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Educational Data Mining (EDM): Turning Big Data into Big Gains for Students
 
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From making travel plans, to online purchases, to watching videos, each day we generate vast amounts of data that contribute to the world of big data. We have already seen big data play a significant role in areas like marketing and science. Now, education has joined the big data movement. In the past, education data was sparse and disparate. Collected across individual gradebooks and housed within multiple platforms, data was inaccessible, laborious, and difficult to analyze. Thankfully, this has changed. Now, educators and researchers can access incredibly rich and meaningful logs about student learning behavior on educational software, and by employing EDM (education data mining), discover a great deal about how students learn. By connecting this powerful data and asking the right questions, there is potential to change the future of education. Learn about the ability to leverage meaningful data with EDM and learning analytics, and find out how to turn big data into big gains for students. Attend this webinar to discover how: Learning analytics and EDM are already transforming education EDM advancements can assess students’ knowledge as they are learning Specific EDM methods are proving useful in understanding and predicting which students are likely to succeed in 21st century careers Learning analytics can provide insight into the effectiveness of educational technology programs and the conditions under which these programs have the greatest return on learning
Views: 1241 eschoolnews
Why is Educational Data Mining Important?
 
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Leading experts from the field of Educational Data Mining weigh in on why educational data mining is important. FEATURING David Lindrum (Founder & Course Designer, Soomo Learning) Piotr Mitros (Chief Scientist, edX) April Galyardt (Assistant Professor, University of Georgia College of Education) Ryan Baker (Associate Professor of Cognitive Studies, Teachers College Columbia University) Tiffany Barnes (Associate Professor of Computer Science, North Carolina State University) RECORDED AND PRODUCED BY Timothy D. Harfield
Views: 913 Timothy Harfield
Data Mining in Education
 
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I created this video with the YouTube Video Editor (http://www.youtube.com/editor)
Views: 719 stlgretchen
Educational Data Mining: Predict the Future, Change the Future
 
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Teachers College is proud to introduce the 2012-13 Julius and Rosa Sachs Distinguished Lecturer Professor Ryan Baker, Columbia University. Ryan Shaun Joazeiro de Baker is Visiting Associate Professor in the Department of Human Development. He earned his Ph.D. in Human-Computer Interaction from Carnegie Mellon University, and was a post-doctoral fellow in the Learning Sciences at the University of Nottingham. He earned his Bachelor's Degree (Sc.B.) in Computer Science from Brown University. Dr. Baker has been Assistant Professor of Psychology and the Learning Sciences at Worcester Polytechnic Institute. He previously served as the first Technical Director of the Pittsburgh Science of Learning Center DataShop, the largest public repository for data on the interaction between learners and educational software. He is currently serving as the founding President of the International Educational Data Mining Society, and as Associate Editor of the Journal of Educational Data Mining. His research combines educational data mining, learning analytics and quantitative field observation methods in order to better understand how students respond to educational software, and how these responses impact their learning. He studies these issues within intelligent tutors, simulations, and educational games. In recent years, he and his colleagues have developed strategies to make inferences in real-time about students' motivation, meta-cognition, affect, and robust learning.
Big Data, the Science of Learning, Analytics, and Transformation of Education
 
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From the mediaX Conference “Platforms for Collaboration and Productivity”, Candace Thille, with the Stanford Graduate School of Education highlights the power of platform tools and technologies to transform observation and data collection. This process enables researchers from industry and academia to know their user better – as consumers, as producers, and as learners.
Views: 10260 Stanford
What is Educational Data Mining?
 
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Leading experts from the field of Educational Data Mining weigh in what exactly they do. FEATURING David Lindrum (Founder & Course Designer, Soomo Learning) Tiffany Barnes (Associate Professor of Computer Science, North Carolina State University) Vineet Sinha (Director Analytics Platforms, Cengage Learning) Ryan Baker (Associate Professor of Cognitive Studies, Teachers College Columbia University) April Galyardt (Assistant Professor, University of Georgia College of Education) Scott McQuiggan (Director, SAS Curriculum Pathways) RECORDED AND PRODUCED BY Timothy D. Harfield
Views: 435 Timothy Harfield
Predicting Instructor Performance Using Data Mining Techniques in Higher Education
 
