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Homework assignments that learn from students. Courses tailored to fit individual pupils. Textbooks that talk back. This is tomorrow’s education landscape, thanks to the power of big data. These advances go beyond the much-discussed rise of online courses. As the New York Times-bestselling authors of Big Data explain, the truly fascinating changes are actually occurring Homework assignments that learn from students. Courses tailored to fit individual pupils. Textbooks that talk back. This is tomorrow’s education landscape, thanks to the power of big data. These advances go beyond the much-discussed rise of online courses. As the New York Times-bestselling authors of Big Data explain, the truly fascinating changes are actually occurring in how we measure students’ progress and how we can use that data to improve education for everyone, in real time, both on- and offline. Learning with Big Data offers an eye-opening, insight-packed tour through these new trends, for educators, administrators, and readers interested in the latest developments in business and technology.


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Homework assignments that learn from students. Courses tailored to fit individual pupils. Textbooks that talk back. This is tomorrow’s education landscape, thanks to the power of big data. These advances go beyond the much-discussed rise of online courses. As the New York Times-bestselling authors of Big Data explain, the truly fascinating changes are actually occurring Homework assignments that learn from students. Courses tailored to fit individual pupils. Textbooks that talk back. This is tomorrow’s education landscape, thanks to the power of big data. These advances go beyond the much-discussed rise of online courses. As the New York Times-bestselling authors of Big Data explain, the truly fascinating changes are actually occurring in how we measure students’ progress and how we can use that data to improve education for everyone, in real time, both on- and offline. Learning with Big Data offers an eye-opening, insight-packed tour through these new trends, for educators, administrators, and readers interested in the latest developments in business and technology.

30 review for Learning With Big Data (Kindle Single): The Future of Education

  1. 5 out of 5

    Daniel Aguilar

    Clear, brief, to the point. Raises interesting questions both about opportunities and dangers of Big Data applied to education considering educators, institutions and students interests.

