Universal Design for Learning as an Equity Framework: Addressing Educational Barriers and Enablers for Diverse Non-Traditional Learners

Abstract

Non-traditional learners comprise approximately 73% of undergraduate enrollment, representing diverse populations including first-generation college students, adult learners, veterans, multilingual learners, and students with family responsibilities. Despite their numerical dominance, these students face systemic barriers that traditional pedagogical approaches often fail to address. This mixed-methods study examined how Universal Design for Learning (UDL) principles impact non-traditional learners’ educational experiences in higher education. Using a convergent parallel design with 154 participants from a Hispanic-serving institution, the study collected quantitative data through the validated Personalized Learning Supporting Instrument (PLSI) and qualitative data from open-ended questions. The refined 12-item PLSI demonstrated strong psychometric properties. While UDL factors showed limited direct association with GPA overall, Flexible Instructional Methods and Materials significantly predicted academic performance. Qualitative analysis identified six barrier themes (online learning difficulties, course content issues, financial constraints, balancing responsibilities, accessibility challenges, and health interruptions) and five positive impact themes (interactive learning, supportive environments, skill development, goal clarification, and effective assignments). Demographic analysis revealed counterintuitive patterns—students with traditional “barriers” achieved high GPAs at rates of 73–76%, while first-generation students showed the lowest high GPA rate (53.2%). These findings challenge deficit-based assumptions about non-traditional learners while revealing important equity gaps. This study demonstrates both the promise and limitations of UDL for diverse populations, suggesting institutions need comprehensive approaches with differentiated support strategies.

1. Introduction

The landscape of higher education has undergone dramatic transformation over the past two decades, with non-traditional learners now comprising approximately 73% of undergraduate enrollment according to the U.S. Department of Education (Tandet, 2024). These students, who deviate from the traditional 18–22 age demographic attending college immediately after high school, face unique barriers that traditional pedagogical approaches often fail to address. Despite growing recognition of their diverse needs, there remains a critical gap in understanding how Universal Design for Learning (UDL) principles can systematically support their academic success.

Therefore, higher education institutions have had to shift their plans to suit this new and diversifying student body. In most cases, non-traditional learners balance work, family, and school, which is a challenge and, at the same time, a chance for colleges and universities to enhance participation, retention, and graduation rates. The growing reliance on online and distance learning, as well as the innovations in financial aid, student support, and PLA, are evidence of the understanding of the non-traditional learner as the future of higher education (Allen & Seaman, 2017; Johnson et al., 2019).

If educators recognize the strengths of non-traditional learners and remove the barriers to their learning, then the student experience of higher education can be positive and productive. However, this can only be achieved if there is a sustained effort by educators, administrators, and policymakers to engage institutions of higher education and, most importantly, the non-traditional learners themselves to identify what works for them in terms of academic adjustment and success and what should be retained or used in the future.

The aim of this study is to identify who non-traditional students are in post-secondary education today, how they differ from traditional students, and to explore the lived experiences of non-traditional learners in post-secondary institutions, to identify the perceived enablers and barriers they encounter, with a focus on how UDL methods, programs, and strategies impact their educational experiences. The goal is to provide data-driven insights and practical recommendations for institutions to enhance learner engagement and success. This study also looks at ways that higher education institutions have addressed these learners through online learning, flexible timing of course delivery, financial aid, and student support services. Moreover, this paper aims to describe how these strategies have developed to address the changing needs of non-traditional learners and why they have been successful in some instances and not in others.

This research directly addresses the Special Issue’s focus on innovative approaches for inclusion, diversity, and personalized learning by examining how Universal Design for Learning principles can create more equitable educational experiences for non-traditional learners—a population representing 73% of undergraduate enrollment yet often marginalized by traditional pedagogical approaches. The study’s emphasis on flexible, culturally responsive pedagogy and its examination of diverse demographic groups (including multilingual learners, first-generation students, veterans, and adult learners) contributes to understanding how educational institutions can innovate to serve increasingly diverse student populations.

Defining the Non-Traditional Learner

Non-traditional aged learners have in the past been defined by their age, that is, students who are older than 24 or through other characteristics that distinguish them from traditional college students. The National Center for Education Statistics (NCES) defined non-traditional students as financially independent, full-time working students while enrolled in school, part-time students, and students with family responsibilities (NCES, 2019). Choy (2002) identified several characteristics of non-traditional students, which included: delayed entry into postsecondary institutions, part-time enrollment, financial independence, and dependent care responsibilities.

In the meantime, the concept of non-traditional learners has expanded to include almost all students except for those who fit the traditional student description. This includes adult learners who are coming back to education after a long break, veterans, students of color, first-generation college students, and people trying to change careers or gain new skills because of changes in the labor market (Kasworm, 2010; Radford et al., 2010).

The imperative to serve non-traditional learners extends beyond enrollment numbers to questions of educational equity and social justice. These students often represent first-generation college attendees, racial and ethnic minorities, and individuals from lower socioeconomic backgrounds who have historically faced systemic barriers to higher education access and success (Radford et al., 2010). Their academic outcomes directly impact broader societal goals of economic mobility, workforce development, and democratic participation, making their educational success a matter of public policy significance rather than merely institutional concern.

The most important shift in the demographic profile of non-traditional learners over the past 20 years is the increased participation of women in higher education. Women are now the predominant population of adult learners in postsecondary institutions, and they usually enroll in school to enhance their employment opportunities and support their families (Johnson et al., 2019). Moreover, the development of online education makes it possible for a variety of non-traditional learners, including those in rural areas and those who cannot attend traditional campus-based programs because of work or family obligations, to access higher education (Allen & Seaman, 2017).

The increase in the number of non-traditional learners is also a result of changing social relationships. Because of technological advancement and changes in the economic structure, many employees have been obliged to go back to school to improve their skills or change their profession in order to stay employable (Carnevale et al., 2018). This has led to increasing demand for more flexible and more accessible educational choices that can help students combine learning with other responsibilities.

While non-traditional learners were once primarily defined by age (typically older than 24), the definition has broadened significantly. Today’s non-traditional learners may include students from a variety of backgrounds who do not follow the traditional path of attending college immediately after high school. This expanded definition includes students who are financially independent, attending part-time, working full-time, or caring for dependents (Choy, 2002). Additionally, many non-traditional learners are first-generation college students, racial and ethnic minorities, or veterans transitioning to civilian life (Radford et al., 2010). For the context of this study, the term non-traditional learner will include learners that fall into one or more of the following demographics: first-generation college student; aged 25 years or older; financially independent college student; multi-lingual learner (MLL); English-language learner (ELL); international student; military veteran; return to school for career change; commuter student (greater than 10 miles from campus); have dependents (e.g., children or parents you care for); part-time student (less than 6 credit hours); delayed enrollment (did not attend college directly after high school); hybrid or asynchronous coursework.

Each of the aforementioned characteristics provides unique positionalities for students that make them non-traditional compared to their traditional counterparts. This can be overlooked due to the perceived level of impact these factors may or may not have on a student’s post-secondary pursuits, as well as educators’ and administrators’ lack of knowledge of this growing demographic in post-secondary education.

