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Author ORCID Identifier


Open Access Dissertation

Document Type


Degree Name

Doctor of Philosophy (PhD)

Degree Program


Year Degree Awarded


Month Degree Awarded


First Advisor

Ryan S. Wells

Subject Categories

Higher Education | Scholarship of Teaching and Learning


The idea that offering multiple means of representing course content will assist students of all abilities constitutes one pillar of Universal Design for Learning (UDL), a framework intended to address needs of students with disabilities while also holding relevance for all students. The efficacy of this UDL guideline lacks a verified empirical basis and therefore merits rigorous examination. My dissertation investigates the effect on learning outcomes of students using multiple modalities while learning course content (e.g., text, video, audio, interactive, or mixed content), targeting improving educational success for non-traditional online students.

I investigate this effect for older undergraduates from a women’s institution who are predominantly low income and working mothers returning to school, many of whom are racial/ethnic minorities. Notably, challenges resulting from a lack of disability diagnosis and accommodation may be prevalent but hidden among these students. Traditional higher education typically does not serve such students well. Use of multiple modalities in class activities holds potential for improving their outcomes.

Results show positive effects of using multiple modalities for learning content in courses across the curriculum presented in an adaptive learning system. Using a within-subjects study design, I found a medium-large positive effect size for knowledge gained across adaptive activities. Using an instrumental variables approach, I found a very large positive effect size for weekly assignment and quiz grades, and results suggest a large positive effect on course grade as well. I illustrate how combining knowledge of this effect with other information from the adaptive learning system and online tutoring in a Bayesian network analysis can predict where students may benefit from tutoring. This can inform potential support recommendations that would be particularly relevant when implementation of UDL-based design does not yet fully address students’ learning needs.

These results provide the first evidence confirming an effect of UDL’s multiple modalities guideline on collegiate learning outcomes and illustrate how this information could be used to provide recommendations to students using a learning analytics perspective. Results have implications for researchers, faculty, course developers, instructional designers, analytics professionals, and institutions aiming to improve learning outcomes through a design-based approach.