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From Data to Decisions: Machine Lear...
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Barnes, Emily J.
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From Data to Decisions: Machine Learning in Higher Education.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
From Data to Decisions: Machine Learning in Higher Education./
作者:
Barnes, Emily J.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2024,
面頁冊數:
180 p.
附註:
Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:
Dissertations Abstracts International85-11B.
標題:
Computer science. -
電子資源:
https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31292637
ISBN:
9798382372860
From Data to Decisions: Machine Learning in Higher Education.
Barnes, Emily J.
From Data to Decisions: Machine Learning in Higher Education.
- Ann Arbor : ProQuest Dissertations & Theses, 2024 - 180 p.
Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Thesis (Ph.D.)--Capitol Technology University, 2024.
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.
ISBN: 9798382372860Subjects--Topical Terms:
523869
Computer science.
Subjects--Index Terms:
Adult learners
From Data to Decisions: Machine Learning in Higher Education.
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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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