Educational Data Mining
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Handbook Of Educational Data Mining
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Author : Cristobal Romero
language : en
Publisher: CRC Press
Release Date : 2010-10-25
Handbook Of Educational Data Mining written by Cristobal Romero and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-10-25 with Business & Economics categories.
This handbook provides a thorough overview of the current state of knowledge in this area. The first part of the book includes nine surveys and tutorials on the principal data mining techniques that have been applied in education. The second part presents a set of 25 case studies that give a rich overview of the problems that EDM has addressed. With contributions by well-known researchers from a variety of fields, the book reflects the multidisciplinary nature of the EDM community. It helps education experts understand what types of questions EDM can address and helps data miners understand what types of questions are important to educational design and educational decision making.
Educational Data Mining
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Author : Alejandro Peña-Ayala
language : en
Publisher: Springer
Release Date : 2013-11-08
Educational Data Mining written by Alejandro Peña-Ayala and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-08 with Technology & Engineering categories.
This book is devoted to the Educational Data Mining arena. It highlights works that show relevant proposals, developments, and achievements that shape trends and inspire future research. After a rigorous revision process sixteen manuscripts were accepted and organized into four parts as follows: · Profile: The first part embraces three chapters oriented to: 1) describe the nature of educational data mining (EDM); 2) describe how to pre-process raw data to facilitate data mining (DM); 3) explain how EDM supports government policies to enhance education. · Student modeling: The second part contains five chapters concerned with: 4) explore the factors having an impact on the student's academic success; 5) detect student's personality and behaviors in an educational game; 6) predict students performance to adjust content and strategies; 7) identify students who will most benefit from tutor support; 8) hypothesize the student answer correctness based on eye metrics and mouse click. · Assessment: The third part has four chapters related to: 9) analyze the coherence of student research proposals; 10) automatically generate tests based on competences; 11) recognize students activities and visualize these activities for being presented to teachers; 12) find the most dependent test items in students response data. · Trends: The fourth part encompasses four chapters about how to: 13) mine text for assessing students productions and supporting teachers; 14) scan student comments by statistical and text mining techniques; 15) sketch a social network analysis (SNA) to discover student behavior profiles and depict models about their collaboration; 16) evaluate the structure of interactions between the students in social networks. This volume will be a source of interest to researchers, practitioners, professors, and postgraduate students aimed at updating their knowledge and find targets for future work in the field of educational data mining.
Data Mining And Learning Analytics
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Author : Samira ElAtia
language : en
Publisher: John Wiley & Sons
Release Date : 2016-09-20
Data Mining And Learning Analytics written by Samira ElAtia and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-20 with Computers categories.
Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles— prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM’s emerging role in helping to advance educational research—from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.
Utilizing Educational Data Mining Techniques For Improved Learning Emerging Research And Opportunities
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Author : Bhatt, Chintan
language : en
Publisher: IGI Global
Release Date : 2019-08-02
Utilizing Educational Data Mining Techniques For Improved Learning Emerging Research And Opportunities written by Bhatt, Chintan and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-02 with Education categories.
Modern education has increased its reach through ICT tools and techniques. To manage educational data with the help of modern artificial intelligence, data and web mining techniques on dedicated cloud or grid platforms for educational institutes can be used. By utilizing data science techniques to manage educational data, the safekeeping, delivery, and use of knowledge can be increased for better quality education. Utilizing Educational Data Mining Techniques for Improved Learning: Emerging Research and Opportunities is a critical scholarly resource that explores data mining and management techniques that promote the improvement and optimization of educational data systems. The book intends to provide new models, platforms, tools, and protocols in data science for educational data analysis and introduces innovative hybrid system models dedicated to data science. Including topics such as automatic assessment, educational analytics, and machine learning, this book is essential for IT specialists, data analysts, computer engineers, education professionals, administrators, policymakers, researchers, academicians, and technology experts.
Responsible Analytics And Data Mining In Education
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Author : Badrul H. Khan
language : en
Publisher: Routledge
Release Date : 2018-12-07
Responsible Analytics And Data Mining In Education written by Badrul H. Khan and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-07 with Computers categories.
Winner of two Outstanding Book Awards from the Association of Educational Communications and Technology (Culture, Learning, & Technology and Systems Thinking & Change divisions)! Rapid advancements in our ability to collect, process, and analyze massive amounts of data along with the widespread use of online and blended learning platforms have enabled educators at all levels to gain new insights into how people learn. Responsible Analytics and Data Mining in Education addresses the thoughtful and purposeful navigation, evaluation, and implementation of these emerging forms of educational data analysis. Chapter authors from around the world explore how data analytics can be used to improve course and program quality; how the data and its interpretations may inadvertently impact students, faculty, and institutions; the quality and reliability of data, as well as the accuracy of data-based decisions; ethical implications surrounding the collection, distribution, and use of student-generated data; and more. This volume unpacks and explores this complex issue through a systematic framework whose dimensions address the issues that must be considered before implementation of a new initiative or program.
Educational Data Mining With R And Rattle
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Author : R. S. Kamath
language : en
Publisher: River Publishers
Release Date : 2016
Educational Data Mining With R And Rattle written by R. S. Kamath and has been published by River Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Computers categories.
It includes the transformation of existing, and the innovation of new approaches derived from multidisciplinary spheres of influence such as statistics, machine learning, psychometrics, scientific computing etc.An archetype that is covered in this book is that of learning by example.
Advancing The Power Of Learning Analytics And Big Data In Education
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Author : Azevedo, Ana
language : en
Publisher: IGI Global
Release Date : 2021-03-19
Advancing The Power Of Learning Analytics And Big Data In Education written by Azevedo, Ana and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-19 with Education categories.
