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Predicting Student Graduation In Higher Education Using Data Mining Models
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Book Synopsis Predicting Student Graduation in Higher Education Using Data Mining Models by : Dheeraj A. Raju
Download or read book Predicting Student Graduation in Higher Education Using Data Mining Models written by Dheeraj A. Raju and published by . This book was released on 2012 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predictive modeling using data mining methods for early identification of students at risk can be very beneficial in improving student graduation rates. The data driven decision planning using data mining techniques is an innovative methodology that can be utilized by universities. The goal of this research study was to compare data mining techniques in assessing student graduation rates at The University of Alabama. Data analyses were performed using two different datasets. The first dataset included pre-college variables and the second dataset included pre-college variables along with college (end of first semester) variables. Both pre-college and college datasets after performing a 10-fold cross-validation indicated no difference in misclassification rates between logistic regression, decision tree, neural network, and random forest models. The misclassification rate indicates the error in predicting the actual number who graduated. The model misclassification rates for the college dataset were around 7% lower than the model misclassification rates for the pre-college dataset. The decision tree model was chosen as the best data mining model based on its advantages over the other data mining models due to ease of interpretation and handling of missing data. Although pre-college variables provide good information about student graduation, adding first semester information to pre-college variables provided better prediction of student graduation. The decision tree model for the college dataset indicated first semester GPA, status, earned hours, and high school GPA as the most important variables. Of the 22,099 students who were full-time, first time entering freshmen from 1995 to 2005, 7,293 did not graduate (33%). Of the 7,293 who did not graduate, 2,845 students (39%) had first semester GPA
Book Synopsis The College Dropout Scandal by : David Kirp
Download or read book The College Dropout Scandal written by David Kirp and published by Oxford University Press. This book was released on 2019-07-01 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Higher education today faces a host of challenges, from quality to cost. But too little attention gets paid to a startling fact: four out of ten students -- that's more than ten percent of the entire population - -who start college drop out. The situation is particularly dire for black and Latino students, those from poor families, and those who are first in their families to attend college. In The College Dropout Scandal, David Kirp outlines the scale of the problem and shows that it's fixable - -we already have the tools to boost graduation rates and shrink the achievement gap. Many college administrators know what has to be done, but many of them are not doing the job - -the dropout rate hasn't decreased for decades. It's not elite schools like Harvard or Williams who are setting the example, but places like City University of New York and Long Beach State, which are doing the hard work to assure that more students have a better education and a diploma. As in his New York Times columns, Kirp relies on vivid, on-the-ground reporting, conversations with campus leaders, faculty and students, as well as cogent overviews of cutting-edge research to identify the institutional reforms--like using big data to quickly identify at-risk students and get them the support they need -- and the behavioral strategies -- from nudges to mindset changes - -that have been proven to work. Through engaging stories that shine a light on an underappreciated problem in colleges today, David Kirp's hopeful book will prompt colleges to make student success a top priority and push more students across the finish line, keeping their hopes of achieving the American Dream alive.
Book Synopsis Handbook of Statistical Analysis and Data Mining Applications by : Ken Yale
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
Book Synopsis Proceedings of the 3rd International Symposium of Information and Internet Technology (SYMINTECH 2018) by : Mohd Azlishah Othman
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.
Book Synopsis Understanding and Reducing College Student Departure by : John M. Braxton
Download or read book Understanding and Reducing College Student Departure written by John M. Braxton and published by John Wiley & Sons. This book was released on 2011-10-07 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: Student departure is a long-standing problem to colleges and universities. Approximately 45 percent of students enrolled in two-year colleges depart during their first year, and approximately one out of four students departs from a four-year college or university. The authors advance a serious revision of Tinto's popular interactionalist theory to account for student departure, and they postulate a theory of student departure in commuter colleges and universities. This volume delves into the literature to describe exemplary campus-based programs designed to reduce student departure. It emphasizes the importance of addressing student departure through a multidisciplinary approach, engaging the whole campus. It proposes new models for nonresidential students and students from diverse backgrounds, and suggests directions for further research. Academic and student affairs administrators seeking research-based approaches to understanding and reducing student departure will profit from reading this volume. Scholars of the college student experience will also find it valuable in defining new thrusts in research on the student departure process.
Book Synopsis Data Mining a Peoplesoft Database to Assist in Developing Student Retention Interventions by : Greg Alan Đào Jonason
Download or read book Data Mining a Peoplesoft Database to Assist in Developing Student Retention Interventions written by Greg Alan Đào Jonason and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Per a Bellwether Education Partners study (Aldeman, 2015, p. 8), "As of 2013, there were 29.1 million college dropouts versus 24.5 million Americans who dropped out with less than a high school diploma. In pure, raw numbers, college dropouts are a bigger problem than high school dropouts." Conceptually this study is framed within theories of student persistence/attainment and the Knowledge Discovery Process (KDP). This research study developed first time in college (FTIC) and transfer (TRAN) student graduation prediction models by using decision trees and support vector machine (SVM) classification algorithms and identified attributes of students who graduate and do not graduate. Data was collected from the University of Houston's data warehouse to provide detailed student academic records as the basis for quantitative analysis. The data set included male and female undergraduate students enrolled in the College of Education's Teaching & Learning Program from 2000-2012 at the University of Houston. These findings may contribute to improving student success and subsequent graduation rates in the College of Education and other colleges across the campus.
