Award Date

December 2022

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Educational Psychology, Leadership, and Higher Education

First Committee Member

Lisa Bendixen

Second Committee Member

Michael Nussbaum

Third Committee Member

Gwen Marchand

Fourth Committee Member

Kendall Hartley

Number of Pages

109

Abstract

Self-regulated learning (SRL) is crucial to student success in online learning environments. Students enrolled in online courses at higher education institutions access courses through the learning management system (LMS) to complete coursework, interact with instructors and peers using various learning management system tools and multiple technological devices. Researchers frequently rely on surveys to identify how students self-regulate, but students often are not honest or over or under-estimate their SRL skills. Learning management system data analytics have recently provided opportunities to examine the frequencies and sequences of how learnings engage with tools and content in the online learning environment. The purpose of this study is to investigate which device and learning management system tools play a specific role in fostering and analyzing students self-reported SRL amongst demographic groups.The study utilized a non-intervention descriptive research design. Results identified multiple device usage when students accessed the learning management system. Utilizing students learning management system data logs, the study examined means differences among demographic groups (gender, race, course enrollment, age, and GPA) and learning management system tool usage. Means differences for learning management system tool usage and demographic groups were found no to be large. To assess students’ self-reported SRL, students completed the Online Self-Regulated Learning Questionnaire (OSLQ) (Barnard, Lan, To, Paton & Lai, 2009). Low-performing students (GPAs 2.99 and below) reported SRL scores on par or slightly higher than their higher-performing peers (3.0 to 3.49, and 3.5 to 5.0) Differences between lower and higher-performing students also appeared with learning management system tool usage. Low-performing students utilized LMS tools less frequently than their higher-performing classmates. These finding support SRL and data analytics literature in that higher and lower performing students report SRL levels that may not be in alignment with learning management system tool and device usage. Instructing students on how to become effective in SRL, and which tools and devices are most effective to support SRL is important to improving student success in the online learning environment.

Keywords

learning management systems; mobile learning; self-regulated learning

Disciplines

Education | Educational Psychology

File Format

pdf

Degree Grantor

University of Nevada, Las Vegas

Language

English

Rights

IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/

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