2. Literature Review

2.1. Evolving Demographics and Institutional Responses

The demographic profile and educational needs of non-traditional learners have evolved significantly over the past two decades, prompting corresponding changes in institutional delivery models. Non-traditional learners today are more likely to come from underrepresented racial and ethnic groups, particularly African American and Latinx communities, and from lower-income backgrounds (Radford et al., 2010). This diversification reflects broader demographic changes and efforts to increase access for historically marginalized groups.

Economic factors have increasingly driven adults to return to school, particularly following the 2008 financial crisis and the rise of the gig economy (Carnevale et al., 2018). These motivational shifts from personal growth to economic necessity have coincided with technological innovations that make education more accessible to diverse populations.

2.2. Flexible Learning Modalities

Institutions have responded to this demographic evolution through flexible course delivery models. Online education has emerged as a key tool, with nearly 33% of all college students taking at least one online course by 2017, many identifying as non-traditional learners (Allen & Seaman, 2017). Hybrid learning models combine online flexibility with face-to-face interaction, particularly benefiting students who value structure while needing schedule accommodation (Means et al., 2014).

Asynchronous learning has further enhanced flexibility by eliminating synchronous attendance requirements, allowing students to balance education with work and caregiving responsibilities (Lieberman, 2020). The COVID-19 pandemic accelerated these trends, forcing rapid adoption of remote learning formats that highlighted both opportunities and challenges in serving non-traditional populations virtually (Lederman, 2020).

2.3. Holistic Support Integration

Recognition that non-traditional learners face challenges beyond the classroom has led to comprehensive support services. Advising systems now address work–life balance, financial aid navigation, and career development alongside academic guidance (Donaldson & Townsend, 2007). Career services have expanded to include targeted counseling for career changers and industry transitions, while childcare programs and mental health services address the unique pressures faced by adult learners managing multiple roles (Klein-Collins, 2010).

Data analytics have become essential tools for identifying at-risk students early and providing targeted interventions, particularly important given non-traditional learners’ higher dropout risk due to competing life demands (Cuseo, 2018). These institutional adaptations reflect growing understanding that serving non-traditional learners requires systemic rather than merely pedagogical changes.

2.4. The Shift to Online Learning and Technological Innovation

The rapid growth of online education has transformed the higher education landscape, particularly for non-traditional learners. Online learning platforms such as Coursera, edX, and Khan Academy, as well as institution-based programs, have democratized access to education by removing geographic and scheduling constraints. This technological innovation has made it possible for non-traditional students to pursue degrees, certificates, and even micro-credentials while balancing work and family responsibilities (Means et al., 2014).

The COVID-19 pandemic further accelerated the shift to online learning, forcing many institutions to adopt remote learning formats almost overnight (Lederman, 2020). While the pandemic posed significant challenges to both institutions and students, it also highlighted the flexibility and accessibility of online education. For non-traditional learners, the expansion of online programs, asynchronous learning opportunities, and virtual student services has made higher education more attainable than ever before (Johnson et al., 2019).

2.5. Data-Driven Retention and Completion Strategies

In addition to embracing new technologies, institutions have increasingly focused on retention and completion strategies aimed at non-traditional learners. Data analytics have become an essential tool for identifying at-risk students early and providing targeted interventions. Many institutions now use predictive analytics to monitor student progress and engagement, enabling them to offer proactive support to students who may be struggling academically or facing external pressures that could impact their ability to complete their degree (Cuseo, 2018).

This shift toward data-driven strategies is partly driven by the increased scrutiny on student outcomes, as institutions face growing pressure to improve graduation rates and reduce student debt (Cross, 1981). Non-traditional learners, who often face significant financial and personal challenges, are at higher risk of dropping out, making retention strategies particularly important for this population. Institutions have responded by developing early warning systems, offering academic and personal support services, and providing financial counseling to help non-traditional learners stay on track (Radford et al., 2010).

2.6. Universal Design for Learning (UDL) Strategies

Universal Design for Learning (UDL) is a framework that is designed to improve teaching and learning for all people through an understanding of how people learn. UDL attempts to provide opportunities for all students to succeed by addressing the diverse ways they learn in adult education environments. The three main principles of UDL include methods of engagement, which are methods of presenting information, enabling action, and methods of idea expression that match well with adult learning theory principles, which take into consideration the diverse backgrounds and challenges that adult learners bring to the learning process. The target population in higher education includes students who are likely engaged in work and other responsibilities, such as family duties and activities outside the academic environment, which may slow down their learning process. Using the UDL strategy, these challenges can be alleviated to some extent to make the learning environment more convenient for adult learners.

The principle of UDL is to provide multiple ways through which learners can gain information and knowledge. This is particularly significant for adult learners because they may have preferred learning styles that are dependent on their prior experiences and barriers they have encountered. For example, veterans with PTSD may require visual or auditory materials to understand, which are less likely to overwhelm them than reading assignments (Cusick, 2023). Likewise, UDL can help non-traditional learners recognize their learning and experiences by providing choices of presenting their learning in a manner that is appreciative of their prior learning and experiences (Armstrong, 2020). Moreover, UDL highlights the importance of offering choices for learners to show what they know in practice and practice. This flexibility can be useful for adult learners who may have had confusion about assessment methods, when they came from an environment that allowed them to demonstrate their learning in a way that was comfortable for them, based on their strengths and experiences (Baucham, 2020).

Another aspect of UDL is engagement which seeks to capture learners’ interest and motivation in learning. Adult learners tend to find course material more engaging when there are connections to real-life experiences and their future career plans. Universal Design for Learning (UDL) has the potential of bringing theory and practice together thus increasing the chances of adult learners being engaged in the learning process as remarked by Adams et al. (2017). The effects of UDL on education for adults are far reaching in the development of learning environments that are versatile. Research shows that UDL can improve the academic success and learning process for adult students. In addition, UDL helps address the challenges of mental health and cultural adaptation for student veterans while leveraging their diverse experiences and abilities to enhance their learning and growth (Brawner et al., 2015; Cusick, 2023).

This paper shows how UDL can be used as a strategic plan to change the way of teaching and learning to meet the principles of adult learning theory, a theory that emphasizes inclusivity and accessibility. The focus of UDL on representation, action, expression, and engagement is well in line with the needs of adult learners in higher education. It is possible to significantly improve the learning experience for all students, including those with special needs, such as military veterans, through the implementation of UDL. Thus, adopting UDL strategies and methodologies helps in creating a supportive learning environment and enhances the learning process by considering the diverse experiences and perspectives of the learners.

Creating Opportunities for Success

Baucham (2020) explores how faculty can incorporate UDL strategies in online general education courses for students with varying learning styles. The study highlights the importance of UDL in the learning process to ensure that every student, such as non-traditional learners, can learn with ease. Through the provision of different means of representation, expression, and engagement, UDL guarantees the inclusion of different learning preferences and needs.

Cusick (2023) looks at the function of UDL in education to ensure that every student, including non-traditional, can succeed. The paper calls for the application of UDL principles in curriculum and teaching, to break down the barriers and enhance the learning process for students with diverse needs. The Universal Design for Learning strategies appear to be effective in the efforts to help non-traditional students overcome the obstacles and be more successful in post-secondary education. With the principles of UDL, such as representation, action, and expression, as well as engagement, educators can ensure that all students in the classroom can learn in a more inclusive and accessible way.

