students are digital natives entering a world in which technology is a vital part of virtually every field. Howard Gardner who I'm sure a lot of people know for his theory of multiple intelligences wrote that the new technologies make the materials vivid easy to access and fun to play with and they readily address the multiple ways of knowing that humans possess. If we want to shape our course content to fit student needs and the future workplace I think first we have to reshape content delivery to do the same. If you're wondering how the idea of going one-to-one took off so quickly the answer might lie in the results of several years of research. So let's get into what ultimately helped convince me that this type of overhaul would work for our school and that's the raw data. Some teachers and administrators appreciate the idea of going one-to-one but doubt its efficacy in practice. The truth is teachers students at academia are in agreement on this topic and maybe only this topic. There have been over a hundred studies conducted on one-to-one computing deployments and I can't find one that points to any major drawbacks to mobile devices in the classroom. The majority left researchers enthusiastic about the possibilities and impressed with the results. The total number that recommended again scubic what is computing models? Zero. But you might ask yourself how the students feel about all this change and the answers are almost universally positive. Students describe themselves as active and engaged learners in one-to-one programs. They use the devices for everything from simple homework to gaining a deeper understanding of more complex ideas. They learn then practice then master real-world skills like multimedia project development broadcasting and computer programming. There are countless studies we can point to. One that's up on the screen right now is the Michigan Freedom to Learn initiative which was studied. 90% of nearly 6,000 student participants wanted to continue working in a one-to-one environment during the next year. Two researchers, Hakan Fleischer and William Penule, authored meta studies covering two decades of one-to-one deployments. In Penule's case he analyzed six years worth of studies on ubiquitous computing in the classroom and concluded that in contrast to how students use technology and other initiatives and emphasize basic skills development or assessment in one-to-one initiatives students most often use productivity and design tools in ways that are integrated into other classroom activities and assignments. The most succinct description of the research might come from Fleischer when he writes that the researched articles reveal a uniform opinion that laptop programs have positive effects for pupils learning experiences. The teachers are also on board. They notice increased attentiveness, greater depth of knowledge, and growing self-motivation among students in one-to-one environments. The news and specific subject areas is also really good. English teachers, math teachers, research in almost every subject area indicates that students either are becoming more engaged, test scores are improving, or other positive outcomes are being reported. There are even reports of students struggling with severe ADHD getting iPads for instruction and improving leaps and bounds. Now that doesn't mean that this is going to happen in every school. No one can promise amazing results because the ultimate arbiters of how your program works is going to be the students, faculty, and administration. But a lot of people are starting to feel like standing in the same place isn't really an option and the data up to this point indicates that a one-to-one program can have enormous benefits. I think this this last quote sums it up pretty nicely. This 2007 study determined that overall the use of the one-to-one laptops appears to contribute generally to the effectiveness of the learning environments per the design criteria of being more learner, assessment, community, and knowledge centered. So I'd like to talk a little bit about how we did it and how I would recommend you doing. And the first thing I would like to recommend is that you free yourself from the insane expectations that so many of us put on ourselves as teachers and administrators. We didn't expect immediate effects and instead we focused on creating an effective long-term plan for success. We weren't afraid to make mistakes. Our administration supported our efforts and we had a great deal of professional development which helped create a foundation of knowledge for the faculty to work from. We also embraced new ideas. More importantly, when our new ideas didn't work, we tried and tried until we found something that did. We pushed forward with blended instruction, flipped our classrooms, and created personal learning environments for our students. Blended learning is the term used to describe combining physical and virtual classroom elements to create a personalized learning environment for your students. A basic example of this might involve asking your students to go home and watch Khan Academy lecture on factoring polynomials the night before classroom lesson on the same topic. A more advanced approach might attempt a virtual field trip, individualized quizzes for real differentiation, or even an entirely flipped classroom in which what we usually consider homework is completed in class while daily lessons are delivered via the web for a student to view and hopefully review at his or her own pace. It's liberating to know that when the bell rings, class and the ideas contained within it just don't end and that's a simple distillation of the concept beyond the limitless classroom and the cloud classroom. Our goal is to untether the classroom from the physical space when we talk about blended learning, flipped classrooms, personal learning environments. And all that leads us to this idea of a cloud classroom. And one of the ways we create a cloud classroom is by integrating a different a series of different apps, websites, programs, all of which allow us to store resources, offer students ways to access those resources, and even provide different levels of instruction at home or via the device that we've given them. No more forgetting books, no more losing handouts, no more misplacing quizzes. All of that stuff can be handled by storage in the cloud. A cloud classroom extends the reach of your school. Every day's lesson is going to be available on Edmodo or whatever site you choose. I know Nearpod is another competitor, as are the students grades, homework assignments, and group assignations. The dog isn't going to eat anyone's homework. Student work can be graded and returned via iAnnotate or a similar annotation app, so there's a digital trail for everything students turn in. And everything I'm talking about is available through apps and browsers, so no devices excluded. Dropbox, Edmodo, Evernote, Khan Academy, they're all incredibly useful and incredibly powerful across every platform. So you can crank up your Apple 2e. If it has a web browser, you're good to go. And now that I think about an Apple 2e probably doesn't have a web browser. Going one to one involves creating a student environment that mirrors and improves upon the classroom. I want to thank you for listening to me today. Once again, I'd like to thank Dr. Friar in the K-12 online learning conference and everyone here today for spending some time with me. If this was helpful, my book Going One to One, iPads and Mobile Computing in the Classroom, will be available in November. Thanks very much, enjoy the rest of the presentations, and have a great time.