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Predictive Relationships Between Lea...

Saknee, Ayad.

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Predictive Relationships Between Learning Management System Engagement Measures and Academic Performance in College.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Predictive Relationships Between Learning Management System Engagement Measures and Academic Performance in College./
作者:	Saknee, Ayad.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
面頁冊數:	197 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-12, Section: A.
Contained By:	Dissertations Abstracts International85-12A.
標題:	Adult education. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31331558
ISBN:	9798383058565

Predictive Relationships Between Learning Management System Engagement Measures and Academic Performance in College.
Saknee, Ayad.

Predictive Relationships Between Learning Management System Engagement Measures and Academic Performance in College. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 197 p.

Source: Dissertations Abstracts International, Volume: 85-12, Section: A.

Thesis (D.B.A.)--Grand Canyon University, 2024.

Higher education institutes experience lower success rates in online learning environments compared to traditional learning. Students' engagement within the learning management system (LMS) is one of the main factors affecting students' academic performance and retention. This quantitative correlational-predictive study examined if, and to what extent students' engagement frequency (SEF) and students' engagement duration (SED) collectively and/or individually predicted students' academic performance (SAP) in business courses at one community college in Arizona. The theoretical framework for this study included engagement theory and a model of student engagement, prior knowledge, and academic performance. The research questions asked to what extent the number of times a student logged into the LMS and the time student spent logged into the LMS during the course on the learning management system predicted their academic performance in multiple business courses. A multiple linear regression was performed on deidentified archival data from a sample of 80 community college students who had enrolled in multiple business classes in the Fall of 2023. The dataset included the total number of student logins, total time each student spent on the entire course, and student final grade. The results showed a nonsignificant predictive relationship between the two predictors considered together and SAP, R2 = .067, adj. R2 = .043, F(2, 77) = 2.762, p = .069, Cohen's effect size f2 = 0.0718 (midway between small and medium). The SEF was the only individually significant predictor in the model (std. β = .258, t =2.280, p = .025), which could be enhanced through the course requirements structure and LMS tracking to improve learning.

ISBN: 9798383058565Subjects--Topical Terms:

543202
Adult education.
Subjects--Index Terms:

Learning management system

Predictive Relationships Between Learning Management System Engagement Measures and Academic Performance in College.
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