Search results “Learning analytics educational data mining”
Big Data, the Science of Learning, Analytics, and Transformation of Education
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: 9590 Stanford
Week 1: Introduction to Learning Analytics
Basic Introduction to Learning Analytics by George Siemens
Educational Data Mining (EDM): Turning Big Data into Big Gains for Students
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: 1085 eschoolnews
[LAK 2012] May 1: 7 - Learning Analytics and Educational Data Mining
George Siemens Ryan S. J. d. Baker Learning Analytics and Educational Data Mining: Towards Communication and Collaboration
Educational Data Mining: Predict the Future, Change the Future
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.
Using Data to Analyze Learning
Introduction to Educational Data Mining, Dr. Luc Paquette
Learning Analytics: Welcome to the future of assessment? (EdMedia2014 Keynote)
Education is about to experience a data tsunami from online trace data (VLEs; MOOCs; Quantified Self) integrated with conventional educational datasets. This requires new kinds of analytics to make sense of this new resource, which in turn asks us to reflect deeply on what kinds of learning we value. We can choose to know more than ever about learners and teachers, but like any modelling technology or accounting system, analytics do not passively describe sociotechnical reality: they begin to shape it. What realities do we want analytics to perpetuate, or bring into being? Can we talk about analytics in the same breath as the deepest values that a wholistic educational experience should nurture? Could analytics become an ally for those who want to shift assessment regimes towards valuing the qualities that many now regard as critical to thriving in the 'age of complexity'? Bio: Simon Buckingham Shum is Professor of Learning Informatics at the Open University's Knowledge Media Institute, where he is also Associate Director (Technology), overseeing knowledge and technology transfer to the OU. He researches, teaches and consults on Learning Analytics, Collective Intelligence and Argument Visualization. He co-edited Visualizing Argumentation (Springer 2003) followed by Knowledge Cartography (2008, 2nd Edition 2014). He served as Program Co-Chair of the 2nd International Learning Analytics conference, chaired the LAK13 Discourse-Centric Learning Analytics workshop, and the LASI13 Dispositional Learning Analytics workshop. He is a co-founder of the Society for Learning Analytics Research, Compendium Institute and and Learning Emergence. In August 2014, he joins the University of Technology Sydney as director of the new Connected Intelligence Centre. WWW: simon.buckinghamshum.net Downloadable slides: http://people.kmi.open.ac.uk/sbs/2014/06/edmedia2014-keynote
Views: 6399 Simon Buckingham Shum
Predicting Instructor Performance Using Data Mining Techniques in Higher Education
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
Recording of a tutorial held at the second annual Learning Analytics Summer Institute on Data Mining aimed at Educational Researchers.
Views: 1100 Christopher Brooks
Learning Analytics & Data Mining
Prepare for Professional Pathway #7: LEARNING ANALYTICS & DATA MINING: How do data scientists work within education? What skills, roles and impact are involved in this field? Read/Watch: Learning Analytics overview 2 min (video) Optional: Big Data, the Science of Learning, Analytics, and Transformation of Education (video, 20 min) To Do: Reflections on Pathway from previous week Weekly online activity: Portfolio- add Project Work LEARNING ANALYTICS & DATA MINING: Student presentation Guests: Professor Alyssa Wise Chad Coleman, DMDL alum
Views: 82 NYU ECT
Learning Analytics Webinar
Apereo webinar examining two critical standards in learning analytics space - xAPI, and IMSGlobal Caliper. The presenters are Aaron Silver, from DISC, the organisation tasked with xAPI interoperability and conformance (http://datainteroperability.org/ ), and Anthony Whyte of the University of Michigan, who has been centrally involved in recent IMS Caliper developments. The drive to deliver education at scale coupled with a demand for accountability backed by measurability has spurred the application of “big data” principles to the business of education. Opportunities to tap new data sources have grown as both the learning technology ecosystem has expanded and the definition of what constitutes learning has evolved beyond the formal classroom experience to include informal, social and experiential modes of acquiring knowledge and skills. The challenges implicit | inherent in describing, collecting and exchanging learner activity data originating from such diverse sources are formidable. In response, two learning technology specifications, ADL’s xAPI and IMS Global’s Caliper Analytics, now offer standardized approaches that aim to address the data and semantic interoperability issues inherent in the blending | sharing of learning data between systems. For both communities, early work has focused on developing a RESTful architecture for mediating machine-to-machine data exchange and grappling with the challenge of defining an information model and controlled vocabularies that leverage semantic web principles and practices. Join Aaron Silvers and Anthony Whyte for a lively discussion of the current state and possible futures of both xAPI and Caliper. The views expressed in this video are the presenters own.
