Evaluation of a Learning Analytics Framework’s impact on online course design

Wu, Di

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https://hdl.handle.net/2142/129455 Copy

Description

Title

Evaluation of a Learning Analytics Framework’s impact on online course design

Author(s)

Wu, Di

Issue Date

2025-04-30

Director of Research (if dissertation) or Advisor (if thesis)

Kalantzis, Mary

Doctoral Committee Chair(s)

Cope, William

Committee Member(s)

• Magee, Liam
• You, Yu-ling

Department of Study

Educ Policy, Orgzn & Leadrshp

Discipline

Educ Policy, Orgzn & Leadrshp

Degree Granting Institution

University of Illinois Urbana-Champaign

Degree Name

Ed.D.

Degree Level

Dissertation

Keyword(s)

• Learning analytics
• online learning
• course design
• learner engagement
• interpersonal interactions
• Community of Inquiry Framework
• Bi-directional LA-Course design Framework
• Higher Education
• Community College

Language

eng

Abstract

This dissertation explores how the Bi-directional Learning Analytics–Course Design Framework (BLDF) supports the design of interpersonal interaction in asynchronous online courses at U.S. community colleges. Grounded in the Bi-directional LA–Course Design Framework and the Community of Inquiry (CoI) framework, the study employs a qualitative multi-site case study method to examine how instructors and students experience the application of learning analytics in online course design. Data was collected through interviews with four instructors and focus groups with their students across three community colleges. Thematic analysis revealed that instructors perceived the BLDF as aligned with their pedagogical goals, especially in enhancing teaching presence and facilitating student engagement. The framework encouraged intentional communication and course adaptations focused on interaction. Students valued instructor responsiveness and the sense of connection it fostered in their learning environments. However, limitations in analytics tools, unclear framework guidance, and a lack of institutional support emerged as barriers to sustained implementation. Instructors needed structured training, learning analytics resources, and detailed guides to make the framework scalable and sustainable across settings. This study contributes to the growing field of learning analytics by offering a new learning analytics conceptual model called the ELEVATE framework and implementation guide. It provides a toolkit and insights into using learning analytics to inform data-driven learning design and improve interpersonal interaction in online learning.

Graduation Semester

2025-05

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/129455

Copyright and License Information

Copyright 2025 Di Wu

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