  2. 4 out of 5

    Vish Wam

    The Utopian Classroom          Let us imagine a future- The year 2020. Here we are seated in one of the most sophisticated classrooms, learning previously undelivered or even unheard of courses, even while we are on the move.  Yes, you read that right. For, in this futuristic era, we take our classrooms along with our cell-phones and tablets. Every student gets individual tutoring through his smart-phone. His performance gets monitored to the minutest of details like: How long did it take for him The Utopian Classroom          Let us imagine a future- The year 2020. Here we are seated in one of the most sophisticated classrooms, learning previously undelivered or even unheard of courses, even while we are on the move.  Yes, you read that right. For, in this futuristic era, we take our classrooms along with our cell-phones and tablets. Every student gets individual tutoring through his smart-phone. His performance gets monitored to the minutest of details like: How long did it take for him to comprehend a video lecture; how often did he have to re-view a video etc. This is then used to recommend individualized suggestions on ‘What other lecture videos to view’, thus customizing education for learners. The sophistication here lies not in taking these classes online, but on what happens behind the curtains (which you will read about in the remainder of the article). The students of this future no longer find education stressful, they find it as pleasing as playing a computer game. What could have possibly lead to this promising future? To answer this question we travel a few years back. The Fourth Khan (Not of Bollywood):           2006 saw the emergence of Salman Khan into the domain of online education. This isn’t your Khan of the Bollywood, but the founder of Khan Academy; the one behind the steering wheels of change in the education sector. A decade later, now in 2016, Khan Academy hosts over 5000 video lessons on everything from Math and Science to Art History, with over 50 million students from over 200 countries signing up for its lessons. Currently an estimate reveals that more than 4 million exercises are done on the site each day. What started out as an attempt by the Indian American to tutor his school-going cousins online, has now bloomed into a rapidly booming sector in the heart of education. Being a business analyst, Khan could not keep away from incorporating statistics into his new venture, so much so that statistics is now the core of his education model. So what did it take to kick things off for Khan?:          A Few Lines of Code and a lot of Patience:Software codes are written to generate questions. Some codes in place also indicate whether answers from students are correct or wrong. With these inputs from students, the software tracks things like how many questions each student got right and wrong, the length of time taken to complete each, the time of the day when the work is done and so on. If the 10 minute video lessons form the heart of Khan Academy, then the Big-Data analytics running in the background form its head. That said, you may now be wondering on how this happens? What Clicks Convey?:           The information conveyed by students through interactions with the web-portal are all stocked into a database and analyzed with statistics to answer challenging questions- “Did students spend more time on questions they answered correctly or on ones they got wrong?”; “Were mistakes made because they didn’t understand the material or because they were simply tired?”; Some highly adventurous questions like: “What is the best course material that can be composed for a given student?”; When do students lose focus on a streaming video?; What is the general attention span offered by students for each concept related video? and so on. By answering these questions using data analysis, learning (for the first time in eons), is customized for each student. The impact doesn’t stop here. The system also aids students by providing them the most appropriate path of lessons to take through the topic. The whole platform is adaptive. It splits the entire set of assessment questions into levels of difficulties for each topic in a course. By “gamifying” the syllabus into discrete levels, students get to realize: At what stage of difficulty they need to work more on, and what concepts they need to particularly learn better. Hence students not only, learn at their own pace, but also in the sequence that works best for them. Ultimately, we not only obtain information on what students learn but completely on how they learn.Not only is this new system about analysis, but is also about how it represents analysis results to students and teachers. The complete analysis of a student’s performance is presented to them in the form of a pie-chart indicating What topics they had learnt; How quickly they learnt each of them; What their weaknesses were and so on; Is that all? The Teachers’ Predator Eye View:           The analysis is also set to help teachers fine tune their delivery for class-room sessions. In a certain project, school teachers were provided with “heat-maps” of the classes they lecture. This is a modified version of thermal vision, where student online-performance reports are correlated with their seating arrangements in their school classrooms, and a transformed map is generated. This map shows ‘hot zones’ in classroom seating-arrangement as regions where the lecture was neatly understood (evaluated through follow up student test assessments) and ‘cold zones’ as regions where students tended to fail.  This way of representing a classroom, would definitely aid lecturers finally realize what prevents their delivery from being completely understood.  Fasten your Seatbelts for a “Big-Change”:             The bottom line being that, this technology helps one realize the basic truth about student performance: What we consider a D grade student and an A grade student, based on performance in a single test, says very little about their true potential. When students can work at their own pace, in an instructional sequence that is best suited to them, even those seemingly less capable can outperform the best.  This system of utilizing data-analytics for fine-tuning education, forms the core of not only Khan Academy, but also other billion dollar ventures like Coursera, Udacity, EdX and so on. It’s high time the edu-sector also realizes the importance of this upcoming age and adapts. If not, we have the story of Amazon (the online shopping store), to look up for caution. When Amazon came out with a scheme for big-data driven book-recommendations, it nearly wiped out the business for traditional book selling stores like Higginbotham’s, Landmark etc. So we realize that, this case of the whole system evolving. We either adapt, or let change affect us. With all this said, provided that we adapt, we positively march towards our initial Utopian vision of a dream classroom.

  3. 5 out of 5

    Justohidalgo

    Four stars for a good, basic introduction to Learning Analytics. While there are already other, deeper works - I specially enjoyed Niall Sclater's-, this is a well-written, short piece that poses more questions than answers about the present and future of data when used in learning environments. Four stars for a good, basic introduction to Learning Analytics. While there are already other, deeper works - I specially enjoyed Niall Sclater's-, this is a well-written, short piece that poses more questions than answers about the present and future of data when used in learning environments.

  4. 4 out of 5

    Erika Bolger

    This was a very interesting read that discusses how “big data” is currently affecting our everyday lives and how this is making it’s way into education. This book is an example itself of big data in that it is only available in the digital format, mostly because they (the authors) want feedback/data about the book. They want feedback about how their audience is interacting with the book. They want to know how long it takes to read the book, how many people finish the book, what sections are high This was a very interesting read that discusses how “big data” is currently affecting our everyday lives and how this is making it’s way into education. This book is an example itself of big data in that it is only available in the digital format, mostly because they (the authors) want feedback/data about the book. They want feedback about how their audience is interacting with the book. They want to know how long it takes to read the book, how many people finish the book, what sections are highlighted or re-read, etc. All of this information is now available with platforms such as Kindle e-readers, tablets, etc.