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Predicting Instructor Performance Using Data Mining Techniques in Higher Education -- Data mining applications are becoming a more common tool in understanding and solving educational and administrative problems in higher education. In general, research in educational mining focuses on modeling student's performance instead of instructors' performance. One of the common tools to evaluate instructors' performance is the course evaluation questionnaire to evaluate based on students' perception. In this paper, four different classication techniquesdecision tree algorithms, support vector machines, articial neural networks, and discriminant analysisare used to build classier models. Their performances are compared over a data set composed of responses of students to a real course evaluation questionnaire using accuracy, precision, recall, and specicity performance metrics. Although all the classier models show comparably high classication performances, C5.0 classier is the best with respect to accuracy, precision, and specicity. In addition, an analysis of the variable importance for each classier model is done. Accordingly, it is shown that many of the questions in the course evaluation questionnaire appear to be irrelevant. Furthermore, the analysis shows that the instructors' success based on the students' perception mainly depends on the interest of the students in the course. The ndings of this paper indicate the effectiveness and expressiveness of data mining models in course evaluation and higher education mining. Moreover, these ndings may be used to improve the measurement instruments. Articial neural networks, classication algorithms, decision trees, linear discriminant analysis, performance evaluation, support vector machines. -- For More Details Contact Us -- S.Venkatesan Arihant Techno Solutions Pudukkottai www.arihants.com Mobile: +91 75984 92789
Introduction to Data Mining for Educational Researchers
 
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Recording of a tutorial held at the second annual Learning Analytics Summer Institute on Data Mining aimed at Educational Researchers.
Views: 1125 Christopher Brooks
Data Mining for Educational Researchers
 
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Chris Brooks
Views: 157 LINK Lab
Data Mining in Higher Education
 
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Table of Contents: 00:04 - Better predict each student's Performance by taking into account More than grades 00:12 - Better manage marketing dollars for recruitment. 00:16 - Better understanding of factors related to struggling students, ultimately to increase retention. 00:22 - An understanding of support programs' effectiveness. 00:26 - Better understanding demographic and other factors 00:32 - Determine which non-need based packages attract the best students. 00:39 - What factors lead to student retention, especially at-risk students? 00:45 - Predict which students are likely to default on their student loans. 00:50 - Comment!
Views: 321 Salford Systems
Data Security and Google Apps for Education
 
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What is the best way to answer questions about Google and data mining in students accounts? In this video, we give you 3 tips to answering concerns. We have also created a Google Doc with lots of resources for you to look at. Google Privacy Information: https://docs.google.com/a/teacherstraining.com.au/document/d/13LyX0hTW7E3YRozYRq10eyLPLHOvIE8G8dw21E5vAJ4/edit# Want to stay up to date with more Google Apps tips & tricks? Click here to subscribe http://www.youtube.com/subscription_center?add_user=teacherstraining And........please click 'Like' You can also check out http://www.googleappsforedu.com
Webinar: Public Sector Data Mining in Higher Education
 
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If you have questions or comments on the contents of this video, please email us at [email protected] One of the biggest assets an organization or institution has is its data. That data contains patterns and relationships not readily identifiable. Enter Predictive Analytics. With IBM SPSS Modeler software, historical data is automatically mined detecting patterns and indicators which can be used to predict future outcomes, allowing you to prioritize efforts on those events which are most likely to occur. SPSS Modeler is a comprehensive analytics platform designed to bring predictive intelligence to decision making across your entire organization. Acquire customers more efficiently Grow value of existing customers Retain profitable customers Manage assets Maintain physical infrastructure Maximize capital Monitor your environment Detect suspicious behavior Control outcomes IBM SPSS Modeler connects data to effective action by drawing reliable conclusions about current conditions and future events. Attend this free webinar to hear how Predictive Analytics can make a difference in the public sector, specifically in the area of higher education. Listen to our SPSS expert discuss and demonstrate how SPSS Modeler software can help predict: Which students are most likely to graduate? Who are the most promising applicants for admission? Which alumni will donate and how much? How can the educational institution more reliably plan for future development? How can tuition and donor forecasts be driven by data and made more accurate?
Web mining || Data Mining || HIndi
 