Views: 1653 Apereo Foundation
Predictive Analytics For Education
What is Predictive Analytics? Predictive Analytics is the stream of the advanced analytics which utilizes diverse techniques like data mining, predictive modelling, statistics, machine learning and artificial intelligence to analyse current data and predict future. To Know more: https://goo.gl/zAcnCR Students At Risk – Predictive Analytics case study for Online education. Explains the challenges faced by online education and the how various models are built to draw the insights. Don’t miss the revelations in Conclusions section. A very interesting one!!Enjoy!! https://goo.gl/8nKgCH Like the Video follow us for more: Facebook: https://www.facebook.com/Altencalsoftlabs/ Twitter: https://twitter.com/Altencalsoftlab LinkedIn: https://in.linkedin.com/company/calsoft-labs-india-p-ltd-an-alten-group-company Google+: https://plus.google.com/u/0/+Altencalsoftlabs _________________________________________________________________ Looking for similar IT Services? Write to us [email protected] (OR) Visit Us @ https://www.altencalsoftlabs.com/
Views: 340 ALTEN Calsoft Labs
Digifest 2017: learner data and learning analytics
Find out more about our effective learning analytics project at https://www.jisc.ac.uk/rd/projects/effective-learning-analytics
Views: 664 Jisc
Learning Analytics: Where to Start?
Table Of Contents: About Unicon 4:19 Today’s Challenges:What We are Hearing From Customers 6:45 An Approach: Open Analytics Infrastructure 9:36 Unicon’s LA Quick Start Service 23:32 Q&A 32:51 Learning Analytics: Where to Start? This webinar presents our strategy for helping institutions get started with learning analytics. We focus on the components included with Unicon’s new LA Quick Start service - from an open analytics infrastructure that integrates data from the LMS into a Learning Record Warehouse using the IMS Caliper standard, to the Student and Course "Pulse" Visualizations. Also highlighted is a collaborative readiness assessment process, which provides institutions with a forum to identify concerns and challenge, from both organizational and technical perspectives. The following questions are addressed: • When it comes to learning analytics, how do you determine whether your institution is ready? • Is there a way to get started with learning analytics without making a big commitment of time and resources? • What are the key components to consider when building open analytics infrastructure at scale?
Views: 690 Unicon, Inc.
Intro to Learning Analytics
Presentation for LAK13 open online course
Views: 12586 George Siemens
Learning Analytics | Finding A Better Way To Do Education
There are many problems with how we do higher education. There is a disconnect between what's studied and the actual skills and knowledge needed for success. There are a lot of inefficiencies. Why aren't we leveraging technology to make education more affordable and accessible and adaptive to student needs? How can we use data and data science to address some of these problems? Have you ever heard of Learning Analytics? How does all of this tie in to the focus of my YouTube Channel? We'll discuss in this video. Read my full story here: http://www.sylvestermorgan.com/about/ Recommended Resources: Pluralsight: http://www.shareasale.com/r.cfm?B=971419&U=1472282&M=53701&urllink= In addition to many free resources, this is the online developer training I used to learn software developer. Read my review here: http://www.sylvestermorgan.com/resources/ Programming textbook that I started out with: http://amzn.to/2ppZzCV This book played a part in inspiring me to become a programmer. This author does a great job of teaching the basics. These books will help you achieve success beyond software development: The 7 Habits of Highly Effective People: Power Lessons in Personal Change: http://amzn.to/2pRfM5z Linchpin: Are you Indispensable: http://amzn.to/2pRclfs QBQ! The Question Behind the Question: Practicing Personal Accountability at Work and in Life: http://amzn.to/2oZK9SM How Successful People Think: Change Your Thinking, Change Your Life: http://amzn.to/2pq71Oz How Successful People Grow: 15 Ways to Get Ahead In Life: http://amzn.to/2ppRA9e How Successful People Win: Turn Every Setback into a Step Forward: http://amzn.to/2pq33W7 Soft Skills: The Software Developer’s Life Manual: http://amzn.to/2qrJZoY Connect: http://www.sylvestermorgan.com/ https://www.linkedin.com/in/sqlsylvester/ https://www.facebook.com/SQLSylvester/ https://twitter.com/SQLSylvester https://www.youtube.com/channel/UCVj_s6XbQcwlRMZjeO_7QSw Equipment I use to shoot my videos: Nikon D3200: http://amzn.to/2pRiLLu Ravelli AVTP Pro Tripod: http://amzn.to/2oZZdj0 CowboyStudio Backdrop: http://amzn.to/2pq62hz Fancierstudio Pro Lighting Kit: http://amzn.to/2ppVUoK Rode Video Mic Go: http://amzn.to/2pRcHCO Insignia - Lapel Mic: http://amzn.to/2oZUFJB Blue Snowball USB Microphone: http://amzn.to/2pzAcgH Disclaimer: This description contains affiliate links. This means that if you click on the links and purchase a product, I do receive a small commission. This helps support the work I do and allows me to continue bringing you guys valuable life changing content.
