Download or read book Enrollment Projection Through Data Mining written by Svetlana S. Aksenova and published by . This book was released on 2005 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Data Mining in Action Case Studies of Enrollment Management written by Jing Luan and published by Jossey-Bass. This book was released on 2006-12-15 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume introduces data mining through case studies of enrollment management. Six case studies employed data mining for solving real-life issues in enrollment yield, retention, transfer-outs, utilization of advanced-placement scores, and predicting graduation rates, among others. The authors furnish a tangible sense of data mining at work. The volume also demonstrates that data mining bears great potential to enhance institutional research. The opening chapter deciphers the similarities and differences between data mining and statistics, debunks the myths surrounding both data mining and traditional statistics, and points out the intrinsic conflict between statistical inference and the emerging need for individual pattern recognition and resulting customized treatment of students - the so-called new reality in applied institutional research. This is the 131st volume of New Directions for Institutional Research, a quarterly journal published by Jossey-Bass. Click here to see the entire list of titles for New Directions for Institutional Research.

Download or read book Proceedings of the 3rd International Symposium of Information and Internet Technology SYMINTECH 2018 written by Mohd Azlishah Othman and published by Springer. This book was released on 2019-05-15 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers the proceedings of a symposium on the role of Internet technologies and how they can transform and improve people’s lives. The Internet is essentially a massive database where all types of information can be shared and transmitted. This can be done passively in the form of non-interactive websites and blogs; or it can be done actively in the form of file sharing and document up- and downloading. Thanks to these technologies, a wealth of information is now available to anyone who can access the Internet. Moreover, Internet technologies are constantly improving: growing faster, offering more diverse information, and supporting processes that would have been impossible in the past. As a result, they have changed, and will continue to change, the way that the world does business and how people interact in their day-to-day lives. In conclusion, the symposium and these proceedings provide a valuable opportunity for leading researchers, engineers and professionals around the globe to discuss the latest advances that are helping the world move forward. They also facilitate the exchange of new ideas in the fields of communication technology to create a dialogue between these groups concerning the latest innovations, trends and concerns, practical challenges and potential solutions in the field of Internet technologies.

Download or read book Comparing Methods to Forecast College Enrollment written by Leon Taylor and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This article discusses how to forecast college enrollment as well as the number of credit hours demanded. The case studies a university in Central Asia that despite a strong reputation has been losing enrollment for nearly a decade. The effects of demographics on enrollment appear stronger than those of such traditional factors as tuition and income; but enrollment and hours may also respond to such characteristics of the university as prerequisite courses that are difficult. The article compares three ways of forecasting enrollment and credits: a structural approach, which predicts the effects of such determinants as tuition and student traits; a univariate approach, which predicts enrollment based on past enrollment; and data mining, which discerns patterns in big datasets through such new models as artificial neural networks. Of the three approaches, the structural one may be the best at explaining enrollment changes but does not necessarily yield the most accurate forecasts, as measured by the percentage error in the modeĺs predictions of past enrollment.

Download or read book Data Mining and Data Warehousing written by Barbara Mento and published by Association of Research Libr. This book was released on 2003 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The goal of this survey was to determine the extent to which data mining technology is being used by ARL member institutions, researchers, libraries and and administrations. The survey also hoped to elicit ideas and opinions concerning the potential role of libraries in supporting data mining and data warehousing in research institutions. The first seven survey questions focus on data mining and data warehousing activities at the institutional level. The remaining questions explore the current library use of data mining technology and opportunities for future use. Since data warehouses are the foundation of data mining, several questions focused on current support and future plans for data warehousing. The survey was sent to 124 ARL member libraries. Sixty-five (52%) responded to the survey"--P. 9.

Download or read book Proceedings of the Third International Conference on Innovations in Computing Research ICR 24 written by Kevin Daimi and published by Springer Nature. This book was released on with total page 794 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Using Data Mining Techniques to Identify the Best Operational Patterns for Enrollment Modeling written by Bogdan Catalin Obarse and published by . This book was released on 2009 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Handbook of Statistical Analysis and Data Mining Applications written by Ken Yale and published by Elsevier. This book was released on 2017-11-09 with total page 824 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. Includes input by practitioners for practitioners Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models Contains practical advice from successful real-world implementations Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications

Download or read book Data Mining Methods and Applications written by Kenneth D. Lawrence and published by CRC Press. This book was released on 2007-12-22 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: With today's information explosion, many organizations are now able to access a wealth of valuable data. Unfortunately, most of these organizations find they are ill-equipped to organize this information, let alone put it to work for them. Gain a Competitive Advantage Employ data mining in research and forecasting Build models with data management