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Here you will find all videos related to education.My only motto is make education completely free. Please stay tuned for more. Looking for tutors too if interested revert to me......
Views: 206 Rishabh Pandey
Data Mining Interpreted by Education Expert Joy Pullmann
 
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Joy Pullmann, an education research fellow at the Heartland Institute, discusses Common Core data mining and testing consortiums with Duke Pesta. She describes how one set of standards provides opportunity for the federal government to collect information such as student behavior, attitude, persistence, and other nonacademic data in order to change how the students think and act. More news at: http://www.thenewamerican.com/ Facebook: http://www.facebook.com/TheNewAmerican Twitter: https://twitter.com/NewAmericanMag *The views expressed by the interviewee(s) in this video do not necessarily reflect the views of The New American or any of its affiliates.*
Prediction of Student Results #Data Mining
 
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We used WEKA datamining s-w which yields the result in a flash.
Views: 34236 GRIETCSEPROJECTS
Using Data to Analyze Learning
 
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Introduction to Educational Data Mining, Dr. Luc Paquette
Views: 1078 Education at Illinois
A Systematic Review on Educational Data Mining
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/clickmyproject Mail Us: [email protected]
Views: 75 Clickmyproject
Ethics of Data Mining and Predictive Analytics in Higher Education
 
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Presented at the Rocky Mountain Association for Institutional Research Conference Laramie, Wyoming | October 5, 2012 Data mining and predictive analytics are increasingly used in higher education to classify students and predict student behavior. But while the potential benefits of such techniques are significant, realizing them presents a range of ethical and social challenges. The immediate challenge considers the extent to which data mining's outcomes are themselves ethical with respect to both individuals and institutions. A deep challenge, not readily apparent to institutional researchers or administrators, considers the implications of uncritical understanding of the scientific basis of data mining. These challenges can be met by understanding data mining as part of a value-laden nexus of problems, models, and interventions; by protecting the contextual integrity of information flows; and by ensuring both the scientific and normative validity of data mining applications.
Views: 772 Jeff Johnson
Higher Education Access Prediction Using Data Mining
 
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This Video shows the demonstration of “Online Auction Using Shill Bidding Prevention”. Human cheating has been a barrier to establishing trust among e-commerce users, throughout the last two decades. In particular, in online auctions, since all the transactions occur among anonymous users, trust is difficult to establish and maintain. At present, shill bidding is the most severe and persistent form of cheating in online auctions, but still there are only a few or no established techniques for shill defense at run-time. Evaluating the strengths and weaknesses of existing approaches to combating shill bidding. Proposed system prevents shilling activities by monitoring bidding behavior and user history. IP tracking techniques are proposed to detect shilling. The system also takes necessary actions against shill activities at run-time. The experimental results demonstrate that, by prevention, detection and response mechanisms, the proposed auction system keeps the auction users secured from shill bidding and therefore establishes trust among online auction users. Sellers may find lower prices where their items are sold at auctions which potential bidders are avoiding. To prevent this shill bidding process, we developed such a system that makes the bidding process neat and clean. To get this project, visit http://nevonprojects.com/online-auction-using-shill-bidding-prevention/ We provide Product Delivery and Customer Support Worldwide, so enter your country details on the website for the pricing details. CHECK OUT COLLECTION OF SOME OF OUR OTHER “Web - Based Project” 1) College Admission Predictor https://youtu.be/A8A6CCd1kzY 2) Exam Cell Automation System https://youtu.be/cfbuEkNyjYk 3) Web Content Trust Rating Prediction Using Evidence Theory https://youtu.be/92jgFhEWnf0 4) Customer Behavior Prediction Using Web Usage Mining https://youtu.be/YXS3r71wFAY 5) Web Mining For Suspicious Keyword Prominence https://youtu.be/CrWD8YzVafk To subscribe this channel click the link https://www.youtube.com/channel/UCisTN-GbgzzLRXftgnCJGKg?sub_confirmation=1 “Nevon Express” is our other channel, watch it at https://www.youtube.com/channel/UCJbZbcQI5PNDvP4TSZUkHRQ
Views: 13075 Nevon Projects
Student Performance Measure By Using Different Classification Methods Of Data Mining
 
