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Author: Ashutosh R. Nandeshwar Publisher: ISBN: Category : College freshmen Languages : en Pages :
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: Ashutosh R. Nandeshwar Publisher: ISBN: Category : College freshmen Languages : en Pages :
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: Burley, Hansel Publisher: IGI Global ISBN: 1609608585 Category : Education Languages : en Pages : 429
Book Description
Institutional research (IR) is a growing, applied, and interdisciplinary area that attracts people from a variety of fields, including computer programmers, statisticians, and administrators and faculty from every discipline to work in archiving, analyzing, and reporting on all aspects of higher education information systems. Cases on Institutional Research Systems is a reference book for institutional research, appealing to novice and expert IR professionals and the administrators and policymakers that rely on their data. By presenting a variety of institutional perspectives, the book depicts the challenges and solutions to those in higher education administration, and state, federal, and even international accreditation.
Author: William Laurance King III Publisher: ISBN: Category : Languages : en Pages :
Book Description
The lack of student persistence is a burgeoning issue and over the last 40 years has become a national concern among researchers, administrators, policymakers and practitioners. Given the low persistence rates of first-year students at America's community colleges, leaders are searching for useful and successful strategies that will aid in closing the gap in student attrition. Successful completion of a degree or certificate is often considered the great economic equalizer in today's society from a public and cultural perspective. The purpose of this research study was to empirically investigate the odds ratio associated with predicting persistence that exists between first-time freshmen students who lived in campus housing and those who live off-campus at a large-city community college referred to as LCCC. Specifically, the focus of this study was to determine whether living in on-campus housing, receiving needs-based federal financial aid (Pell Grant), ethnicity, gender and enrolling in one or more developmental education courses are predictors of persistence. This study was predicated on the collection of quantitative data from a large-city community college's student information system from the years 2010 through 2013. The researcher has concluded based on the data analysis of this research study the results were statistically insignificant for those students living on-campus when compared to those students living off-campus. An analysis of Ethnicity as a predictor of persistence revealed that in the short-term African-American students actually persisted at higher rates than their counterparts. However, in three of the last four semesters analyzed, African-Americans persisted at significantly lower rates than White students. Lastly, an analysis of the students who were enrolled in Developmental Education (Remedial) courses suggested that the odds are significantly lower concerning persistence versus their counterparts. However, it must be noted that both Hispanic students and those receiving needs-based financial aid (Pell) attrition was no worse than their counterparts. Based on the complex nature of both the community college student and the unique opportunity for them to live on-campus, additional data is required in order to measure and evaluate whether housing status promotes improved academic persistence. The reported research studies pertaining to community colleges and living on-campus are meager at best. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/155442
Author: Jennifer Wine Publisher: ISBN: Category : Languages : en Pages : 139
Book Description
This report describes and evaluates the methods and procedures used in the 2004/09 Beginning Postsecondary Students Longitudinal Study (BPS:04/09). BPS:04/09 is the second and final follow-up interview for the cohort of first-time beginning postsecondary students identified in the 2004 National Postsecondary Student Aid Study. For the first time in BPS, in addition to the student interview, transcripts were collected from all of the postsecondary institutions attended by the sample. Together, the student interview and transcript data collections represent a significant and rich data source on this cohort of first-time beginning students. Appended are: (1) NPSAS:04 Institution and Student Sampling Details; (2) Technical Review Panel; (3) Data Elements for Student Interview; (4) Facsimile of Full-scale Instrument; (5) Training Material for Interviewers; (6) Notification Materials for Student Interview Data Collection; (7) Training Agendas for Transcript Data Collection; (8) Notification Materials for Transcript Data Collection; (9) Data Elements for Keying and Coding System; (10) Item Response Rates and Imputation Results; (11) Analysis Variables; (12) Design Effects; and (13) Nonresponse Bias Analysis.
Author: Hady W. Lauw Publisher: Springer Nature ISBN: 3030474267 Category : Computers Languages : en Pages : 906
Book Description
The two-volume set LNAI 12084 and 12085 constitutes the thoroughly refereed proceedings of the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020, which was due to be held in Singapore, in May 2020. The conference was held virtually due to the COVID-19 pandemic. The 135 full papers presented were carefully reviewed and selected from 628 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications. They are organized in the following topical sections: recommender systems; classification; clustering; mining social networks; representation learning and embedding; mining behavioral data; deep learning; feature extraction and selection; human, domain, organizational and social factors in data mining; mining sequential data; mining imbalanced data; association; privacy and security; supervised learning; novel algorithms; mining multi-media/multi-dimensional data; application; mining graph and network data; anomaly detection and analytics; mining spatial, temporal, unstructured and semi-structured data; sentiment analysis; statistical/graphical model; multi-source/distributed/parallel/cloud computing.
Author: Jennifer R. Keup Publisher: First-Year Experience and Students in Transition University of South Carolina ISBN: Category : Education Languages : en Pages : 104
Author: Ashutosh R. Nandeshwar
Author: Ashutosh R. Nandeshwar
Author: 
Author: Miriam A. Gerber Meyer
Author: Burley, Hansel
Author: William Laurance King III
Author: Jennifer Wine
Author: Hady W. Lauw
Author: Jennifer R. Keup
Author: LaKendra Miranda Peoples-McAfee
Author: Mary Tijerina