Download or read book Data Mining and Learning Analytics written by Samira ElAtia and published by John Wiley & Sons. This book was released on 2016-09-20 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Educational Data Mining with R and Rattle written by R.S. Kamath and published by CRC Press. This book was released on 2022-09-01 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: Educational Data Mining (EDM) is one of the emerging fields in the pedagogy and andragogy paradigm, it concerns the techniques which research data coming from the educational domain. EDM is a promising discipline which has an imperative impact on predicting students' academic performance. 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. The intention is that reader will easily be able to replicate the given examples and then adapt them to suit their own needs of teaching-learning. The content of the book is based on the research work undertaken by the authors on the theme "Mining of Educational Data for the Analysis and Prediction of Students' Academic Performance". The basic know-how presented in this book can be treated as guide for educational data mining implementation using R and Rattle open source data mining tools. .Technical topics discussed in the book include:• Emerging Research Directions in Educational Data Mining• Design Aspects and Developmental Framework of the System• Model Development - Building Classifiers• Educational Data Analysis: Clustering Approach

Download or read book Responsible Analytics and Data Mining in Education written by Badrul H. Khan and published by Routledge. This book was released on 2018-12-07 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Data Mining and Learning Analytics written by Samira ElAtia and published by John Wiley & Sons. This book was released on 2016-09-26 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Institutional Research Initiatives in Higher Education written by Nicolas A. Valcik and published by Routledge. This book was released on 2017-11-06 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: American higher education faces a challenging environment. Decreasing state appropriations, rising costs, and tightening budgets have left American colleges and universities scrambling to achieve their missions with ever more limited resources. Campus leaders have therefore increasingly relied upon institutional research and strategic planning departments to make transparent and rational decisions and to promote good stewardship of critical but finite resources. Institutional Research Initiatives in Higher Education illustrates the wealth of institutional research activities occurring in American higher education. Featuring chapters by a prominent mix of authors representing community colleges, traditional undergraduate institutions, land grant institutions, research and flagship universities, and state agencies, this book provides numerous insights into the contemporary challenges, innovative programs, and best practices in institutional research. With contributors from a variety of regions and types of institutions, each chapter provides rigorous analysis of campus-based research activities in areas such as strategic planning, admissions and enrollment management, assessment and compliance, and financial planning and budgeting. Like the departments it studies, Institutional Research Initiatives in Higher Education is an invaluable resource for university administrators, researchers, and policymakers alike.

Download or read book Handbook of Operations Research and Management Science in Higher Education written by Zilla Sinuany-Stern and published by Springer Nature. This book was released on 2021-09-09 with total page 529 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook covers various areas of Higher Education (HE) in which operations research/management science (OR/MS) techniques are used. Key examples include: international comparisons, university rankings, and rating academic efficiency with Data Envelopment Analysis (DEA); formulating academic strategy with balanced scorecard; budgeting and planning with linear and quadratic models; student forecasting; E-learning evaluation; faculty evaluation with questionnaires and multivariate statistics; marketing for HE; analytic and educational simulation; academic information systems; technology transfer with systems analysis; and examination timetabling. Overviews, case studies and findings on advanced OR/MS applications in various functional areas of HE are included.

Download or read book Innovations in Artificial Intelligence and Human Computer Interaction in the Digital Era written by Surbhi Bhatia Khan and published by Elsevier. This book was released on 2023-07-22 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: Innovations in Artificial Intelligence and Human Computer Interaction in the Digital Era investigates the interaction and growing interdependency of the HCI and AI fields, which are not usually addressed in traditional approaches. Chapters explore how well AI can interact with users based on linguistics and user-centered design processes, especially with the advances of AI and the hype around many applications. Other sections investigate how HCI and AI can mutually benefit from a closer association and the how the AI community can improve their usage of HCI methods like “Wizard of Oz prototyping and “Thinking aloud protocols. Moreover, HCI can further augment human capabilities using new technologies. This book demonstrates how an interdisciplinary team of HCI and AI researchers can develop extraordinary applications, such as improved education systems, smart homes, smart healthcare and map Human Computer Interaction (HCI) for a multidisciplinary field that focuses on the design of computer technology and the interaction between users and computers in different domains. Presents fundamental concepts of both HCI and AI, addressing a multidisciplinary audience of researchers and engineers working on User Centered Design (UCD), User Interface (UI) design, and User Experience (UX) design Explores a broad range of case studies from across healthcare, industry, and education Investigates multiple strategies for designing and developing intelligent user interfaces to solve real-world problems Outlines research challenges and future directions for the intersection of AI and HCI

Download or read book Using Data Mining to Model Student Success written by Becky Geltz and published by . This book was released on 2009 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: As funding for higher education through federal and state sources continues to decline, and a stronger call for accountability is placed upon higher education institutions to graduate students within the expected amount of time, colleges and universities are looking for ways to best leverage their resources to attract college-ready students who will enroll in their institutions, remain enrolled consistently, and earn their undergraduate degrees in a timely manner. Federal research conducted by the U.S. Department of Education's National Center for Education Statistics through the Integrated Postsecondary Education Data System (IPEDS) examines aggregate student enrollment, degree completions, and graduation rates. But to be truly helpful to the institutional researcher, unit record data is required. Only by examining the many attributes of each individual student can an institution determine the unique characteristics which will lead to student academic success -- degree attainment. Because of the overall readability and the strong level of accuracy they can produce, decision trees are a good method for identifying the relationships between attributes in large datasets. Therefore, this study explores the use of data mining on higher education unit record data to develop a decision tree classification model of student success.