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This video explains about the various classification methods of data mining to measure the performance of the students using the grades obtained in four semesters.
Views: 3103 Neha Choudhary
A Systematic Review on Educational Data Mining | Final year Projects 2016 - 2017
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 536 Clickmyproject
SAXually Explicit Images: Data Mining Large Shape Databases
 
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Google TechTalks May 12, 2006 Eamonn Keogh ABSTRACT The problem of indexing large collections of time series and images has received much attention in the last decade, however we argue that there is potentially great untapped utility in data mining such collections. Consider the following two concrete examples of problems in data mining. Motif Discovery (duplication detection): Given a large repository of time series or images, find approximately repeated patterns/images. Discord Discovery: Given a large repository of time series or images, find the most unusual time series/image. As we will show, both these problems have applications in fields as diverse as anthropology, crime...
Views: 4724 Google
[LAK 2012] May 1: 7 - Learning Analytics and Educational Data Mining
 
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George Siemens Ryan S. J. d. Baker Learning Analytics and Educational Data Mining: Towards Communication and Collaboration
Statistical Aspects of Data Mining (Stats 202) Day 1
 
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Google Tech Talks June 26, 2007 ABSTRACT This is the Google campus version of Stats 202 which is being taught at Stanford this summer. I will follow the material from the Stanford class very closely. That material can be found at www.stats202.com. The main topics are exploring and visualizing data, association analysis, classification, and clustering. The textbook is Introduction to Data Mining by Tan, Steinbach and Kumar. Googlers are welcome to attend any classes which they think might be of interest to them. Credits: Speaker:David Mease
Views: 217065 GoogleTechTalks
APPLICATION OF BIG DATA IN EDUCATION DATA MINING
 
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APPLICATION OF BIG DATA IN EDUCATION DATA MINING
Views: 315 Chennai Sunday
Google and CIA Internet Data Mining Project
 
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Google Ventures, the investment arm of Google, has injected a sum of up to $10 million, as has In-Q-Tel -- which handles investments for the CIA and the wider intelligence network -- into a company called Recorded Future. The company describes its analytics as "the ultimate tool for open-source intelligence". Wired's defence analyst, Noah Schachtman, has a detailed report on the joint venture: "...it scours tens of thousands of websites, blogs and Twitter accounts to find the relationships between people, organizations, actions and incidents — both present and still-to-come. In a white paper, the company says its temporal analytics engine "goes beyond search" by "looking at the 'invisible links' between documents that talk about the same, or related, entities and events." The idea is to figure out for each incident who was involved, where it happened and when it might go down. Recorded Future then plots that chatter, showing online "momentum" for any given event." Recorded Future "continually scans thousands of news publications, blogs, niche sources, trade publications, government web sites, financial databases and more," according to it's portfolio. It sifts through millions of posts and conversations taking place on blogs, YouTube, Twitter and Amazon to "assemble actual real-time dossiers on people." It is also being integrated with Google Earth, which, as Schachtman points out in his piece, was seeded with In-Q-Tel/CIA investment. This integration will allow real time tracking of the locations of persons or groups as part of the overall intelligence dossier. Recorded Future takes in vast amounts of personal information such as employment changes, personal education and family relations. The video also shows categories covering pretty much everything else, including entertainment, music and movie releases, as well as other innocuous things like patent filings and product recalls.
Views: 6257 the1dutchmaster
Statistical Aspects of Data Mining (Stats 202) Day 11
 
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Google Tech Talks August 3, 2007 ABSTRACT This is the Google campus version of Stats 202 which is being taught at Stanford this summer. I will follow the material from the Stanford class very closely. That material can be found at www.stats202.com. The main topics are exploring and visualizing data, association analysis, classification, and clustering. The textbook is Introduction to Data Mining by Tan, Steinbach and Kumar. Googlers are welcome to attend any classes which they think might be of interest to them. Credits: Speaker:David Mease
Views: 15572 GoogleTechTalks
Statistical Aspects of Data Mining (Stats 202) Day 10
 
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Google Tech Talks July 31, 2007 ABSTRACT This is the Google campus version of Stats 202 which is being taught at Stanford this summer. I will follow the material from the Stanford class very closely. That material can be found at www.stats202.com. The main topics are exploring and visualizing data, association analysis, classification, and clustering. The textbook is Introduction to Data Mining by Tan, Steinbach and Kumar. Googlers are welcome to attend any classes which they think might be of interest to them. Credits: Speaker:David Mease
Views: 18380 GoogleTechTalks
LASI14 Workshop: Introduction to Data Mining for Educational Researchers pt1 June 30, 2014
 
