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An Analysis of Online Course Sizes a...
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Aylward, Bryan T.
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An Analysis of Online Course Sizes as It Relates to Student Success and Faculty Performance through Hierarchical Regression.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
An Analysis of Online Course Sizes as It Relates to Student Success and Faculty Performance through Hierarchical Regression./
作者:
Aylward, Bryan T.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:
121 p.
附註:
Source: Dissertation Abstracts International, Volume: 79-07(E), Section: A.
Contained By:
Dissertation Abstracts International79-07A(E).
標題:
Higher education administration. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10745156
ISBN:
9780355627435
An Analysis of Online Course Sizes as It Relates to Student Success and Faculty Performance through Hierarchical Regression.
Aylward, Bryan T.
An Analysis of Online Course Sizes as It Relates to Student Success and Faculty Performance through Hierarchical Regression.
- Ann Arbor : ProQuest Dissertations & Theses, 2018 - 121 p.
Source: Dissertation Abstracts International, Volume: 79-07(E), Section: A.
Thesis (Psy.D.)--The University of the Rockies, 2018.
Online distance education program has continued to change the educational landscape in higher education. There has been extensive research on the relationship between course size, faculty performance, and student success within traditional on-ground colleges and universities, but limited research as it related to online educational modalities. The present quantitative correlational study addresses the relationship between course size and student success and faculty performance, as well as the extent to which course size and faculty performance could be utilized to predict students' success. Archival data were used from an undergraduate population at a large, for-profit, primarily online university (population of n = 406,476 & n = 146,267). Correlation, multiple regression, and hierarchical regression analyses were utilized to examine the relationships between course size, student success, and faculty performance. Analysis of the data revealed statistically significant relationships between variables; although the relationships were weak, potentially due to the population being large enough to make a smaller effect significant. The adjusted R-Square value of 0.005 supported a 0.5% variation in student success (letter grade), which could be explained by the predictor variable of group size. Additionally, the adjusted R-Square reported a value of 0.001, which supported a 0.1% variation in the EOCS score, which could be explained by the predictor variable of group size. Through hierarchical regression analysis, the adjusted R-Square value of 0.028 supported a 2.8% variation in student success, which could be explained by the predictor variables of course size and student success as transferable to the population of 406,476 students.
ISBN: 9780355627435Subjects--Topical Terms:
2122863
Higher education administration.
An Analysis of Online Course Sizes as It Relates to Student Success and Faculty Performance through Hierarchical Regression.
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Online distance education program has continued to change the educational landscape in higher education. There has been extensive research on the relationship between course size, faculty performance, and student success within traditional on-ground colleges and universities, but limited research as it related to online educational modalities. The present quantitative correlational study addresses the relationship between course size and student success and faculty performance, as well as the extent to which course size and faculty performance could be utilized to predict students' success. Archival data were used from an undergraduate population at a large, for-profit, primarily online university (population of n = 406,476 & n = 146,267). Correlation, multiple regression, and hierarchical regression analyses were utilized to examine the relationships between course size, student success, and faculty performance. Analysis of the data revealed statistically significant relationships between variables; although the relationships were weak, potentially due to the population being large enough to make a smaller effect significant. The adjusted R-Square value of 0.005 supported a 0.5% variation in student success (letter grade), which could be explained by the predictor variable of group size. Additionally, the adjusted R-Square reported a value of 0.001, which supported a 0.1% variation in the EOCS score, which could be explained by the predictor variable of group size. Through hierarchical regression analysis, the adjusted R-Square value of 0.028 supported a 2.8% variation in student success, which could be explained by the predictor variables of course size and student success as transferable to the population of 406,476 students.
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