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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
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: 1177 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: 866 Timothy Harfield
classroom visualization through the eras + educational data  mining and E-learning
 
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visualization to a teacher situation through the eras of education, in order to illustrate the impact and the importance of using educational data mining technique
Views: 76 norahalhajri
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: 3012 Neha Choudhary
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.
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: 394 Timothy Harfield
SPS2017: Educational Data Mining Software
 
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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: 87 Prabhjot Kaur
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: 66 Clickmyproject
Sebastian Ventura. “Educational Data Mining”
 
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Charla magistral del Congreso Interacción 2014. Sebastian Ventura. “Educational Data Mining” --- Creative Commons: Reconocimiento - No Comercial - Sin Obra Derivada (CC BY NC ND). http://creativecommons.org/licenses/by-nc-nd/4.0/deed.es_ES
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: 523 Clickmyproject
INTRODUCTION TO DATA MINING IN HINDI
 
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Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 113498 LearnEveryone
How to predict students' performance?
 
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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: 6399 Norman Poh
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 Shop Now @ https://myprojectbazaar.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: 43 myproject bazaar
Predicting Instructor Performance Using Data Mining Techniques in Higher Education
 
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Views: 174 S3 TECHNOLOGIES
Data Mining in Education
 
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I created this video with the YouTube Video Editor (http://www.youtube.com/editor)
Views: 695 stlgretchen
A Review of Educational Data Mining Tools & Techniques IJETL  3, 1 ,17 23
 
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A Review of Educational Data Mining Tools & Techniques
A Review on Mining Students’ Data for Performance Prediction  | 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/clickmyproject Mail Us: [email protected]
Views: 863 Clickmyproject
Data Mining Techniques for Detecting Behavioral Patterns of Gifted Students in Online Learning
 
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The paper entitled "Data Mining Techniques for Detecting Behavioral Patterns of Gifted Students in Online Learning Environment (Case Study)" will be presented in the framework of the fourth edition of the international conference "The Future of Education" that will be held in Florence on 12 - 13 June 2014
Views: 243 Pixel Conferences
[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
Prediction of Student Results #Data Mining
 
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We used WEKA datamining s-w which yields the result in a flash.
Views: 33647 GRIETCSEPROJECTS
Data Mining for Educational Researchers
 
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Chris Brooks
Views: 154 LINK Lab
Educational Data Mining Prospects with Canvas | InstructureCon 2013
 
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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: 673 CanvasLMS
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Website: https://www.edureka.co/data-science Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 74413 edureka!
Data Mining - Foundations of Learning to Rank: Needs & Challenges | Lectures On-Demand
 
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Ambuj Tewari - EECS at the University of Michigan The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
Views: 3773 Michigan Engineering
data mining techniques
 
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This video describes data mining tasks or techniques in brief. Each technique requires a separate explanation as well. #datamining #techniques #weka Data mining tutorial in hindi Weka tutorial in hindi Data mining tutorial
Views: 7586 yaachana bhawsar
Final Year Projects | E - Learning
 
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Final Year Projects | E - Learning More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * 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/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 8894 Clickmyproject
Introduction to Clustering Techniques | Mahout Clustering techniques | Mahout Clustering Tutorial
 
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Watch Sample Class Recording: http://www.edureka.co/mahout?utm_source=youtube&utm_medium=referral&utm_campaign=clustering-tech Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. Know More about various clustering techniques through this video. Following are the topics covered in the video: 1.Difference between various clustering techniques. 2. K- means Clustering 3.Fuzzy K- means Clustering 4.Fuzzy K- means Clustering MapReduce flow. 5.Various clustering algorithms. Related Blogs http://www.edureka.co/blog/introduction-to-clustering-in-mahout/?utm_source=youtube&utm_medium=referral&utm_campaign=clustering-tech http://www.edureka.co/blog/k-means-clustering/?utm_source=youtube&utm_medium=referral&utm_campaign=clustering-tech Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would prefer a hassle free and self paced learning environment, accessible from any part of the world. The topics related to ‘Clustering Techniques’ have extensively been covered in our course ‘Machine Learning with Mahout’. For more information, please write back to us at [email protected] Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004
Views: 2545 edureka!
Week 1: Introduction to Learning Analytics
 