2.7. Raising Enablers Through UDL

2.7.1. Leveraging Prior Experience

It is important that colleges and universities understand and value the skills and experiences that many non-traditional students have to bring to the educational process (Armstrong, 2020). The principle of providing ways for action and expression in UDL is a way through which non-traditional learners can easily incorporate their experiences such as leadership, problem solving, and teamwork skills that they gained in the military into the college. For example, giving students a project to work on can help students use the leadership, problem-solving, and teamwork skills that they developed during their careers to make the transition to college easier and to help them to persevere and succeed.

2.7.2. Campus Resources and Support Systems

Similarly, it is imperative that non-traditional students have support systems in place to help them succeed (Adams et al., 2017). In the context of UDL, support systems are created by helping learning environments to meet the diverse needs of students. For instance, virtual forums and discussion boards can extend the reach of campus support services so students can get the support they need from their peers. Thus, through the integration of UDL principles, higher education institutions can work towards the improvement of the barriers faced by non-traditional students and the promotion of the factors that support their success. This approach does not only benefit non-traditional students but all students and, hence, fosters a more inclusive and equitable learning environment. Thus, as higher education institutions work to meet the needs of non-traditional students, they can become more welcoming, ready, and encouraging as students transition to academic life.

2.7.3. Addressing Psychological and Physical Health Concerns

Some students may also have physical and/or mental challenges, such as PTSD or traumatic injuries, which can limit their learning (Bitting, 2023). However, with UDL strategies, for example, through the ways of presenting information, such as offering course materials in audio, video, or text formats, many of these challenges can be addressed. For instance, students with PTSD may use videos instead of lectures to control the learning process. Also, providing some grace in assignment submissions and other forms of student participation can help those with certain impairments to not feel left out in the learning process (Cusick, 2023).

2.7.4. Enhancing Cultural and Social Integration

Social integration of non-traditional students into academic environments can be accompanied by feelings of isolation (Brawner et al., 2015). This conceptualization can be related to the use of UDL in class and how it helps to solve the problem of gaps in students’ engagement. Other strategies like group activities, such as group projects and peer-review sessions, can also help to build the students’ relationships, including non-traditional students, and create a community in the classroom. Also, curricula that include student viewpoints can help in incorporating the experiences of non-traditional students and making them feel more included and appreciated in the learning process (Baucham, 2020).

2.7.5. Limitations of Literature on UDL Strategies for Non-Traditional Learners in Higher Education

Although UDL has been around for decades, there is still limited literature regarding UDL Strategies for non-traditional learners. There are some scarce sources that have started to address the issue of educational barriers and enablers for non-traditional learners in higher education within the context of UDL. Due to the limited research, there is still a gap in knowledge exploring the application of UDL strategies for the plethora of students that self-report as falling into one or more of the several non-traditional learner demographics.

Boothe et al. (2018) conducted a study that focused on how the needs of diverse students can be met through UDL strategies. Almost all the UDL-related research has been done on its effectiveness for students and policies. However, little is known about how, specifically, non-traditional learners have been impacted by the UDL strategies and whether it either helped them or acknowledged their own learned barriers (Boothe et al., 2018). The study outlined a greater need for further information and research regarding what UDL could do for these unique student groups in need of special attention (Boothe et al., 2018).

In recent years, there has been increasing number of examples of UDL strategies adjusted to the obstacles that non-traditional learners may also experience. Bradshaw (2020) conducted a qualitative study with a disabled student sample in order to explore how UDL strategies can help them. The results of semi-structured interviews and the subsequent analysis showed that there was a need for more inclusive strategies that take into account the mental and physical issues of students. As a result, the works of recent research (Boothe et al., 2018; Murawski & Scott, 2019; Bradshaw, 2020) have revealed several main issues of contemporary UDL literature. This offers many possibilities for the study to help in facilitating further research and investigation into what UDL techniques can achieve for non-traditional learners.

2.8. Research Gaps and Contemporary Challenges

Recent scholarship has identified several critical gaps in supporting non-traditional learners. Winfield et al. (2023) found that post-COVID-19 institutions have struggled to maintain quality distance learning specifically designed for non-traditional adult learners’ unique needs. Similarly, Beck Wells (2022) demonstrated that students with disabilities, language barriers, and low socioeconomic status are often less successful in online education than students from dominant cultural groups, highlighting systemic inequities in virtual learning environments.

The COVID-19 pandemic has further illuminated these challenges. Ren (2023) investigated online instructors’ experiences and found that while remote learning offers accessibility benefits, it has revealed significant engagement and retention challenges among non-traditional populations. These findings underscore the urgent need for systematic approaches like UDL that can address multiple barriers simultaneously (Almeqdad et al., 2023).

Despite growing interest in UDL applications, research specifically examining its effectiveness for non-traditional learners remains limited. Brozina et al. (2024) conducted a systematic review revealing inconsistent definitions of non-traditional students and called for clearer frameworks in higher education research. Most UDL studies focus on K-12 populations or traditional college students with disabilities, leaving a significant empirical gap regarding how UDL principles intersect with the complex lives and learning needs of adult students.

2.9. Theoretical Framework

2.9.1. Connecting Adult Learning Theory and UDL

This study is grounded in the intersection of adult learning theory and Universal Design for Learning principles. M. Knowles (1984) andragogy emphasizes that adult learners are self-directed, bring rich experience to learning contexts, and are motivated by immediate applicability of knowledge. These characteristics align closely with UDL’s three primary principles: multiple means of engagement (connecting to learners’ interests and motivations), multiple means of representation (accommodating diverse learning preferences), and multiple means of action and expression (allowing various ways to demonstrate knowledge) (CAST, 2018).

The theoretical alignment becomes particularly relevant for non-traditional learners who often balance competing responsibilities while pursuing education. Mezirow’s (2000) transformative learning theory further informs this study, as non-traditional learners frequently experience perspective transformation through their educational journey. UDL’s flexible framework can facilitate these transformative experiences by removing barriers and providing multiple pathways to learning.

Universal Design for Learning functions not merely as a pedagogical approach but as an equity framework that addresses systemic educational barriers. Kumashiro’s (2000) anti-oppressive education theory aligns with UDL’s foundational premise that traditional educational structures inadvertently privilege certain learning styles, cultural backgrounds, and life experiences while marginalizing others.

For non-traditional learners, these marginalization processes are particularly pronounced. First-generation college students may lack cultural capital and institutional knowledge that traditional students take for granted (Stephens et al., 2012). Veterans transitioning from military to academic contexts must navigate entirely different organizational cultures and communication styles (Rumann & Hamrick, 2010). Adult learners with family and work responsibilities face temporal and geographic constraints that traditional campus-based models fail to accommodate.

UDL’s three principles directly address these equity concerns: multiple means of engagement acknowledges diverse motivational structures shaped by cultural and experiential backgrounds; multiple means of representation recognizes that information processing preferences are influenced by linguistic diversity, cultural learning styles, and prior educational experiences; and multiple means of action and expression provides alternatives to assessment methods that may disadvantage students from non-dominant cultural groups or those with competing life responsibilities.

