Using Data Mining to Model Student Success

Using Data Mining to Model Student Success

Bibliographic Details
Main Author: Geltz, Rebecca L.
Language:English
Published: Youngstown State University / OhioLINK 2009
Subjects:
Artificial Intelligence
Computer Science
Information Systems
Operations Research
Statistics
Technology
data mining and decision trees
predicting student success
graduation rates
institutional research
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=ysu1264697709
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-ysu12646977092021-08-03T06:17:54Z Using Data Mining to Model Student Success Geltz, Rebecca L. Artificial Intelligence Computer Science Information Systems Operations Research Statistics Technology data mining and decision trees predicting student success graduation rates institutional research As funding for higher education through federal and state sources continues to decline, and a stronger call for accountability is placed upon higher education institutionsto 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 eachindividual 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. 2009 English text Youngstown State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ysu1264697709 http://rave.ohiolink.edu/etdc/view?acc_num=ysu1264697709 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Artificial Intelligence
Computer Science
Information Systems
Operations Research
Statistics
Technology
data mining and decision trees
predicting student success
graduation rates
institutional research
spellingShingle Artificial Intelligence
Computer Science
Information Systems
Operations Research
Statistics
Technology
data mining and decision trees
predicting student success
graduation rates
institutional research
Geltz, Rebecca L.
Using Data Mining to Model Student Success
author Geltz, Rebecca L.
author_facet Geltz, Rebecca L.
author_sort Geltz, Rebecca L.
title Using Data Mining to Model Student Success
title_short Using Data Mining to Model Student Success
title_full Using Data Mining to Model Student Success
title_fullStr Using Data Mining to Model Student Success
title_full_unstemmed Using Data Mining to Model Student Success
title_sort using data mining to model student success
publisher Youngstown State University / OhioLINK
publishDate 2009
url http://rave.ohiolink.edu/etdc/view?acc_num=ysu1264697709
work_keys_str_mv AT geltzrebeccal usingdataminingtomodelstudentsuccess
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