Views: 838 Sylvester Morgan
What is Educational Data Mining?
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: 354 Timothy Harfield
Data Mining for Educational Researchers
Chris Brooks
Views: 148 LINK Lab
Data Mining in Education
I created this video with the YouTube Video Editor (http://www.youtube.com/editor)
Views: 691 stlgretchen
How to be Successful with Edtech Data:
A short video reviewing big data in education, practical tips for success, and illuminating discoveries.
Views: 434 Macmillan Learning
How to predict students' performance?
Talk presented at SSCI2014, in Orlando. Download paper from: http://personal.ee.surrey.ac.uk/Personal/Norman.Poh/data/poh_gradcert.pdf Abstract: Student performance depends upon factors other than intrinsic ability, such as environment, socio-economic status, personality and familial-context. Capturing these patterns of influence may enable an educator to ameliorate some of these factors, or for governments to adjust social policy accordingly. In order to understand these factors, we have undertaken the exercise of predicting student performance, using a cohort of approximately 8,000 South African college students. They all took a number of tests in English and Maths. We show that it is possible to predict English comprehension test results from (1) other test results; (2) from covariates about self-efficacy, social economic status, and specific learning difficulties there are 100 survey questions altogether; (3) from other test results + covariates (combination of (1) and (2)); and from (4) a more advanced model similar to (3) except that the covariates are subject to dimensionality reduction (via PCA). Models 1-4 can predict student performance up to a standard error of 13-15%. In comparison, a random guess would have a standard error of 17%. In short, it is possible to conditionally predict student performance based on self-efficacy, socio-economic background, learning difficulties, and related academic test results.
Views: 6201 Norman Poh
Open Learning Analytics: Erik Duval at TEDxUHowest
Who Is Erik Duval? Erik chairs the research unit on human-computer interaction, at the computer science department of the Katholieke Universiteit Leuven. His research focuses on massive hyper-personalization ("The Snowflake Effect"), learning analytics, openness and abundance - topics on which he regularly keynotes. In practical terms, his team researches information visualization, mobile information devices, multi-touch displays and personal informatics. They typically apply their results to technology enhanced learning, access to music and 'research2.0′. More information on his activities and research team can be found at the pages of his research unit, his publications (see also his google scholar profile and his microsoft academic search profile) and his presentations. He also serves: - as chair of the IEEE LTSC working group on Learning Object Metadata, on the executive committee of the Society for Learning Analytics Research (SoLAR), - as a fellow of the AACE, - as a member of ACM, and the IEEE computer society. - on the Editorial Review Board and the Executive Advisory Board of the International Journal on E-Learning, - on the Editorial Board of the IEEE Transactions on Learning Technologies (TLT), - on the board of editors of the Journal of Universal Computer Science, - as a member of the informatics section of the Academia Europeae. He co-founded two spin-offs that apply research results for access to music and scientific output, as well as the not-for-profit ARIADNE Foundation that promotes share and reuse of learning material. What is this talk all about? Open Learning Analytics Learning is about empowerment. So, learning should be available to everybody. Open learning focuses on removing barriers and creating networks for authentic learning rather than the fake learning that characterizes much of formal education nowadays. As learning networks can involve thousands and as the interactions can be intense, the experience is sometimes overwhelming. However, as most activities take place on-line, we can collect the 'digital exhaust' in a Quantified Self approach to learning. Some use these traces for Educational Data Mining, in order to steer learners in the right direction. We believe more in a Learning Analytics approach where the relevant data are presented in an actionable way, in dashboards that put the learner in control. About TED and TEDx In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)
Views: 8181 TEDx Talks
Why is Educational Data Mining Important?