2.9.2. Why UDL Should Specifically Benefit Non-Traditional Learners

The theoretical rationale for UDL’s particular relevance to non-traditional learners extends beyond general inclusivity principles to address specific challenges this population faces. Non-traditional learners typically enter higher education with:

• Diverse learning histories: Unlike traditional students with recent, standardized K-12 experiences, non-traditional learners may have gaps in formal education, varied cultural educational backgrounds, or learning approaches developed in non-academic contexts (military, workplace, family responsibilities). UDL’s multiple means of representation directly addresses this diversity in prior learning experiences.
• Competing cognitive demands: Adult learners simultaneously manage work, family, and educational responsibilities, requiring cognitive load management that traditional pedagogies often ignore. UDL’s emphasis on clear goals and flexible expression methods should theoretically reduce extraneous cognitive burden.
• Varied motivational structures: Unlike traditional students often motivated by external expectations or general career preparation, non-traditional learners frequently have specific, immediate goals (career change, skill updating, economic mobility). UDL’s engagement principles should align particularly well with this goal-directed motivation.

This theoretical alignment suggests that UDL implementation should show stronger effects for non-traditional than traditional student populations, making the current study’s mixed findings particularly significant for understanding implementation challenges.”

2.10. Research Questions

The increasing student diversity within the context of higher education calls for a deeper consideration and analysis of Universal Design for Learning (UDL) and its effectiveness in improving the academic achievement and learning experiences for non-traditional learners. This study will help to unravel the multifaceted relationship between the implementation of UDL and the results for students who are not the typical university students because of their age, work or family responsibilities, among several others (Schreffler et al., 2019). The study has several specific research questions which will help to explore the impact of UDL on different aspects of the non-traditional students’ academic experience.

RQ1: Do UDL-aligned courses have an impact on the academic performance (GPA) of non-traditional learners, compared to those not aligned with the UDL framework?

H0. 

There is no statistically significant relationship between UDL-aligned coursework and academic performance (GPA).

H1. 

There is a statistically significant relationship between UDL-aligned coursework and academic performance (GPA).

This will be addressed through comparison of data from the PLSI and self-reported student GPA range data.

RQ2: Do UDL-aligned courses have the same impact across the various non-traditional learner demographics?

H0. 

There is no statistically significant relationship between the impact of UDL-aligned coursework and the individual non-traditional learner demographics.

H1. 

There is a statistically significant relationship between the impact of UDL-aligned coursework and the individual non-traditional learner demographics.

This will be addressed through comparison of data from the PLSI and self-reported non-traditional student demographic data.

RQ3: What are the perceived enablers or positive-influencing characteristics of UDL-aligned coursework, according to non-traditional student feedback?

RQ4: What are the perceived barriers or negative-influencing characteristics of UDL-aligned coursework, according to non-traditional student feedback?

RQ5: What are the best practices post-secondary institutions can implement to improve academic experience and performance, according to non-traditional student feedback?

These research questions will be addressed through thematic coding and analysis of self-reported non-traditional student feedback from the three qualitative survey questions. The research questions are based on previous studies which show that UDL has the potential to meet the needs of a diverse student population through multiple means of engagement, representation, and expression, and thus consistent with the principles of adult learning theory and inclusive education (Hall et al., 2012; Scott et al., 2003). Examining the impact of UDL-aligned coursework on GPA and students’ feedback on UDL, will help inform the development of educational practices and policies that seek to enhance learner engagement and achievement.

This examination includes the use of a Personalized Learning Supporting Instrument (PLSI), which has been proven to be a valid tool in assessing the extent of compatibility of learning environments with the UDL principles (Zhang et al., 2022). The use of quantitative data from the PLSI and qualitative data from student feedback provides a holistic view of the impact of UDL across different learning environments for various non-traditional demographics, leading to more sophisticated and specific strategies for meeting the needs of today’s students.

3. Instrumentation and Protocol

This study employed a convergent parallel mixed-methods design using both validated quantitative measures and open-ended qualitative questions to comprehensively assess UDL implementation effectiveness for non-traditional learners.

3.1. Personalized Learning Supporting Instrument (PLSI)

The quantitative component utilized Zhang et al.’s (2022) Personalized Learning Supporting Instrument, adapted for higher education contexts. The PLSI measures four UDL-aligned constructs through participant self-report on 0–100 sliding scales:

• Clear and Relevant Goals (CRGs): 3 items measuring goal clarity and relevance;
• Flexible Instructional Methods and Materials (FMMs): 11 items assessing instructional flexibility and choice;
• Supporting Learner Variability (SLV): 5 items evaluating accommodation of diverse learning needs;
• Expert Learning (EL): 6 items measuring self-regulation and strategic learning;
• The instrument demonstrated strong content validity through expert panel review (CVI > 0.80) and preliminary reliability in secondary education contexts. For this study, language was adapted to reflect higher education experiences while maintaining construct integrity.

3.2. Qualitative Data Collection

The four open-ended questions were created through an iterative, theory- and literature-driven process to ensure strong content validity. First, the research team mapped each research question to the study’s conceptual framework (Universal Design for Learning and adult learning theory) and to themes emerging from prior UDL and non-traditional learner studies (e.g., Boothe et al., 2018; Winfield et al., 2023). Draft questions were then reviewed by two experts in mixed-methods research and one faculty member experienced in qualitative interviewing to check clarity, alignment, and relevance. After minor revisions for readability and neutrality of wording, the final set of four questions, one demographic prompt and three focused on barriers, enablers, and institutional improvements, was pilot-tested with three non-participant adult learners to confirm comprehensibility before full deployment.

Three open-ended questions complemented the quantitative measures to capture nuanced experiences not reflected in standardized items:

• Barriers: “Can you describe any obstacles or challenges you have faced during your coursework? What specific aspects of the coursework design contributed to these challenges?”
• Enablers: “In your higher education experience, what aspects of your coursework have had the most significant impact on your learning and academic success? Please provide examples.”
• Recommendations: “How do you think your university/college could improve coursework to better support non-traditional learners like yourself?”

These questions were designed to identify specific UDL implementation strengths and gaps from the student perspective, providing actionable insights for institutional improvement.

3.3. Data Integration Strategy

The convergent design allowed simultaneous collection and independent analysis of quantitative and qualitative data, with integration occurring during interpretation. This approach addresses the complexity noted by Brozina et al. (2024) in non-traditional student research by capturing both standardized effectiveness measures and contextual factors that quantitative instruments might miss.

3.4. Participant Recruitment and Sample Size

A power analysis using G*Power 3.1.9.7 determined that 107 participants were required for structural equation modeling analyses, based on anticipated medium effect sizes (f² = 0.15) with α = 0.05 and power = 0.80.

Participants were recruited through multiple channels at a Hispanic-serving institution using Canvas learning management system announcements and university communication platforms. Initial contact emails explained the study purpose, participation requirements, and data anonymity protections. Informed consent was integrated into the SurveyMonkey questionnaire introduction.

To optimize response rates, systematic follow-up reminders were sent weekly for four weeks following initial contact, following Dillman et al.’s (2014) recommendations for survey research. This recruitment strategy yielded 174 initial respondents (19% response rate), with 154 completing the quantitative portion and 129 providing complete quantitative and qualitative responses for final analysis.

The final sample (N = 154) (

Table 1. Non-traditional learner characteristics (N = 154).