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Basic Introduction to Learning Analytics by George Siemens
Tubes - Educational Data Mining
 
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-- Created using PowToon -- Free sign up at http://www.powtoon.com/join -- Create animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 10 Desepta Isna Ulumi
Educational Data Mining in the Service of Building Detectors of Losing interest
 
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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
Data Mining in the Medical Field
 
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Video about data mining in the medical field. Made by Aditya Jariwala, Alex Truitt, Tongfei Zhang, and Yishi Xu for Purdue COM 21700 final project, Spring 2017.
Views: 3772 Aditya Jariwala
Data Mining as a healthcare research tool (Analytics Techniques Listed Below)
 
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Here are some additional techniques for data mining: 1. Decision Tree Analysis: https://www.youtube.com/watch?v=bJC5S_ViRCo 2. Text mining in Twitter: https://www.youtube.com/watch?v=I0VCGCnquTQ
Student Learning Evaluation - Predicting Student Performance
 
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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. Generally, 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 study, four different classification techniques, –decision tree algorithms, support vector machines, artificial neural networks, and discriminant analysis– are used to build classifier models. Their performances are compared over a dataset composed of responses of students to a real course evaluation questionnaire using accuracy, precision, recall, and specificity performance metrics. Although all the classifier models show comparably high classification performances, C5.0 classifier is the best with respect to accuracy, precision, and specificity. In addition, an analysis of the variable importance for each classifier 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 findings of the study indicate the effectiveness and expressiveness of data mining models in course evaluation and higher education mining. Moreover, these findings may be used to improve measurement instruments. Artificial neural networks, classification 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
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.
Using Data to Analyze Learning
 
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Introduction to Educational Data Mining, Dr. Luc Paquette
ASEE MidAtlantic Conference Talk on Educational Data Mining
 
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Washington D.C. ASEE Conference giving talk
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: 1026 WPI
Tubes - Educational Data Mining
 
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-- Created using PowToon -- Free sign up at http://www.powtoon.com/join -- Create animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 198 Desepta Isna Ulumi
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: 166 Crystiano Jose
K-Means Clustering Algorithm - Cluster Analysis | Machine Learning Algorithm | Data Science |Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4 Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #kmeans #clusteranalysis #clustering #datascience #machinelearning How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
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Learn data  Mining with Weka,data analytics by free online course. Find 2017 highest paying job.
 
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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 .
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Lecture 58 — Overview of Clustering | Mining of Massive Datasets | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Mining Social Media Data for Understanding Students’ Learning Experiences
 
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Students’ informal conversations on social media (e.g. Twitter, Facebook) shed light into their educational experiences—opinions, feelings, and concerns about the learning process. Data from such uninstrumented environments can provide valuable knowledge to inform student learning. Analyzing such data, however, can be challenging. The complexity of students’ experiences reflected from social media content requires human interpretation. However, the growing scale of data demands automatic data analysis techniques. In this paper, we developed a workflow to integrate both qualitative analysis and large-scale data mining techniques. We focused on engineering students’ Twitter posts to understand issues and problems in their educational experiences. We first conducted a qualitative analysis on samples taken from about 25,000 tweets related to engineering students’ college life. We found engineering students encounter problems such as heavy study load, lack of social engagement, and sleep deprivation. Based on these results, we implemented a multi label classification algorithm to classify tweets reflecting students’ problems. We then used the algorithm to train a detector of student problems from tweets streamed at the geo-location of Purdue University. This work, for the first time, presents a methodology and results that show how informal social media data can provide insights into students’ experiences.

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