Date of Award

Summer 8-24-2024

Document Type

Dissertation

Degree Name

Ph.D.

Organizational Unit

Morgridge College of Education, Research Methods and Information Science, Research Methods and Statistics

First Advisor

Yixiao Dong

Second Advisor

P. Bruce Uhrmacher

Third Advisor

Bobbie Kite

Fourth Advisor

Tianjie Deng

Copyright Statement / License for Reuse

All Rights Reserved
All Rights Reserved.

Keywords

Higher education, Hierarchical linear model (HLM), Mixed effects random forest (MERF), Model evaluation, Online satisfaction, Random forest (RF)

Abstract

One of the many impacts of the COVID-19 pandemic has been the increasing prevalence and accessibility of online education. This trend has also introduced challenges for students, instructors, and institutions. This study examines factors affecting online course satisfaction, focusing on individual, instructor, and institutional level characteristics with clustered, separated train and test datasets across two terms. This study compares Hierarchical Linear Model (HLM), Non-clustered and Clustered Random Forest (RF & MERF) models to understand these impacts. This intention is to provide a comprehensive framework comparing traditional HLM with the latest developed MERF models while delving into the effectiveness of RF and MERF models in predicting student satisfaction across different programs. This study investigates how MERF can be applied to analyzing real clustered higher education data to bridge the knowledge gap when evaluating different predictive models. The author encourages researchers to adopt an integrated approach combining HLM, RF, and MERF models with a suitable clustered dataset, as each model holds a unique niche in terms of their predictive performance, model sensitivity, and computational efficiency.

Copyright Date

8-2024

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Rights Holder

Jiaqi (Jackie) Shi

Provenance

Received from Author

File Format

application/pdf

Language

English (eng)

Extent

100 pgs

File Size

1.0 MB

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