Characteristic	n	%
Age 25 years or older	92	59.74%
First-generation college student	62	40.26%
Financially independent college student	52	33.77%
Commuter student (greater than 10 miles from campus)	43	27.92%
Have dependents (e.g., children or parents you care for)	40	25.97%
Returned to school for career change	37	24.03%
Hybrid or asynchronous coursework	35	22.73%
International student	34	22.08%
Delayed enrollment (did not attend college directly after high school)	23	14.94%
Multi-lingual learner (MLL)	22	14.29%
English-language learner (ELL)	16	10.39%
Part-time student (less than 6 credit hours)	11	7.14%
Military veteran	8	5.19%
Do not wish to disclose	5	3.25%
Other (please specify)	3	1.95%

) exceeded minimum power requirements and demonstrated diverse non-traditional characteristics: 59.74% age 25+, 40.26% first-generation college students, 33.77% financially independent, and 47.4% identifying with language diversity (multilingual or English-language learners). This demographic composition aligned well with contemporary non-traditional learner populations while providing adequate statistical power for planned analyses.

3.5. Sample Representative Considerations

The academic performance distribution (

Table 2. Self-reported GPA ranges (N = 160).

GPA Range	n	%
3.6–4.0	99	61.88%
3.0–3.5	46	28.75%
2.5–2.9	6	3.75%
2.0–2.4	1	0.63%
0–1.9	0	0.00%
Do not wish to disclose	8	5.00%

) in this sample raises important questions about representativeness and generalizability. With 90.63% of participants reporting GPAs above 3.0 and only 4.38% below this threshold, the sample appears to over-represent academically successful non-traditional learners. This distribution could result from several factors: (1) response bias, where higher-performing students are more likely to participate in educational research; (2) institutional context effects, as recruitment occurred at a single Hispanic-serving institution with specific support structures; or (3) genuine effectiveness of UDL-aligned environments in supporting student success.

This high-achieving sample bias has several implications for interpreting results. The limited variance in GPA may have restricted our ability to detect UDL effects that might be more pronounced among students facing greater academic challenges. Additionally, the positive qualitative responses may reflect experiences of already-successful students rather than those most needing support. Future research should prioritize recruitment strategies that capture broader academic performance ranges, potentially through partnerships with student support services or remedial programs.

3.6. Mixed-Methods Design and Integration

This study employed a convergent parallel mixed-methods design, where quantitative and qualitative data were collected simultaneously and independently analyzed before integration (Creswell & Plano Clark, 2018). The PLSI provided standardized measurements of UDL implementation effectiveness, while open-ended questions captured nuanced experiences and contextual factors that quantitative measures might miss.

Integration occurred during interpretation, where qualitative themes were used to explain quantitative findings, particularly in cases where statistical relationships were non-significant. This approach allows for a more comprehensive understanding of how UDL impacts non-traditional learners beyond what either method could reveal independently, addressing the complexity noted by Brozina et al. (2024) in their systematic review of non-traditional student research.

4. Data Analysis

The data analysis for both the quantitative and qualitative components of this study was planned to ensure that the findings are rigorous, reliable, and valid. Using statistically and thematically analyzed findings and integrating them through a mixed-methods approach, the study generated practical findings that may significantly improve the educational experiences and outcomes of non-traditional learners in higher education settings.

4.1. Demographic Analysis and Academic Performance Patterns

Cross-tabulation analysis of participant demographics and self-reported GPA ranges reveals compelling patterns that both challenge traditional assumptions about non-traditional learner academic performance and illuminate potential areas of educational inequity (

Table 3. Academic performance by non-traditional learner demographics.

Demographic Characteristic	Sample Size (n)	High GPA (3.6–4.0)	Medium GPA (3.0–3.5)	Lower GPA (<3.0)
Part-time student	11	11 (100.0%)	0 (0.0%)	0 (0.0%)
Multi-lingual learner (MLL)	22	17 (77.3%)	2 (9.1%)	1 (4.5%)
Returned for career change	37	28 (75.7%)	8 (21.6%)	0 (0.0%)
Military veteran	8	6 (75.0%)	2 (25.0%)	0 (0.0%)
Have dependents	40	30 (75.0%)	7 (17.5%)	2 (5.0%)
Delayed enrollment	23	17 (73.9%)	4 (17.4%)	2 (8.7%)
Financially independent	52	38 (73.1%)	10 (19.2%)	2 (3.8%)
Age 25+ years	92	64 (69.6%)	22 (23.9%)	2 (2.2%)
English-language learner	16	11 (68.8%)	3 (18.8%)	1 (6.3%)
Hybrid/asynchronous courses	35	23 (65.7%)	10 (28.6%)	1 (2.9%)
International student	34	20 (58.8%)	12 (35.3%)	1 (2.9%)
Commuter student	43	25 (58.1%)	15 (34.9%)	1 (2.3%)
First-generation college	62	33 (53.2%)	26 (41.9%)	2 (3.2%)

). The analysis examined 13 distinct non-traditional demographic characteristics against five GPA categories, providing insights into which populations may be most effectively served by current institutional approaches.

4.2. Counterintuitive High-Performance Patterns

The demographic analysis reveals several counterintuitive patterns that challenge deficit-based assumptions about non-traditional learners. Students with what are traditionally considered “barriers” to academic success demonstrate remarkably high academic performance. Most notably, students with dependents, often viewed as facing significant time and attention constraints, achieved high GPAs at a 75.0% rate, substantially exceeding the overall sample rate of 68.0%. Similarly, financially independent students, who typically juggle employment with academics, demonstrated high academic performance (73.1% with GPAs ≥ 3.6).

These patterns suggest that life experiences traditionally framed as obstacles may actually cultivate skills and motivations that enhance academic success. Adult learners returning for career advancement (75.7% high GPA) and those with delayed enrollment (73.9% high GPA) may bring increased focus, clear goal orientation, and real-world experience that facilitates academic achievement. This aligns with M. Knowles’ (1984) andragogical principles, which emphasize that adult learners’ life experiences serve as rich resources for learning rather than impediments to overcome.

4.3. Language Diversity as Academic Strength

Particularly striking is the strong academic performance of language-diverse students. Multi-lingual learners achieved the second-highest high GPA rate (77.3%), while English-language learners maintained a 68.8% high GPA rate, both exceeding the sample average. This finding contradicts traditional assumptions that language barriers impede academic success and instead suggests that linguistic diversity may correlate with cognitive flexibility, cultural adaptability, and academic resilience.

The strong performance of both international students (58.8% high GPA) and domestic multilingual learners (77.3% high GPA) indicates that language diversity, rather than constituting a barrier, may represent an academic asset when properly supported through inclusive pedagogical approaches like UDL. This finding has significant implications for institutional policies and faculty development approaches.

4.4. Educational Equity Concerns

Despite these overall positive trends, the analysis reveals important educational equity concerns that warrant institutional attention. First-generation college students demonstrated the lowest high GPA rate (53.2%), representing a 14.8 percentage point gap below the sample average. This disparity suggests that cultural capital and institutional knowledge deficits identified by Stephens et al. (2012) continue to impact academic outcomes even within UDL-aligned learning environments.

Commuter students (58.1% high GPA) and international students (58.8% high GPA) also performed below the sample average, indicating that geographic and cultural integration challenges may not be fully addressed by current pedagogical approaches. These findings suggest that while UDL principles support many non-traditional populations effectively, certain groups require additional or differentiated intervention strategies.