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Learning Analytics Summer Institute (LASI 2014) June 30, 2014 Workshop: Introduction to Data Mining for Educational Researchers, pt1 Christopher Brooks (University of Michigan), Zach Pardos (UC Berkeley), Vitomir Kovanovic (Simon Fraser University), Srecko Joksimovic (Simon Fraser University) http://solaresearch.org/conferences/lasi/lasi2014/lasi-2014-program-monday/ https://sites.google.com/a/umich.edu/lak-2014-tutorial-introduction-to-data-mining-for-educational-researchers/lasi-2014
Statistical Aspects of Data Mining (Stats 202) Day 9
 
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Google Tech Talks July 24, 2007 ABSTRACT This is the Google campus version of Stats 202 which is being taught at Stanford this summer. I will follow the material from the Stanford class very closely. That material can be found at www.stats202.com. The main topics are exploring and visualizing data, association analysis, classification, and clustering. The textbook is Introduction to Data Mining by Tan, Steinbach and Kumar. Googlers are welcome to attend any classes which they think might be of interest to them. Credits: Speaker:David Mease
Views: 12758 GoogleTechTalks
Data Mining Students Through Common Core
 
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http://www.thenewamerican.com/culture/education/item/15213-data-mining-students-through-common-core Awareness is growing rapidly about the recent initiative to bring Common Core Standards to schools across America. Although the standards were supposedly proposed by the National Governors Association (NGA) and the Council of Chief State School Officers (CCSSO) — giving the illusion that the agenda is “state-led,” it was the federal government that endorsed the plan by offering $4 billion in grant money through Obama’s Race to the Top program to cooperating states. Representative Blaine Luetkemeyer (R-Mo.) recently decided to take action and write a letter to U.S. Department of Education Secretary Arne Duncan and is currently seeking co-signers from congressional colleagues. Congressman Luetkemeyer addressed several issues of concern with Common Core — and in the last half of his letter he emphasized the crux of the problem: data mining. “We understand that as a condition of applying for [Race to the Top] grant funding, states obligated themselves to implement a State Longitudinal Database System (SLDS) used to track students by obtaining personally identifiable information,” Luetkemeyer said. “We formally request a detailed description of each change to student privacy policy that has been made under your leadership, including the need and intended purpose for such changes.” Parents might reasonably assume that the “personally identifiable information” collected for the database will include students' test scores and perhaps other measures of academic proficiency. But they would be much less likely to imagine that the federal government envisions something far more extensive and invasive than merely tracking academic performance. According to the Department of Education’s February 2013 report Promoting Grit, Tenacity, and Perseverance: Critical Factors for Success in the 21st Century, “Researchers are exploring how to gather complex affective data and generate meaningful and usable information to feed back to learners, teachers, researchers, and the technology itself. Connections to neuroscience are also beginning to emerge.” (Emphasis added.)
Views: 123 VisionLiberty
Digging Deep into Educational Data
 
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Ryan Baker, assistant professor of learning sciences and psychology at WPI, is an internationally known pioneer in educational data mining, which uses powerful algorithms to pull paradigm-changing insights from the vast quantities of data about how students interact with learning technologies.
Views: 1029 WPI
AN INVESTIGATION OF STUDENTS BEHAVIOR IN DISCUSSION FORUMS USING EDUCATIONAL DATA MINING.
 
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Presentation of published research at the Twenty-Eighth International Conference on Software Engineering and Knowledge Engineering (SEKE 2016).
Views: 167 Crystiano Jose
Learn Educational Data Mining
 
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EDM- Educational Data Mining
Views: 106 Dheeraj Mehrotra
LASI14 Workshop: Introduction to Data Mining for Educational Researchers pt2 June 30, 2014
 
