國立虎尾科技大學 |

From Data to Decisions : = Machine Learning in Higher Education.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	From Data to Decisions :/
其他題名:	Machine Learning in Higher Education.
作者:	Barnes, Emily J.
面頁冊數:	1 online resource (180 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
標題:	Higher education administration. -
電子資源:	click for full text (PQDT)
ISBN:	9798382372860

From Data to Decisions : = Machine Learning in Higher Education.
Barnes, Emily J.

From Data to Decisions :Machine Learning in Higher Education. - 1 online resource (180 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Thesis (Ph.D.)--Capitol Technology University, 2024.

Includes bibliographical references

This quantitative study investigates the predictive power of machine learning (ML) models on degree completion among adult learners in higher education, emphasizing the enhancement of data-driven decision-making (DDDM). By analyzing three ML models-Random Forest, Gradient-Boosting machine (GBM), and CART Decision Tree-within a not-for-profit, four-year institution in the Midwest, the research meticulously processes a dataset from 2013-14 to 2021-22. The analysis reveals the GBM model's superior accuracy, achieving up to 83.3 percent, thereby underscoring its effectiveness in identifying key success factors for adult learners. The findings highlight the significance of age, attendance, and Pell Grant eligibility as critical predictors of academic success, offering actionable insights for educational administrators. This study not only addresses a gap in educational research concerning adult learners but also suggests a strategic focus on these determinants to improve student success rates. By demonstrating the GBM model's predictive capabilities, the research contributes to the broader discourse on employing advanced analytical techniques to enhance educational outcomes, advocating for a more inclusive and data-informed approach to policy development and institutional practice in higher education.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382372860Subjects--Topical Terms:

1148709
Higher education administration.
Subjects--Index Terms:

Adult learnersIndex Terms--Genre/Form:

554714
Electronic books.

From Data to Decisions : = Machine Learning in Higher Education.
LDR:02718ntm a22004097 4500 001 1150497
005 20241028051505.5
006 m o d
007 cr bn ---uuuuu
008 250605s2024 xx obm 000 0 eng d
020 $a 9798382372860
035 $a (MiAaPQ)AAI31292637
035 $a AAI31292637
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Barnes, Emily J. $3 1476986
245 1 0 $a From Data to Decisions : $b Machine Learning in Higher Education.
264 0 $c 2024
300 $a 1 online resource (180 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
500 $a Advisor: Perry, Karriem.
502 $a Thesis (Ph.D.)--Capitol Technology University, 2024.
504 $a Includes bibliographical references
520 $a This quantitative study investigates the predictive power of machine learning (ML) models on degree completion among adult learners in higher education, emphasizing the enhancement of data-driven decision-making (DDDM). By analyzing three ML models-Random Forest, Gradient-Boosting machine (GBM), and CART Decision Tree-within a not-for-profit, four-year institution in the Midwest, the research meticulously processes a dataset from 2013-14 to 2021-22. The analysis reveals the GBM model's superior accuracy, achieving up to 83.3 percent, thereby underscoring its effectiveness in identifying key success factors for adult learners. The findings highlight the significance of age, attendance, and Pell Grant eligibility as critical predictors of academic success, offering actionable insights for educational administrators. This study not only addresses a gap in educational research concerning adult learners but also suggests a strategic focus on these determinants to improve student success rates. By demonstrating the GBM model's predictive capabilities, the research contributes to the broader discourse on employing advanced analytical techniques to enhance educational outcomes, advocating for a more inclusive and data-informed approach to policy development and institutional practice in higher education.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Higher education administration. $3 1148709
650 4 $a Higher education. $3 1148448
650 4 $a Computer science. $3 573171
653 $a Adult learners
653 $a Algorithms
653 $a Data-driven decision making
653 $a Gradient-Boosting machine
653 $a Machine learning
655 7 $a Electronic books. $2 local $3 554714
690 $a 0800
690 $a 0984
690 $a 0745
690 $a 0446
710 2 $a Capitol Technology University. $b Technology (PhD). $3 1467163
710 2 $a ProQuest Information and Learning Co. $3 1178819
773 0 $t Dissertations Abstracts International $g 85-11B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31292637 $z click for full text (PQDT)