4.5. Implications for UDL Implementation

The demographic performance patterns provide important context for interpreting the quantitative UDL effectiveness findings. The overall high academic performance across most non-traditional demographics suggests that either (a) UDL-aligned environments are effectively supporting diverse learners, or (b) the sample may be skewed toward academically successful non-traditional students, potentially limiting detection of UDL’s impact on struggling populations.

The exceptionally high performance of part-time students (100% high GPA, though n = 11) and students in hybrid/asynchronous formats (65.7% high GPA) provides preliminary evidence that flexible learning modalities, core UDL principles, may indeed support academic success. However, the lower performance of first-generation students indicates that UDL implementation alone may be insufficient to address systemic educational inequities without complementary cultural and institutional support interventions. These patterns underscore the complexity of educational equity work and highlight the need for differentiated approaches that recognize both the strengths that non-traditional learners bring and the specific barriers that certain populations continue to face despite inclusive pedagogical frameworks.

4.6. Quantitative

Quantitative data was analyzed from the Personalized Learning Supporting Instrument (PLSI) using rigorous statistical analysis. The PLSI was originally developed and validated to ensure that it is a valid and reliable measure (Zhang et al., 2022) and includes items that measure the effectiveness of learning environments aligned with UDL principles. This instrument includes constructs such as Clear and Relevant Goals (CRGs), Flexible Instructional Methods and Materials (FMMs), Supporting Learner Variability (SLV), and Expert Learning (EL).

To test the hypothesized relationships between UDL-aligned learning environments and student outcomes, Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were used. These relatively stringent methods were used because they capture a comprehensive understanding of the constructs and how they work together (Brown, 2015). To appropriately interpret these hypothesized relationships and effectively mode their complexity, the analysis was conducted using SPSS 21 and Amos 21 software (Byrne, 2012).

4.6.1. Confirmatory Factor Analysis

A confirmatory factor analysis (CFA) was conducted to evaluate the measurement model of the PLSI. The final model included the original aforementioned four factors: Clear and Relevant Goals (CRGs); Flexible Instructional Methods and Materials (FIMMs); Expert Learning (EL); and Supporting Learner Variability (SLV). The model demonstrated excellent fit across multiple indices (

Table 4. Model fit and reliability results.

Category	Statistic	Value/Result	Standard/Threshold
Model Fit	Comparative Fit Index (CFI)	0.969	Excellent fit (≥0.95 is “good”)
	Tucker–Lewis Index (TLI)	0.958	Excellent fit (≥0.95 is “good”)
	Root Mean Square Error of Approximation (RMSEA)	0.077	Acceptable fit (<0.08)
	Standardized Root Mean Square Residual (SRMR)	0.093	Slightly above ideal (<0.08), but close
	Chi-square/df	1.73	Acceptable (<2–3)
	NNFI, IFI, NFI, RFI, RNI	>0.90	All excellent (>0.90)
Factor Loadings	All retained items (except SLV3)	>0.70	Strong factor loadings
	SLV3	0.847	Strong loading
	Cross-loadings/very low indicators	None	No problematic cross-loadings or low loadings
Reliability	ω (omega) and α (alpha) for CRGs, FIMMs, EL	>0.85	Excellent internal consistency
	ω and α for SLV	0.563, 0.598	Borderline/low; consider further development

), with CFI = 0.969 and TLI = 0.958 both exceeding the stringent threshold of 0.95, indicating large effect sizes for model fit. The RMSEA = 0.077 fell within acceptable range (<0.08), while SRMR = 0.093 approached but slightly exceeded the ideal threshold (<0.08), collectively suggesting the model adequately captured the underlying factor structure with minimal misspecification (Browne & Cudeck, 1993; Hu & Bentler, 1999).

All factor loadings were statistically significant and above recommended thresholds (range: 0.78 to 1.13), representing medium to large effect sizes according to Cohen’s conventions for factor loadings (Cohen, 1988). The standardized factor loadings exceeded the minimum threshold of 0.70, with 85% of items showing loadings above 0.80, indicating strong relationships between observed variables and their respective latent constructs. Internal consistency reliability exceeded acceptable thresholds for most constructs, with CRGs (ω = 0.849, α = 0.834), FIMMs (ω = 0.872, α = 0.874), and EL (ω = 0.895, α = 0.891) all demonstrating good to excellent reliability (Nunnally & Bernstein, 1994).

Supporting Learner Variability Construct Limitations

The Supporting Learner Variability (SLV) construct demonstrated concerning psychometric properties (ω = 0.563, α = 0.598), falling substantially below the conventional reliability threshold of 0.70 (Nunnally & Bernstein, 1994). This poor internal consistency suggests that the retained SLV items may not coherently measure a single underlying construct, potentially reflecting the conceptual complexity of ‘learner variability’ as experienced by non-traditional students.

The reliability issues may stem from the diverse ways non-traditional learners experience variability-temporal (schedule changes), cognitive (competing attention demands), or contextual (changing life circumstances). The three retained items (SLV2: focus and understanding; SLV3: participation despite challenges; SLV5: accessing help/resources) may represent distinct rather than unified aspects of variability support.

These measurement limitations compromise interpretations involving the SLV construct and suggest this domain requires substantial item development before reliable assessment can be achieved. Future research should consider whether learner variability support represents a single construct or multiple related but distinct dimensions.”

4.6.2. Factor Intercorrelations and Effect Sizes

Factor intercorrelations ranged from r = 0.23 to r = 0.67, with most relationships falling in the small to medium effect size range according to Cohen’s conventions (small r = 0.10, medium r = 0.30, large r = 0.50). The strongest correlation was observed between Clear and Relevant Goals and Expert Learning (r = 0.67, 95% CI [0.54, 0.77]), representing a large effect size and supporting the theoretical relationship between goal clarity and self-regulated learning strategies.

4.6.3. Strategic Elimination of Items

To develop a reliable, valid, and generalizable instrument, the researchers adopted a strategic, multi-stage process for item elimination in the CFA framework (Bandalos & Finney, 2019). This approach was guided by empirical evidence (e.g., low loadings, high modification indices) and theoretical rationale to ensure the resulting measure was both psychometrically robust and conceptually comprehensive (DeVellis, 2017; Kline, 2016).

Initially, all 25 items were included (Appendix A.1), corresponding to four hypothesized latent factors. The first stage of analysis evaluated factor loadings and internal consistency, identifying items with low standardized loadings (<0.60), high residuals, or problematic cross-loadings as candidates for removal (Hair et al., 2019). Modification indices were also consulted to highlight sources of model misfit (Byrne, 2012). At each stage, item elimination decisions were made in conjunction with a review of item content, ensuring no essential aspects of each construct were lost (Clark & Watson, 2019).

The process was intentionally iterative, conservative, and exhaustive. After each item elimination, the model was reanalyzed, and fit indices were reassessed (Marsh et al., 2004). Items were only removed when their exclusion resulted in substantial improvements to model fit and did not compromise the theoretical breadth or content validity of the associated factors (DeVellis, 2017). Through this process, the PLSI was refined from 25 to 12 items (Appendix A.2), each demonstrating strong factor loadings, minimal error covariances, and high internal consistency. The final model (

Table 5. Item elimination loading and rationale.