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Learning Analytics Summer Institute (LASI 2014) June 30, 2014 Workshop: Introduction to Data Mining for Educational Researchers, pt2 Christopher Brooks (University of Michigan), Zach Pardos (UC Berkeley), Vitomir Kovanovic (Simon Fraser University), Srecko Joksimovic (Simon Fraser University) http://solaresearch.org/conferences/lasi/lasi2014/lasi-2014-program-monday/ https://sites.google.com/a/umich.edu/lak-2014-tutorial-introduction-to-data-mining-for-educational-researchers/lasi-2014
Statistical Aspects of Data Mining (Stats 202) Day 12
 
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Google Tech Talks August 7, 2007 ABSTRACT This is the Google campus version of Stats 202 which is being taught at Stanford this summer. I will follow the material from the Stanford class very closely. That material can be found at www.stats202.com. The main topics are exploring and visualizing data, association analysis, classification, and clustering. The textbook is Introduction to Data Mining by Tan, Steinbach and Kumar. Googlers are welcome to attend any classes which they think might be of interest to them. Credits: Speaker:David Mease
Views: 11001 GoogleTechTalks
Common Core Education EXPOSED!  Data Mining
 
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CLICK on a link below to jump to a segment. 00:00:00 Common Core Example 00:01:40 DENISE PURSCHE : Introduction 00:05:05 LIA BUSH : Panelist intros 00:08:10 SANDRA STOTSKY, Ed. D. 00:38:29 Dr. JAMES MILGRAM 01:10:10 KEVIN SNIDER, Attorney 01:19:55 ELISE COOKE: Data Mining 01:34:16 QUESTIONS 01:34:48 - Parent: our CC "opt our form" returned unopened 01:37:26 - Data Mining 01:40:48 - Data mining: what can we do? 01:44:18 - Did signers benefit for signing CC model? 01:46:05 - What do universities think of CC? 01:48:10 - No mention at all of "prime factorization"? 01:50:34 - Segregating children by collected data 01:52:49 - View of CC by substitute teacher 02:00:48 - Online tests and content of questions 02:03:46 - Why was CC implemented when it goes against the 10th amendment? 02:05:53 - What do you think of "mental math" and breaking apart of numbers? 02:09:16 - Our children have not been being taught for years... 02:13:50 - View from a teacher: "We are being coerced"... 02:19:26 SALLY WOOD: Closing Video by Steve Kemp
Views: 1170 Steve Kemp
Dhrubojyoti Mukherjee, Aegis Alumni sharing experience @Aegis School of Data Science
 
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Dhrubojyoti Mukherjee, Associate Data Analyst, LogisticsNow sharing experience @Aegis School of Data Science Education Qualification: Bachelor's Degree in Electronics Engineering from Pillai's Institute of Information Convocation ceremony convened by Aegis School of Business, Data Science, Cyber Security and Telecommunication to confer the Post Graduate Program in Data Science, Business analytics and Big Data in association with IBM at The Park Hotel in Navi Mumbai on 28th April 2019. Convocation proceedings were carried by Dr. Abhijit Gangopadhyay, Dean, Aegis School. He congratulated and awarded PGP certification by Aegis and IBM to all the graduating students. Srikanth Velamakanni, Co-founder & CEO of Fractal Analytics delivered the Convocation address to graduating students. Meet participants of Aegis School of Data Science's PGP/EPGP in Data Science, Business Analytics & Big Data in association with IBM. Get the Best Brains trained and certified jointly by IBM and Aegis having skills and competency in Data Science, Business Analytics, Big Data, Machine Learning, AI, Natural Language Process (NLP), Text Mining, Data Mining, Cognitive Computing, Hadoop, Spark, IBM Watson, IBM Cognos, Infosphere Big Insight, IBM SPSS, SAS, Tableau etc Write to Taranjit Oberai at [email protected] for your talent needs. Check Full Time PGP in Data Science, Business Analytics & Big Data in association with IBM at https://www.muniversity.mobi/PGP-DataScience/ Part Time Executive Weekend program in Mumbai, Pune, Bangalore, https://www.muniversity.mobi/Weekend-EPGP-DataScience/ Online Executive PGP Program worldwide https://www.muniversity.mobi/Online-EPGP-DataScience/ About Aegis: Aegis is a leading higher education provider in the field of Telecom, Data Science, Business Analytics, Big Data, Machine Learning, Deep Learning and Cyber Security. Aegis was started in 2002 with the support of Airtel Bharti, among the top five mobile operators to develop the cross functional techno-business leaders. Aegis is the number one school for Data Science and among the top five for business analytics in India. It has campuses in Mumbai, Pune and Banaglore. Aegis & IBM jointly delivers full time and Executive Post Graduate Program/MS in Data Science, Business Analytics, Big Data and Cyber Security. Aegis offers Deep Learning courses in partnership with NVIDIA. Find more about Aegis at www.aegis.edu.in www.mUniversity.mobi/Aegis
Views: 19 Aegis TV
Data-driven Education | Khurram Virani | TEDxWestVancouverED
 