The term learning analytics is used in the context of the use of analytics in e-learning environments. Learning analytics is used to improve quality. It uses data about students and their activities to provide better understanding and to improve student learning. The use of learning management systems, where the activity of the students can be easily accessed, potentiated the use of learning analytics to understand their route during the learning process, help students be aware of their progress, and detect situations where students can give up the course before its completion, which is a growing problem in e-learning environments. Advancing the Power of Learning Analytics and Big Data in Education provides insights concerning the use of learning analytics, the role and impact of analytics on education, and how learning analytics are designed, employed, and assessed. The chapters will discuss factors affecting learning analytics such as human factors, geographical factors, technological factors, and ethical and legal factors. This book is ideal for teachers, administrators, teacher educators, practitioners, stakeholders, researchers, academicians, and students interested in the use of big data and learning analytics for improved student success and educational environments.
The Future Of Learning Educational Data Mining For Every Individual
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Author : Edwin Kane
language : en
Publisher: Sunshine
Release Date : 2023-12-02
The Future Of Learning Educational Data Mining For Every Individual written by Edwin Kane and has been published by Sunshine this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-02 with Technology & Engineering categories.
Understanding Educational Data MiningIn the rapidly evolving world of education, the extraction of valuable insights from data has become more crucial than ever before. Educational Data Mining (EDM) is a powerful tool that enables educators, researchers, and policymakers to uncover patterns, trends, and relationships hidden within educational data. By leveraging these insights, stakeholders can make informed decisions to enhance learning outcomes and improve the overall educational experience for every individual. EDM utilizes data mining techniques specifically tailored to the field of education. It involves collecting, cleaning, analyzing, and interpreting large volumes of data generated within educational settings. This data could include information from student assessments, attendance records, course materials, and even data from online learning platforms. The primary goal of EDM is to transform raw data into actionable knowledge. By employing various statistical and machine learning algorithms, educational researchers can identify patterns that may not be easily discernible through traditional methods. These patterns can then be used to develop personalized learning strategies, identify at-risk students, and evaluate the effectiveness of educational interventions. For educators, EDM offers valuable insights into student behavior and performance. By analyzing data, teachers can gain a deeper understanding of how students learn, their strengths, and areas where additional support may be required. This knowledge allows educators to tailor their instruction to meet individual student needs, making the learning process more engaging and effective. Researchers also benefit from EDM by being able to conduct large-scale studies to investigate educational phenomena. By analyzing data from diverse populations, researchers can identify factors that contribute to student success and develop evidence-based recommendations for educational policies and practices. Policymakers can utilize EDM to make data-driven decisions that impact educational systems at a broader level. By analyzing aggregated data from multiple schools or districts, policymakers can identify systemic issues, develop targeted interventions, and allocate resources more effectively.
Educational Data Mining And Learning Analytics
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Author : Tai Tan Mai
language : en
Publisher: Springer
Release Date : 2023-11-11
Educational Data Mining And Learning Analytics written by Tai Tan Mai and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-11 with Education categories.
This book is the result of a collaboration between a human editor and an artificial intelligence algorithm to create a machine-generated literature overview of research articles analyzing Educational Data Mining and Learning Analytics. It’s a new publication format in which state-of-the-art computer algorithms are applied to select the most relevant articles published in Springer Nature journals and create machine-generated literature reviews by arranging the selected articles in a topical order and creating short summaries of these articles. The popularity of Educational Data Mining has grown among educators seeking more effective ways to monitor and incentivize student progress and engagement during the COVID-19 pandemic. This has led to increased interest within research communities. The book provides a comprehensive overview of state-of-the-art research in Education Data Mining and its applications. Each chapter includes case studies to support theoretical concepts. The book is of great interest for a wide range of audiences, including computer scientists and educational philosophers.
Modern Approach To Educational Data Mining And Its Applications
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Author : Soni Sweta
language : en
Publisher: Springer Nature
Release Date : 2021-01-22
Modern Approach To Educational Data Mining And Its Applications written by Soni Sweta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-22 with Technology & Engineering categories.
This book emphasizes that learning efficiency of the learners can be increased by providing personalized course materials and guiding them to attune with suitable learning paths based on their characteristics such as learning style, knowledge level, emotion, motivation, self-efficacy and many more learning ability factors in e-learning system. Learning is a continuous process since human evolution. In fact, it is related to life and innovations. The basic objective of learning to grow, aspire and develop ease of life remains the same despite changes in the learning methodologies. Introduction of computers empowered us to attain new zenith in knowledge domain, developed pragmatic approach to solve life’s problem and helped us to decipher different hidden patterns of data to get new ideas. Of late, computers are predominantly used in education. Its process has been changed from offline to online in view of enhancing the ease of learning. With the advent of information technology, e-learning has taken centre stage in educational domain. In e-learning context, developing adaptive e-learning system is buzzword among contemporary research scholars in the area of Educational Data Mining (EDM). Enabling personalized systems is meant for improvement in learning experience for learners as per their choices made or auto-detected needs. It helps in enhancing their performance in terms of knowledge, skills, aptitudes and preferences. It also enables speeding up the learning process qualitatively and quantitatively. These objectives are met only by the Personalized Adaptive E-learning Systems in this regard. Many noble frameworks were conceptualized, designed and developed to infer learning style preferences, and accordingly, learning materials were delivered adaptively to the learners. Designing frameworks help to measure learners’ preferences minutely and provide adaptive learning materials to them in a way most appropriately.