Item	Loading	Rationale for Elimination
CRG1	1.000	Substantial conceptual overlap with CRG2/CRG3; not as essential as CRG2 (“I learned useful things…”) and CRG3 (“My instructor communicated the goals…”).
FMM1	1.000	Redundant with FMM6/FMM7; “options to learn” overlapped with broader/stronger items.
FMM2	1.051	Redundant with FMM7/FMM8 (“options to show learning”/“ways to demonstrate learning”).
FMM3	1.209	Conceptually similar to FMM7/FMM8 (“multiple ways to participate” vs. “demonstrate learning”).
FMM4	1.090	Content overlap with FMM6/FMM7 (“multiple ways to learn”); eliminated for parsimony.
FMM5	1.137	“Choices in materials” overlapped with FMM6 (“choices in how I learned”); slightly lower loading than retained items.
FMM9	1.148	Focus on remediation/alternate ways to understand covered by broader FMM6/FMM8.
FMM10	1.130	“Feedback on how to improve” overlaps with FMM11 (“feedback/check progress”); FMM11 has broader interpretation.
EL1	1.000	Overlaps with EL4/EL6 (regarding self-regulation); not as central as retained items.
EL2	0.835	Lowest loading of ELs; “motivation” is important, but EL3/EL4/EL6 better capture strategies and regulation.
EL5	0.526	Lowest EL loading; content about “solving problems on my own” covered by EL3/EL4/EL6.
SLV1	1.000	Overlaps with SLV2 (both about understanding/focus); retained items represent construct with higher reliability.
SLV4	0.704	Lowest SLV loading; “identifying what to do” overlaps with SLV3/SLV5.

) achieved excellent global fit, indicating that the retained items provide a parsimonious yet comprehensive measure of the targeted constructs (Hu & Bentler, 1999). This strategic reduction enhances the usability of the instrument in both research and practice, ensuring robust, interpretable, and actionable results.

During CFA model refinement, 13 items were eliminated from the original 25-item PLSI. Removal was guided by both statistical performance (low factor loadings, high modification indices) and content analysis to avoid redundancy and preserve conceptual coverage. For example, CRG1 (“I understood the learning goals of the course” loading = 1.000) was dropped because its core concept was already captured by the retained items CRG2 and CRG3, and its contribution to model fit was less than these more specific items.

Within the Flexible Instructional Methods and Materials (FMMs) domain, several items overlapped in meaning, according to the participant feedback. FMM2 (“options to show what I learned” loading = 1.051), FMM7 (“multiple opportunities to show what I learned” loading = 1.138), and FMM8 (“multiple ways to show my understanding” loading = 1.154) all addressed assessment flexibility. FMM7 and FMM8 were retained due to stronger statistical contributions and slightly clearer item wording; FMM1–5 and others were removed to avoid redundancy.

Similarly, FMM10 (“the instructor provides me other ways to understand” loading = 1.155) overlapped conceptually with items emphasizing multiple ways to access content (FMM6, FMM7), and its removal improved overall fit indices. In the Expert Learning (EL) factor, EL2 (“I was motivated to achieve the course goals” loading = 0.794) and EL5 (“I was encouraged to solve problems on my own before asking for help” loading = 0.526) were eliminated due to their lower loadings and substantial overlap with retained items addressing problem-solving and use of strategies (EL3, EL4, EL6). For Supporting Learner Variability (SLV), SLV1 (“How well did you understand the topic of the course?” loading = 1.000) and SLV4 (“How easy was it to identify what needed to get done…?” loading = 0.704) were dropped for redundancy with SLV2 and SLV3, and for contributing the least unique variance.

This theory- and data-driven process ensured that retained items had strong loadings, minimal overlap, and that each construct was represented by at least three distinct, high-performing items, consistent with best practices for scale refinement (Clark & Watson, 2019; Worthington & Whittaker, 2006). This approach allowed us to retain the most distinct, robust, and theoretically central items per factor, maximizing both psychometric quality and content coverage (Clark & Watson, 2019; Worthington & Whittaker, 2006).

The reduction of the PLSI from 25 to 12 items offers several methodological and practical benefits (DeVellis, 2017; Furr, 2022). A shorter scale enhances respondent engagement and reduces survey fatigue (Credé et al., 2012), particularly valuable in applied or classroom settings where time is limited. Eliminating low-performing or redundant items improves the overall psychometric integrity of the instrument: factor loadings are increased, model fit is improved, and the potential for measurement error or construct contamination is reduced (Clark & Watson, 2019; Hair et al., 2019). The resulting instrument is both easier to administer and more likely to yield reliable and interpretable scores across diverse samples.

However, this process is not without its potential drawbacks. The primary concern in item reduction is the risk of compromising content validity, the extent to which the scale fully represents the conceptual domain of each construct (Clark & Watson, 2019). By removing items, particularly those that capture unique or nuanced aspects of the factors, there is a possibility that the measure becomes too narrow or omits important facets (DeVellis, 2017). In this study, great care was taken to retain at least three strongly loading, theoretically central items per factor; nonetheless, some degree of loss in breadth is inevitable when moving from 25 to 12 items.

Another consideration is generalizability. While the shorter instrument performed well in the current sample, its validity and reliability should be further confirmed in independent and more diverse populations (Bandalos & Finney, 2019). Moreover, for the Supporting Learner Variability (SLV) factor, reliability indices remained lower than desired, suggesting the need for future item development to fully capture this dimension.

The strategic item reduction process produced a robust and efficient instrument that balances statistical rigor with practical utility. That being said, ongoing validation is recommended to ensure further that the shortened scale remains a comprehensive and flexible tool for assessing barriers and enablers to learning in higher education contexts. Especially given that this instrument was administered to a smaller private Hispanic-serving post-secondary institution.

Reducing the number of survey items from 25 to 12 provides several practical advantages in both research and applied contexts. Shorter questionnaires have consistently been shown to improve response rates and reduce respondent burden (Rolstad et al., 2011; Hoerger, 2010). This is particularly important in educational settings, where participants may already face multiple demands in their professional and personal time. Excessively long surveys are more likely to induce survey fatigue, which can lead to careless responding, increased missing data, and lower data quality (Galesic & Bosnjak, 2009; Porter et al., 2004). By streamlining the instrument, researchers can obtain more reliable and complete data, while also promoting positive participant experiences. Furthermore, brief, psychometrically robust measures are more feasible for use in institutional assessment or classroom diagnostics, thereby increasing the likelihood of adoption and sustained use in practice (Fitzpatrick et al., 1998).

4.6.4. Structural Equation Modeling

Following CFA, structural equation modeling (SEM) was used to examine the relationships between the four latent factors and GPA. In the final model, none of the factors were found to be statistically significant predictors of GPA (all p > 0.05). The overall model explained 12% of the variance in GPA (R² = 0.12, adjusted R² = 0.08), representing a small to medium effect size (Cohen, 1988). Individual path coefficients ranged from β = −0.054 to β = 0.149, all falling within Cohen’s small effect size range (|β| < 0.30).