  5. 5 out of 5

    Philippe

    Balanced and concise outline of a complex issue. The promises and pitfalls of computer-assisted, individualised, adaptive learning need to be reflected on, urgently and by all members of society. The implications of misgoverning this dilemma between efficiency and human freedom will be momentous. The authors of this essay clearly lay out the stakes but are prudent enough not to draw final conclusions. There is, however, an important element that is missing from the discussion in this short book. Balanced and concise outline of a complex issue. The promises and pitfalls of computer-assisted, individualised, adaptive learning need to be reflected on, urgently and by all members of society. The implications of misgoverning this dilemma between efficiency and human freedom will be momentous. The authors of this essay clearly lay out the stakes but are prudent enough not to draw final conclusions. There is, however, an important element that is missing from the discussion in this short book. Take the example of a research team that gathers data about the factors that predict when a student will drop out of classes. Data on more than a million students were collected across 33 variables. Some of these variables where generic (age, gender) while others were associated with student behavior at course level. One of the insights was that a strong predictor of dropping out was simply how many classes they were taking. Students were more likely to persist if they took fewer courses simultaneously in the beginning. There raises questions about the adequacy of public policy. US financial grants require the recipient to carry a full-time course load as condition for support. An important question is, however, how the 33 relevant variables were identified. Because that selection reflects how we conceptualise student accomplishment. In this case this may be a rather trivial question (which I doubt) but in other settings it clearly isn't. Take the development of smart cities where monitoring capacity will soon be pervasive, with sensors embedded in every nook and cranny of our urban environment. But does this data glut guarantee a `better' picture of urban reality? Only when we pick those variables that reveal the `essence' of the city. And that requires a mix of intuition and a deep conceptual understanding of what urban processes are about. The future may be bright for 'algorithmists' - statisticians, data and scientists - but we will also need deep systemic thinkers - philosophers, ethicists, sociologists - who are able to frame complex societal challenges in multiple ways. There's a great opportunity to mesh different intellectual traditions: exact sciences, humanities and design.

  6. 4 out of 5

    Caitlin

    Big Data Learning I enjoyed reading Big Data by Mayer-Schönberger and Cukier for a better understanding of what is to come with our educational system and technology. It was actually very inspiring, as well. It talks about how "big data" will be shaping our world in the years to come. By feedback, initialization, and probabilistic predictions we can better understand how, what, and why we learn. However, this was something I recommend for those new to this concept - still loved Big Data Learning I enjoyed reading Big Data by Mayer-Schönberger and Cukier for a better understanding of what is to come with our educational system and technology. It was actually very inspiring, as well. It talks about how "big data" will be shaping our world in the years to come. By feedback, initialization, and probabilistic predictions we can better understand how, what, and why we learn. However, this was something I recommend for those new to this concept - still loved

  7. 4 out of 5

    Ralyn Longs

    Interesting (short) book, setting out clearly the pros and cons of using big data in education.

  8. 4 out of 5

    Santiago Ortiz

    learning, data, learning about learning, data about learning, data about learning about data, and, specially, learning about data about learning

  9. 5 out of 5

    Sitki Cagdas Inam

  10. 5 out of 5

    Carmen

  11. 4 out of 5

    Jose Luis

  12. 5 out of 5

    Matthew Henderson

  13. 5 out of 5

    Jori

  14. 4 out of 5

    Tan Bee wah

  15. 5 out of 5

    Holger Meeh

  16. 4 out of 5

    Dhevi Sundari

  17. 5 out of 5

    Shakoko

  18. 5 out of 5

    B

  19. 5 out of 5

    Rani Adityo

  20. 4 out of 5

    Preeti Karthik

  21. 4 out of 5

    T M Morton

  22. 5 out of 5

    Zsolt

  23. 5 out of 5

    Yuan-Ping

  24. 4 out of 5

    Malte

  25. 5 out of 5

    Mark Traveller

  26. 5 out of 5

    Ashok

  27. 4 out of 5

    Ryan Lewis

  28. 4 out of 5

    Jillian

  29. 5 out of 5

    Pannathorn Naksung

  30. 4 out of 5

    Stephen

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