東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Machine Learning Models of Participation in Work-Related Training.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning Models of Participation in Work-Related Training./
Author:	Zigner, Michael.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	275 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-06, Section: B.
Contained By:	Dissertations Abstracts International83-06B.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28929414
ISBN:	9798494498687

Machine Learning Models of Participation in Work-Related Training.
Zigner, Michael.

Machine Learning Models of Participation in Work-Related Training. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 275 p.

Source: Dissertations Abstracts International, Volume: 83-06, Section: B.

Thesis (Ph.D.)--The Pennsylvania State University, 2021.

Predictive modeling has become so ubiquitous that it now impacts our daily lives. These models can be seen at work in determining the ads we see on social media, controlling our e-mail spam, and preparing us for extreme weather events. The predictions from these types of analyses have been shown to be powerful and valuable, and are rapidly being adopted for new and innovative purposes as the technology becomes available to implement them. Many academic disciplines now use predictive models as part of their regular research practices; and the number of academic articles that use this methodology continue to increase yearly.However, the adoption of this innovation has yet to happen in workforce development-related fields. This is unfortunate because by not adopting an analytical mindset and building technological skills, practitioners face being left behind and marginalized within their organizations. The choice to understand and utilize innovations like predictive modeling is rapidly becoming a necessity rather than a luxury for professionals working in the field. An underlying purpose to this study, therefore, is to foster the continued adoption of advanced analytics like predictive modeling among workforce development researchers and practitioners.By demonstrating the building and evaluation of predictive models using a topic germane to the field (i.e., work-related training), this study helps provide the how-to and principles knowledge needed to spur adoption (Rogers, 1995), and provide confirmation of its value by subject-matter experts. This study used a large, publicly available data set (the National Survey of College Graduates; n = 275,598) and more than 150 variables to build and evaluate a selection of predictive models that can accurately identify future participants in work-related training.This document walks the reader through the predictive modeling process. It explains key concepts and principles that help build a foundational understanding of the strengths and weaknesses of the different models that are necessary for an effective evaluation of a model's predictions. Most importantly, by providing a detailed demonstration of the predictive modeling process on a relevant topic, this dissertation provides a familiar context that helps the practitioner understand how this new innovation applies to their profession.

ISBN: 9798494498687Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Predictive modeling

Machine Learning Models of Participation in Work-Related Training.
LDR:03426nmm a2200349 4500 001 2352176
005 20221118093833.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798494498687
035 $a (MiAaPQ)AAI28929414
035 $a AAI28929414
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zigner, Michael. $3 3691801
245 1 0 $a Machine Learning Models of Participation in Work-Related Training.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 275 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-06, Section: B.
500 $a Advisor: Passmore, David.
502 $a Thesis (Ph.D.)--The Pennsylvania State University, 2021.
520 $a Predictive modeling has become so ubiquitous that it now impacts our daily lives. These models can be seen at work in determining the ads we see on social media, controlling our e-mail spam, and preparing us for extreme weather events. The predictions from these types of analyses have been shown to be powerful and valuable, and are rapidly being adopted for new and innovative purposes as the technology becomes available to implement them. Many academic disciplines now use predictive models as part of their regular research practices; and the number of academic articles that use this methodology continue to increase yearly.However, the adoption of this innovation has yet to happen in workforce development-related fields. This is unfortunate because by not adopting an analytical mindset and building technological skills, practitioners face being left behind and marginalized within their organizations. The choice to understand and utilize innovations like predictive modeling is rapidly becoming a necessity rather than a luxury for professionals working in the field. An underlying purpose to this study, therefore, is to foster the continued adoption of advanced analytics like predictive modeling among workforce development researchers and practitioners.By demonstrating the building and evaluation of predictive models using a topic germane to the field (i.e., work-related training), this study helps provide the how-to and principles knowledge needed to spur adoption (Rogers, 1995), and provide confirmation of its value by subject-matter experts. This study used a large, publicly available data set (the National Survey of College Graduates; n = 275,598) and more than 150 variables to build and evaluate a selection of predictive models that can accurately identify future participants in work-related training.This document walks the reader through the predictive modeling process. It explains key concepts and principles that help build a foundational understanding of the strengths and weaknesses of the different models that are necessary for an effective evaluation of a model's predictions. Most importantly, by providing a detailed demonstration of the predictive modeling process on a relevant topic, this dissertation provides a familiar context that helps the practitioner understand how this new innovation applies to their profession.
590 $a School code: 0176.
650 4 $a Computer science. $3 523869
650 4 $a Artificial intelligence. $3 516317
650 4 $a Adult education. $3 543202
650 4 $a Information science. $3 554358
653 $a Predictive modeling
653 $a Workforce development
653 $a Advanced analytics
690 $a 0984
690 $a 0723
690 $a 0800
690 $a 0516
710 2 $a The Pennsylvania State University. $3 699896
773 0 $t Dissertations Abstracts International $g 83-06B.
790 $a 0176
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28929414