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: 796 Timothy Harfield
Data-driven Education | Khurram Virani | TEDxWestVancouverED
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: 12804 TEDx Talks
OLC talks about Educational Data Mining with Ryan Baker
Ryan will join this hangout and talk about the power of data mining and analytics.  This is a preview of Ryan's Plenary talk at the upcoming OLC Blended Learning Conference:  http://olc.onlinelearningconsortium.org/conference/2015/blended/educational-data-mining-potentials-moocs-and-blended-learning-higher-educati Learn more about Ryan: Ryan Baker is Associate Professor of Cognitive Studies at Teachers College, Columbia University. He earned his Ph.D. in Human-Computer Interaction from Carnegie Mellon University. Dr. Baker 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. He has taught the largest MOOC thus far on educational data mining, with over 50,000 students enrolled, and has written an online textbook based on the course, Big Data and Education. Dr. Baker also 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. His research combines educational data mining and quantitative field observation methods to better understand how students respond to educational software, and how these responses impact their learning and long-term outcomes. He studies these issues within intelligent tutors, simulations, multi-user virtual environments, and educational games. Register and join us in Denver, CO http://olc.onlinelearningconsortium.org/conference/2015/blended/registration
Views: 757 Sandra Coswatte
Lea's Box Project Learning Analytics
Learning Analytics education learning Lea's Box educational data mining
Views: 99 Lea's Box
Big Data and Education | PennX on edX
Enroll now: https://www.edx.org/course/big-data-education-pennx-bde1x Learn the methods and strategies for using large-scale educational data to improve education and make discoveries about learning. Online and software-based learning tools have been used increasingly in education. This movement has resulted in an explosion of data, which can now be used to improve educational effectiveness and support basic research on learning. In this course, you will learn how and when to use key methods for educational data mining and learning analytics on this data. You will examine the methods being developed by researchers in the educational data mining, learning analytics, learning-at-scale, student modeling, and artificial intelligence communities. You’ll also gain experience with standard data mining methods frequently applied to educational data. You will learn how to apply these methods and when to apply them, as well as their strengths and weaknesses for different applications. The course will discuss how to use each method to answer education research questions, and to drive intervention and improvement in educational software and systems. Methods will be covered at a theoretical level, and in terms of learning how to apply them using software tools like RapidMiner. We will also discuss validity and generalizability; establishing how trustworthy and applicable the analysis results. What you'll learn Key methods for educational data mining How to apply methods using standard tools such as RapidMiner How to use methods to answer practical educational questions
Views: 1728 edX
SPS2017: Educational Data Mining Software
The video is giving details about research software developed using WEKA (Open source Data Mining tool) and JAVA (Programming Language). The first version is developed in 2017. Anyone having the link can download this software and directly use this software without any installation. All the instructions are given in 'README.txt' file in a downloaded zip folder. The link to download the setup will be provided on request. Any suggestions and questions are invited in the comment section below. Feel free to add below. Developer: Er. Prabhjot Kaur Music Credits: Youtube Audio Library
Views: 86 Prabhjot Kaur
LAK '13: Interpreting Data Mining Results with Linked Data for Learning Analytics
Interpreting Data Mining Results with Linked Data for Learning Analytics: Motivation, Case Study and Directions. Mathieu D'Aquin, Nicolas Jay (Full) LAK Conference 2013, KU Leuven, Belgium
Views: 336 KULeuvenHCI
A Systematic Review on Educational Data Mining | Final year Projects 2016 - 2017
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Views: 487 Clickmyproject
Educational Data Mining in the Service of Building Detectors of Losing interest
Speaker Bio: Neil Heffernan is a professor of Computer Science and the co-director of the PhD program in Learning Sciences and Technologies.  He developed ASSISTments not only to help teachers be more effective in the classroom but also so that he could use the platform to conduct studies to improve the quality of education.  He is very passionate about educational data mining. Professor Heffernan enjoys supervising WPI students helping them create ASSISTments content and features. Several student projects have resulted in peer-reviewed publications looking at comparing different ways to optimize student learning.  Professor Heffernan's goal is to give ASSISTments to millions across the US and internationally as a free service of WPI.  The talk: During this talk, Neil talks about educational data mining and building better educational technology products.  He created ASSISTments at WPI, a product used by 50,000 last year to solve 12 million problems. Neil started ASSISTments about a decade ago, and ever sense then they have been logging student performance. In this session, Neil talks about a cool use of that data.