Book Synopsis Developing a Model to Explain IPEDS Graduation Rates at Minnesota Public Two-year Colleges and Four-year Universities Using Data Mining by : Brenda Arndt Bailey
Download or read book Developing a Model to Explain IPEDS Graduation Rates at Minnesota Public Two-year Colleges and Four-year Universities Using Data Mining written by Brenda Arndt Bailey and published by . This book was released on 2006 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Longitudinal Study of First-time Freshmen Using Data Mining by : Ashutosh R. Nandeshwar
Download or read book Longitudinal Study of First-time Freshmen Using Data Mining written by Ashutosh R. Nandeshwar and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In the modern world, higher education is transitioning from enrollment mode to recruitment mode. This shift paved the way for institutional research and policy making from historical data perspective. More and more universities in the U.S. are implementing and using enterprise resource planning (erp) systems, which collect vast amounts of data. Although few researchers have used data mining for performance, graduation rates, and persistence prediction, research is sparse in this area, and it lacks the rigorous development and evaluation of data mining models. The primary objective of this research was to build and analyze data mining models using historical data to find out patterns and rules that classified students who were likely to drop-out and students who were likely to persist. Student retention is a major problem for higher education institutions, and predictive models developed using traditional quantitative methods do not produce results with high accuracy, because of massive amounts of data, correlation between attributes, missing values, and non-linearity of variables; however, data mining techniques work well with these conditions. In this study, various data mining models were used along with discretization, feature subset selection, and cross-validation; the results were not only analyzed using the probability of detection and probability of false alarm, but were also analyzed using variances obtained in these performance measures. Attributes were grouped together based on the current hypotheses in the literature. Using the results of feature subset selectors and treatment learners, attributes that contributed the most toward a student's decision of dropping out or staying were found, and specific rules were found that characterized a successful student. The performance measures obtained in this study were significantly better than previously reported in the literature. [The dissertation citations contained here are published with the permission of ProQuest llc. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.].
Book Synopsis Data Mining in Action: Case Studies of Enrollment Management by : Jing Luan
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.
Book Synopsis Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges by : Aboul Ella Hassanien
Download or read book Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges written by Aboul Ella Hassanien and published by Springer Nature. This book was released on 2020-12-14 with total page 648 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data. It is a valuable resource for researchers in the area of big data analytics and its applications.
Book Synopsis Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019 by : Aboul Ella Hassanien
Download or read book Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019 written by Aboul Ella Hassanien and published by Springer Nature. This book was released on 2019-10-02 with total page 1093 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the proceedings of the 5th International Conference on Advanced Intelligent Systems and Informatics 2019 (AISI2019), which took place in Cairo, Egypt, from October 26 to 28, 2019. This international and interdisciplinary conference, which highlighted essential research and developments in the fields of informatics and intelligent systems, was organized by the Scientific Research Group in Egypt (SRGE). The book is divided into several sections, covering the following topics: machine learning and applications, swarm optimization and applications, robotic and control systems, sentiment analysis, e-learning and social media education, machine and deep learning algorithms, recognition and image processing, intelligent systems and applications, mobile computing and networking, cyber-physical systems and security, smart grids and renewable energy, and micro-grid and power systems.
Book Synopsis Data Mining and Learning Analytics by : Samira ElAtia
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.
Book Synopsis Internet of Things and Its Applications by : Keshav Dahal
Download or read book Internet of Things and Its Applications written by Keshav Dahal and published by Springer Nature. This book was released on 2022-02-18 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes selected papers presented at the International Conference on IoT and its Applications 2020. The research papers presented were carefully reviewed and selected from several initial submissions on the topics - the Internet of Things (IoT) and its applications such as smart cities, smart devices, agriculture, transportation and logistics, healthcare, etc. The book contains peer-reviewed chapters written by leading international scholars from around the world. This book will appeal to students, practitioners, industry professionals, and researchers working in the field of IoT and its integration with other technologies to develop comprehensive solutions to real-life problems.
Download or read book Data Mining written by Derya Birant and published by BoD – Books on Demand. This book was released on 2021-01-20 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining is a branch of computer science that is used to automatically extract meaningful, useful knowledge and previously unknown, hidden, interesting patterns from a large amount of data to support the decision-making process. This book presents recent theoretical and practical advances in the field of data mining. It discusses a number of data mining methods, including classification, clustering, and association rule mining. This book brings together many different successful data mining studies in various areas such as health, banking, education, software engineering, animal science, and the environment.
Book Synopsis Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky by : Andrew M. Olney
Download or read book Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky written by Andrew M. Olney and published by Springer Nature. This book was released on with total page 531 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Advances in Data Mining by : Petra Perner
Download or read book Advances in Data Mining written by Petra Perner and published by Springer Science & Business Media. This book was released on 2002-08-21 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents six thoroughly reviewed and revised full papers describing selected projects on data mining. Three papers deal with data mining and e-commerce, focusing on sequence rule analysis, association rule mining and knowledge discovery in databases, and intelligent e-marketing with Web mining. One paper is devoted to experience management and process learning. The last two papers report on medical applications, namely on genomic data processing and on case-based reasoning for prognosis of influenza.
Book Synopsis Proceedings of International Conference on Computational Intelligence, Data Science and Cloud Computing by : Valentina Emilia Balas
Download or read book Proceedings of International Conference on Computational Intelligence, Data Science and Cloud Computing written by Valentina Emilia Balas and published by Springer Nature. This book was released on 2021 with total page 795 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes selected papers presented at International Conference on Computational Intelligence, Data Science and Cloud Computing (IEM-ICDC) 2020, organized by the Department of Information Technology, Institute of Engineering & Management, Kolkata, India, during 25-27 September 2020. It presents substantial new research findings about AI and robotics, image processing and NLP, cloud computing and big data analytics as well as in cyber security, blockchain and IoT, and various allied fields. The book serves as a reference resource for researchers and practitioners in academia and industry.