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Data-driven education - it's not what you think. How Khurram is using data-driven education to personalize learning. Khurram is the co-founder and Head of Education at Lighthouse Labs, where he has been disrupting the way people learn to code. After spending 10 years as a developer and entrepreneur, he saw a problematic divide in technology between the way people learn and the way they work. By founding Lighthouse Labs and The HTML500, Canada's largest coding bootcamp and learn-to-code event respectively, he brought tech best practices into education to empower 100's of people with today's most valuable career skills - and taught them faster than ever before. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 13811 TEDx Talks
Data Mining for Privacy | Data Dialogs 2014
 
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Data Mining for Privacy Jessica Staddon, Google Data Dialogs Conference 2014 UC Berkeley School of Information http://datadialogs.ischool.berkeley.edu/ The privacy dangers of data mining are serious and much discussed. Data mining also can help us understand privacy attitudes and behaviors. This talk will cover some recent efforts to leverage public data to better support anonymity and understand topic sensitivity. Use cases include anonymous blogging, document sanitization and more user-friendly sharing and advertising. I will also talk about challenges in moving forward with this area of research and open problems.
RANT: SGT Report DELETED!! Data Mining/Mind Control Though Education Technology
 
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When YouTube decides to censor and delete channels that have over 300,000 subscribers for simply reporting the truth, it becomes apparent that many of us on this platform are probably not too far behind. For many of us who have been exposing the nefarious activities of the elite, there will come a cross roads when we will either have to comply to their brand of useless mind manipulating content, or be cut off. The enforcement won't necessarily have to come through some sort of violent uprising. Rather, it will come from the education system itself, programming the minds of an entire generation with the cloud of data and recorded experiences to "learn" from. The elimination of such individualism will result in the loss of identity, in exchange for something else...just like losing the image of God in exchange for the image of the beast. LINKS SGT Report Back up Channel https://www.youtube.com/user/SGTreport https://www.sgtreport.com/2018/07/you-tube-is-even-worse-than-big-brother-they-delete-history-altogether/ ADL http://www.adlnet.gov/research SRI Ventures https://www.sri.com/engage/ventures/all PERLS http://www.adlnet.gov/PERLS Leaked Google Video https://www.youtube.com/watch?v=QDVVo14A_fo&app=desktop ≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡ ◘SUBSCRIBE!! https://goo.gl/xtq0Vs ◘PLEASE CONSIDER SUPPORTING THIS MINISTRY -Paypal: https://goo.gl/lW4Q5K -Patreon - https://www.patreon.com/Facelikethesun -Bitcoin Donation: 13waWLWfRhxSoCPcf7MPg1VCATvvhVCHwZ -Ethereum Donation: 0x178d86c4F8497CFbD234c66bE28824947f4F3E1D ◘FaceLikeTheSun Blog http://facelikethesun.com ◘Canary Cry Radio YouTube Channel http://goo.gl/Qtd44w ◘PODCAST Canary Cry Radio http://canarycryradio.com ◘Facebook http://facebook.com/canarycryradio ◘Twitter http://twitter.com/facelikethesun ◘steemit https://steemit.com/@facelikethesun ◘AGE OF DECEIT: Fallen Angels and the New World Order (2011) https://goo.gl/MLlM2g ◘AGE OF DECEIT 2: Alchemy and the Rise of the Beast Image (2014) https://goo.gl/SkT8KJ
Views: 23883 FaceLikeTheSun
The Murder of Student Privacy
 
