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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
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: 2474 Neha Choudhary
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: 804 eschoolnews
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.
Data Mining  Association Rule - Basic Concepts
 
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short introduction on Association Rule with definition & Example, are explained. Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database. Parts of Association rule is explained with 2 measurements support and confidence. types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples. Names of Association rule algorithm and fields where association rule is used is also mentioned.
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
Views: 2151 yaachana bhawsar
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: 374 Clickmyproject
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: 57 norahalhajri
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: 673 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. Any suggestions and questions are invited in the comment section below. Feel free to add below. Music Credits: Youtube Audio Library
Predicting Instructor Performance Using Data Mining Techniques in Higher Education
 
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Views: 149 S3 TECHNOLOGIES
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: 37 Shirin Mojarad
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: 262 Timothy Harfield
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: 221 Pixel Conferences
Prediction of Student Results #Data Mining
 
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We used WEKA datamining s-w which yields the result in a flash.
Views: 28749 GRIETCSEPROJECTS
Learn Educational Data Mining
 
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EDM- Educational Data Mining
Views: 33 Dheeraj Mehrotra
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: 5299 Norman Poh
[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
Data Mining in Education
 
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I created this video with the YouTube Video Editor (http://www.youtube.com/editor)
Views: 666 stlgretchen
INTRODUCTION TO DATA MINING IN HINDI
 
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Views: 101675 LearnEveryone
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: 18 Clickmyproject
Linear Regression - Machine Learning Fun and Easy
 
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Linear Regression - Machine Learning Fun and Easy https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Hi and welcome to a new lecture in the Fun and Easy Machine Learning Series. Today I’ll be talking about Linear Regression. We show you also how implement a linear regression in excel Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. Dependent Variable – Variable who’s values we want to explain or forecast Independent or explanatory Variable that Explains the other variable. Values are independent. Dependent variable can be denoted as y, so imagine a child always asking y is he dependent on his parents. And then you can imagine the X as your ex boyfriend/girlfriend who is independent because they don’t need or depend on you. A good way to remember it. Anyways Used for 2 Applications To Establish if there is a relation between 2 variables or see if there is statistically signification relationship between the two variables- • To see how increase in sin tax has an effect on how many cigarettes packs are consumed • Sleep hours vs test scores • Experience vs Salary • Pokemon vs Urban Density • House floor area vs House price Forecast new observations – Can use what we know to forecast unobserved values Here are some other examples of ways that linear regression can be applied. • So say the sales of ROI of Fidget spinners over time. • Stock price over time • Predict price of Bitcoin over time. Linear Regression is also known as the line of best fit The line of best fit can be represented by the linear equation y = a + bx or y = mx + b or y = b0+b1x You most likely learnt this in school. So b is is the intercept, if you increase this variable, your intercept moves up or down along the y axis. M is your slope or gradient, if you change this, then your line rotates along the intercept. Data is actually a series of x and y observations as shown on this scatter plot. They do not follow a straight line however they do follow a linear pattern hence the term linear regression Assuming we already have the best fit line, We can calculate the error term Epsilon. Also known as the Residual. And this is the term that we would like to minimize along all the points in the data series. So say if we have our linear equation but also represented in statisitical notation. The residual fit in to our equation as shown y = b0+b1x + e To learn more on Augmented Reality, IoT, Machine Learning FPGAs, Arduinos, PCB Design and Image Processing then Check out http://www.arduinostartups.com/ Please like and Subscribe for more videos :) -------------------------------------------------- Support us on Patreon http://bit.ly/PatreonArduinoStartups --------------------------------------------------
Views: 97515 Augmented Startups
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://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: 246 myproject bazaar
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: 1037 Christopher Brooks
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: 2048 Aditya Jariwala
Data Mining for Educational Researchers
 
02:16:03
Chris Brooks
Views: 129 LINK Lab
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: 194 Desepta Isna Ulumi
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: 477 Clickmyproject
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. .
OLC talks about Educational Data Mining with Ryan Baker
 
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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: 719 Sandra Coswatte
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: 157 Crystiano Jose
Lecture 46 — Dimensionality Reduction - Introduction | 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. .
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: 659 CanvasLMS
ASEE MidAtlantic Conference Talk on Educational Data Mining
 
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Washington D.C. ASEE Conference giving talk
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
VirtualMine -  Educational Mining Offer in Slovenia
 
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The Golden Fleece between the Posavje and Secovjie salt pans. Educational Mining offer for childen and youth in Slovenia. Video by Project Partner ZAG
Tubes - Educational Data Mining
 
01:42
-- 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.
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
 
10:46
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: 13 myproject bazaar
Social Media Social Data and Python: 4 - Social Media Mining Techniques
 
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In this video we will briefly discuss the overall process for building a social media mining application, before digging into the details. ----- ------ Channel link: https://goo.gl/nVWDos Subscribe here: https://goo.gl/gMdGUE Link to playlist: https://goo.gl/WIHiEy ---- Join my Facebook Group to stay connected: http://bit.ly/2lZ3FC5 Like my Facebbok Page for updates: https://www.facebook.com/tigerstylecodeacademy/ Follow me on Twitter: https://twitter.com/sukhsingh Profile on LinkedIn: https://www.linkedin.com/in/singhsukh/ ---- Schedule: New educational videos every week ----- ----- Source Code for tutorials on Youtube: http://bit.ly/2nSQSAT ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh
Views: 1928 Sukhvinder Singh
Week 1: Introduction to Learning Analytics
 
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Basic Introduction to Learning Analytics by George Siemens
13. Classification
 
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag introduces supervised learning with nearest neighbor classification using feature scaling and decision trees. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 28806 MIT OpenCourseWare
Mining Social Media Data for Understanding Students’ Learning Experiences
 
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Abstract—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 about 35,000 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. Index Terms—Education, computers and education, social networking, web text analysis
Data Mining and Business Intelligence for Cyber Security Applications Summer Program at BGU
 
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The purpose of the Summer Program in Data Mining and Business Intelligence is to provide both theoretical and practical knowledge, including tools, on data mining. The program offers two academic courses (each for 3 credits), where students learn the basic tools of data mining and the utilization of machine learning techniques for solving cyber security problems. The program includes a mandatory one week internship at BGU’s Cyber Security Research Center. The internship corresponds with the course materials and contributes the practical experience component. In addition, students will take part in professional fieldtrips to leading companies, in order to enhance their understanding of data mining and cyber security To Apply: https://www.tfaforms.com/399172 For More information: www.bgu.ac.il/global
Views: 1126 BenGurionUniversity

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