Views: 42 Shirin Mojarad
How Learning Analytics will benefit students [Telstra Expert Series]
The Telstra Expert Series on Education (Data Visualisation) covers discussion around the latest trends and topics in education and how data analytics is enabling and transforming modern learning practices. Further information can be found in the Telstra Education whitepaper: http://tel.st/z9th Telstra Education: http://telstra.com/education
Views: 7967 Telstra Enterprise
Webinar: Public Sector Data Mining in Higher Education
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?
Learning Analytics in Higher Education | UC3Mx on edX
Learn about the state of the art of learning analytics in higher education and developing strategies and policies for institutional adoption of learning analytics. Take this course for free on edx.org. This course gives an overview of learning analytics in higher education and introduces the SHEILA framework that can be used to support strategy and policy formation in addition to readiness assessment. The course begins with an introduction to the concept of learning analytics and drivers for institutional adoption, followed by the landscape of adoption in higher education, identified success and challenges, ethical and privacy issues, existing policy frameworks and higher education quality assurance. The second week of the course introduces the use of the SHEILA framework by highlighting three elements – key actions to take, key challenges to address and key questions to answer with regards to policy and strategy formation. In addition, a number of case studies are presented to demonstrate the use of the SHEILA framework in the real world. The third week of the course focuses on cultural impacts on stakeholder expectations. The adoption of learning analytics is often context-based and expectations tend to vary among different stakeholders. These will be key topics to cover in this week in addition to live panels made of learning analytics experts to share their experience with systematic adoption of learning analytics in higher education. This course will be taught by a group of instructors with rich experiences of learning analytics research. It will equip participants with the knowledge and skills required to plan and implement learning analytics in a complex educational system. This project has been funded with support from the European Commission [562080-EPP-1-2015-1-BE-EPPKA3-PIFORWARD]. The European Commission’s support for the production of this publication does not constitute an endorsement of the contents, which reflects the views only of the authors, and the Commission will not be held responsible for any use which may be made of the information contained therein.
Views: 1155 edX
Neil Heffernan and Ryan Baker: Educational Data Mining
Neil Heffernan & Ryan Baker, Worcester Polytechnic Institute, at the Cyberlearning Research Summit on January 18, 2012, Talk Set 5: Digital Books in Social Spaces with Educational Data Mining. See http://cyberlearning.sri.com
Views: 1501 cyberlearningvideos
Lorena Barba: Keynote - Data driven Education and the Quantified Student
PyData Seattle 2015 Education has seen the rise of a new trend in the last few years: Learning Analytics. This talk will weave through the complex interacting issues and concerns involving learning analytics, at a high level. The goal is to whet the appetite and motivate reflection on how data scientists can work with educators and learning scientists in this swelling field. Higher education has used analytics for a long time to guide administrative decisions. Universities are already adept at developing data-driven admissions strategies and increasingly they are using analytics in fund-raising. Learning analytics is a newer trend. Its core goal is to improve teaching, learning and student success through data. This is very appealing, but it's also fraught with complex interactions among many concerns and with disciplinary gaps between the various players. Faculty have always collected data on students' performance on assessments and responses on surveys for the purposes of grading and complying with accreditation, sometimes also for improving teaching methods and more rarely for research on how students learn. To call it Learning Analytics, though, requires scale and some form of systemic effort. Some early university efforts in analytics developed predictive models to identify at-risk first-year students, aiming to improve freshman retention (e.g., Purdue's "Signals" project). Others built alert systems in support of student advising, with the goal of increasing graduation rates (e.g., Arizona State University's "eAdvisor" system). Experts now segregate these efforts out of learning analytics, proper, because retention and graduation are not the same as learning. The goal, in that case, is to improve the function of the educational system, while learning analytics should be guided by educational research and be aimed at enhancing learning. To