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In the background of our already crazy world, a deeper issue is happening. Students are being hunted by the big evil predator, Google. Their data is the bounty that Google oh so desperately hunts after. The Murder of Student Privacy is going to walk you through four different perspectives on this digital issue. To begin, we look here at the perspective of the Google Education Executive. Works Cited “Google Deceptively Tracks Students’ Internet Browsing, EFF Says in FTC Complaint.” Electronic Frontier Foundation, 1 Dec. 2015, https://www.eff.org/press/releases/google-deceptively-tracks-students-internet-browsing-eff-says-complaint-federal-trade. Kronk, Henry. “Is G Suite for Education Mining Student Data? Does It Matter?” ELearningInside News, 9 Nov. 2017, https://news.elearninginside.com/g-suite-education-mining-student-data-matter/. Malkin, Michelle. “The Student Data-Mining Scandal Under Our Noses.” National Review, 11 Apr. 2018, https://www.nationalreview.com/2018/04/the-student-data-mining-scandal-under-our-noses/. Singer, Natasha. “How Google Took Over the Classroom.” The New York Times, 13 May 2017, https://www.nytimes.com/2017/05/13/technology/google-education-chromebooks-schools.html.
Views: 66 Jennifer Hill
Abschlussvortrag – Educational Data Mining
 
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Das großflächige Sammeln, Verknüpfen und automatisierte Auswerten von Daten verändert unsere Lebenswelt auf vielfältige (und nicht immer nur positive) Weise. Durch die fortschreitende Digitalisierung in der Bildung, z. B. durch e-Learning Systeme, können diese unter den Schlagworten "Data Mining" bzw. "Big Data" zusammengefassten Methoden nun auch vermehrt in der Bildungsforschung eingesetzt werden. Dieses "Educational Data Mining" erlaubt neue Einblicke in Lehr-Lern-Prozesse, die bisher nicht möglich waren. Es werden einige grundlegende Methoden und Ergebnisse dieses noch recht jungen Wissenschaftszweigs vorgestellt und diskutiert, inwiefern sie auch für den eigenen Unterricht relevant sein können. Vortrag von Andreas Mühling, Informatik-Didaktik, Universität Kiel auf der MINTdigital Lehrertagung 2017.
Tubes - Educational Data Mining
 
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Views: 202 Desepta Isna Ulumi
Nikunj Oza: "Data-driven Anomaly Detection" | Talks at Google
 
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This talk will describe recent work by the NASA Data Sciences Group on data-driven anomaly detection applied to air traffic control over Los Angeles, Denver, and New York. This data mining approach is designed to discover operationally significant flight anomalies, which were not pre-defined. These methods are complementary to traditional exceedance-based methods, in that they are more likely to yield false alarms, but they are also more likely to find previously-unknown anomalies. We discuss the discoveries that our algorithms have made that exceedance-based methods did not identify. Nikunj Oza is the leader of the Data Sciences Group at NASA Ames Research Center. He also leads a NASA project team which applies data mining to aviation safety. Dr. Ozaąs 40+ research papers represent his research interests which include data mining, machine learning, anomaly detection, and their applications to Aeronautics and Earth Science. He received the Arch T. Colwell Award for co-authoring one of the five most innovative technical papers selected from 3300+ SAE technical papers in 2005. His data mining team received the 2010 NASA Aeronautics Research Mission Directorate Associate Administratorąs Award for best technology achievements by a team. He received his B.S. in Mathematics with Computer Science from MIT in 1994, and M.S. (in 1998) and Ph.D. (in 2001) in Computer Science from the University of California at Berkeley.
Views: 8122 Talks at Google
Common Core and Data Mining - The Transformation of American Education
 
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This video contains a thorough breakdown of the Common Core Standards, Assessments, Curricula and data mining. It explains the impact on children and the goals of the CCSS' authors and prime supporters. Most disturbing is the dishonesty with which the authors and copyright holders have shamelessly promoted CCSS as being state led and internationally bench marked when neither is true. You will see one of the highest officials in government tell audiences the government does not have access to student data and see the signed agreements proving this statements is utterly false. You will see how Common Core creates barriers to teacher input to the standards to gain improvements and creates an educational environment that deters parental engagement with teachers and learners. Finally you will see how the CCSS has not been proven to help children become college and career ready, the program's stated objective. Rather, the entire Common Core system migrates the control of all education to the federal government, placing the states under the thumb of the Department of Education. Uniformity and federal control and access are the key accomplishments of Common Core.
Views: 2327 John Anthony