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

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	From Data to Decisions :/
Reminder of title:	Machine Learning in Higher Education.
Author:	Barnes, Emily J.
Description:	1 online resource (180 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
Subject:	Computer science. -
Online resource:	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:

573171
Computer science.
Subjects--Index Terms:

Adult learnersIndex Terms--Genre/Form:

554714
Electronic books.

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