elucidate what is learning analytics, it looks like we first need to answer: what is learning? What is knowledge? And can more data lead to better learning? That is perhaps the zeroth assumption of learning analytics—and it needs to be tested. There are assumptions behind any data system that go as far back as selecting what to track, where it will be tracked, how it will be collected, stored and delivered. Most analytics is based on log data in the Learning Management System (LMS). This "learning in a box" model is inadequate, but the diverse ecosystem of apps and services used by faculty and students poses a huge interoperability problem. The billion-dollar education industry of LMS platforms, textbook publishers and testing companies all want a part in the prospect of "changing education" through analytics. They're all marketing their dazzling dashboards in a worrying wave of ed-tech solutionism. Meanwhile, students' every move gets tracked and logged, often without their knowledge or consent, adding ethical and legal issues of privacy for the quantified student. Slides available here: http://figshare.com/articles/Data_driven_Education_and_the_Quantified_Student/1495511
Views: 2879 PyData
Tubes - Educational Data Mining
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Digging Deep into Educational Data
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: 1026 WPI
LASI14 Workshop: Introduction to Data Mining for Educational Researchers pt1 June 30, 2014
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
The Learning Analytic Critic
Overview of Learning Analytics as mentioned in the 2011 Horizon Report, with a focus on speed and innovation. Tiffany W. Video Number: 7.F.2. Wissahickon High School.
Views: 682 ynaffitenuj
Presentation of published research at the Twenty-Eighth International Conference on Software Engineering and Knowledge Engineering (SEKE 2016).
Views: 165 Crystiano Jose
Privacy-preserving Data Sharing - Omar Ali Fdal
PyData Berlin 2018 This talk presents the issue of extracting insights from data sets containing sensitive information, while preserving privacy. We will review the techniques used today for private data sharing and their limitations. We will explain why it is hard to share useful data while preserving the privacy and point to promising approaches enabling the implementation of such a goal. Slides: https://www.slideshare.net/mobile/figago/privacypreserving-data-sharing-pydata-berlin-2018 --- www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 482 PyData
Learn data  Mining with Weka,data analytics by free online course. Find 2017 highest paying job.
Watch complete tutorial: https://click.linksynergy.com/fs-bin/click?id=gD7cdGyIKG4&offerid=529351.12&type=3&subid=0 What topics will you cover:data analytics ,what is data mining ,data mining definition,introduction to data mining,decision tree in data mining,datasets for data mining, educational data mining, weka data mining, data mining techniques,data mining concepts and techniques,data mining process ,data mining analysis ,data mining methods ,data mining in business . Master data mining software,data mining tools ,data analytics tools. We offer free online courses with certificates, online training,online study ,free online classes ,distance education,online learning,distance learning .
Views: 8 Kanji Yun
TLT Symposium 2013: Learning Analytics Discussion Panel
Big Data, business intelligence, educational data mining, and learning analytics are all increasingly on the radar of leaders in higher education. In this session, panelists will discuss the methods and implications of how Big Data is being used in education and industry to support decision making. Panelists will provide a variety of perspectives on this topic, including current initiatives at Penn State, and then open the floor for debate about how this might impact teaching and learning.
Views: 502 Penn State TLT
Educational Data Mining Prospects with Canvas | InstructureCon 2013
Brian Kokensparger of Creighton University Educational Data Mining uses mainstream data mining methods to work with educational data to accomplish educational objectives . Canvas offers attractive features to support educational data mining efforts. This session will present three ways to collect data from the Canvas LMS, as well as some examples of how Canvas data are already being used in data mining projects. Want to get your feet wet in data mining? This session will show you how.
Views: 672 CanvasLMS
Prediction of Student Results #Data Mining
We used WEKA datamining s-w which yields the result in a flash.
LASI 2015 Bilbao - Applications of Social Data Mining to Learning Analytics
LASI 2015 Bilbao: Learning Analytics Summer Institute. 22-23 junio 2015. Experiences Session, Kais Dai.
Getting education data right
The UNESCO Institute for Statistics is producing globally-comparable education data for the monitoring and implementation of SDG 4 and the Education 2030 agenda.
Views: 8708 UNESCO