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Author: Dheeraj A. Raju
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 207
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Book Description
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
Author: Dheeraj A. Raju
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 207
Get Book Here
Book Description
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
Author: David Kirp
Publisher: Oxford University Press
ISBN: 019086222X
Category : Education
Languages : en
Pages : 256
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Book Description
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.
Author: Ken Yale
Publisher: Elsevier
ISBN: 0124166458
Category : Mathematics
Languages : en
Pages : 824
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Book Description
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
Author: Mohd Azlishah Othman
Publisher: Springer
ISBN: 303020717X
Category : Technology & Engineering
Languages : en
Pages : 99
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Book Description
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.
Author: John M. Braxton
Publisher: John Wiley & Sons
ISBN: 111821661X
Category : Education
Languages : en
Pages : 116
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Book Description
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.
Author: Greg Alan Đào Jonason
Publisher:
ISBN:
Category : Curriculum and Instruction
Languages : en
Pages :
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Book Description
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.
Author: Brenda Arndt Bailey
Publisher:
ISBN:
Category :
Languages : en
Pages : 470
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Book Description
Author: Ashutosh R. Nandeshwar
Publisher:
ISBN:
Category : College freshmen
Languages : en
Pages :
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Book Description
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.].
Author: Derya Birant
Publisher: BoD – Books on Demand
ISBN: 183968318X
Category : Computers
Languages : en
Pages : 214
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Book Description
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.
Author: Jing Luan
Publisher: Jossey-Bass
ISBN:
Category : Education
Languages : en
Pages : 144
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Book Description
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.