The relationship between UDL factors and GPA revealed complex patterns that require careful interpretation. In preliminary analysis using the original 25-item PLSI, Flexible Instructional Methods and Materials demonstrated a statistically significant positive relationship with GPA (β = 0.191, p = 0.039), suggesting that perceived instructional flexibility correlates with higher academic performance. However, following psychometrically-driven item reduction to improve model fit, this relationship became non-significant in the final 12-item model (β = 0.149, p = 0.164). This pattern illustrates the tension between statistical optimization and construct breadth in scale development.

For the other three constructs, Clear and Relevant Goals (CRGs), Expert Learning (EL), and Supporting Learner Variability (SLV), the path coefficients were positive but not statistically significant, except for SLV, which was weakly negative but non-significant (

Table 6. Relationships between PLSI factors and GPA (original 25-item version).

Predictor (PLSI Factor)	Path Coefficient to GPA	95% CI	Effect Size	Significance (p-Value)	Direction
Flexible Instructional Methods and Materials (FMMs)	0.191	[0.01, 0.37]	Small–Medium	0.039	Positive (S)
Clear and Relevant Goals (CRGs)	0.061	[−0.12, 0.24]	Small	0.507	Positive (NS)
Expert Learning (EL)	0.006	[−0.18, 0.19]	Negligible	0.950	Positive (NS)
Supporting Learner Variability (SLV)	−0.032	[−0.21, 0.15]	Small	0.742	Negative (NS)

Note: Effect sizes interpreted using Cohen’s conventions: small (0.10), medium (0.30), large (0.50). Model R² = 0.15, adjusted R² = 0.11, representing small to medium effect size for overall model. S = Significant; NS = Not Significant.

). This pattern suggests that while these UDL principles are directionally consistent with improved academic performance, their unique contributions to GPA may be more modest or context-dependent, or they may be more strongly associated with other important outcomes such as engagement, motivation, or retention.

After item reduction to the 12-item model, the relationship between FMM and GPA remained positive but was no longer statistically significant (β = 0.149, p = 0.164). The other constructs continued to display small, non-significant coefficients (CRG: β = 0.102; EL: β = 0.022; SLV: β = −0.054), and the pattern of positive or near-zero associations persisted (

Table 7. Relationships between PLSI factors and GPA (final 12-item version).

Predictor (PLSI Factor)	Path Coefficient to GPA	95% CI	Effect Size	Significance (p-Value)	Direction
Flexible Instructional Methods and Materials (FMMs)	0.149	[−0.06, 0.36]	Small	0.164	Positive (NS)
Clear and Relevant Goals (CRGs)	0.102	[−0.11, 0.31]	Small	0.373	Positive (NS)
Expert Learning (EL)	0.022	[−0.15, 0.19]	Negligible	0.822	Positive (NS)
Supporting Learner Variability (SLV)	−0.054	[−0.24, 0.13]	Small	0.628	Negative (NS)

Note: Effect sizes interpreted using Cohen’s conventions: small (0.10), medium (0.30), large (0.50). Model R² = 0.09, adjusted R² = 0.05, representing small effect size for overall model. NS = Not Significant.

). The reduction in significance could be due to the narrowing of construct coverage, reduced error variance, or the natural variability of sample characteristics after model refinement (Clark & Watson, 2019).

4.6.5. Interpretation of Non-Significant Quantitative Findings

The absence of statistically significant relationships between PLSI factors and GPA requires careful interpretation within the context of recent UDL research. Several factors may explain these findings. First, GPA may not fully capture the benefits that non-traditional learners derive from UDL-aligned courses. Beck Wells (2022) found that these students often prioritize skill acquisition, career relevance, and personal growth over grades. The qualitative findings support this interpretation, with participants emphasizing practical applications and supportive learning environments over academic achievement per se.

Second, the complexity of non-traditional learners’ lives means that GPA is influenced by numerous external factors (work demands, family responsibilities, financial stress) that may overshadow the impact of pedagogical approaches. Ren (2023) noted similar challenges in online instructor experiences with non-traditional learners. The significant positive relationship found in the original 25-item model between Flexible Instructional Methods and Materials (FMMs) and GPA (β = 0.191, p = 0.039) suggests that UDL elements do matter, but their effects may be subtle and context-dependent.

Finally, the cross-sectional nature of this study limits our ability to capture the cumulative effects of UDL implementation over time. Almeqdad et al. (2023) in their meta-analysis found that UDL effectiveness is often best detected through longitudinal research designs that can account for the complexity of educational interventions.

4.6.6. Theoretical Implications

The consistently positive direction of relationships, especially for FMMs, CRGs, and EL, supports the theoretical rationale of UDL and suggests these constructs may play a role in academic achievement, even if the effects were not statistically significant in this study. Importantly, the absence of significant negative associations means that efforts to support UDL-aligned practices are unlikely to be detrimental to GPA, and may offer non-academic benefits (e.g., improved satisfaction or persistence) that were not captured here.

4.6.7. Practical Implications

The significant relationship observed between FMMs and GPA in the original model provides preliminary evidence that increasing instructional flexibility could be a valuable strategy for improving academic outcomes, particularly for non-traditional or diverse learners. Even after scale refinement, the positive associations point to the potential value of UDL-aligned practices, and future research with larger samples or alternative outcome variables may clarify these relationships further.

5. Discussion

These findings reinforce the centrality of flexible instructional design within UDL frameworks and encourage continued investigation into how UDL-aligned constructs relate to a variety of learner outcomes beyond GPA. It is important to note that preliminary analyses using the full 25-item PLSI revealed a positive and statistically significant correlation between the factor of Flexible Instructional Methods and Materials (FMMs) and students’ self-reported GPA. This suggests that when courses offered greater flexibility in instructional methods, materials, and ways to demonstrate learning, students tended to report higher academic achievement and positive experiences.

This finding provides initial evidence supporting the impact of Universal Design for Learning (UDL)-aligned instructional flexibility on student success. Flexible instructional approaches, such as providing multiple options for engagement, varied types of learning materials, and diverse assessment formats, may facilitate deeper learning and accommodate diverse learner needs, ultimately supporting improved academic performance (CAST, 2018; Rao, 2021).

However, after the instrument was refined through strategic item elimination for psychometric robustness, the direct relationship between FMMs and GPA was no longer statistically significant in the final model. This highlights an important trade-off in scale development: while streamlined instruments are more efficient and reliable, the process of item reduction may attenuate certain relationships by narrowing construct breadth or removing contextually salient items (Clark & Watson, 2019; Furr, 2022).

5.1. Interpreting Mixed Statistical Findings

The divergent results between preliminary and final models highlight important methodological considerations. The significant relationship observed in the original model suggests that UDL’s emphasis on flexible instructional methods may indeed support academic achievement for non-traditional learners, consistent with adult learning theory’s emphasis on accommodation of diverse life circumstances (M. S. Knowles et al., 2020). However, the loss of significance following item reduction may indicate that (a) the relationship is modest and requires larger samples to detect reliably, or (b) certain eliminated items captured aspects of flexibility particularly relevant to academic performance.

5.2. Qualitative Data

Open-ended survey questions were posited to collect qualitative data, while thematic coding and analysis were used to analyze the responses. This allows for suitable identification, analysis, and reporting patterns (themes) within the data. It cleanly organized and described the dataset in rich detail, aiding in interpretation of various aspects of the research topic (Braun & Clarke, 2006). The study includes 3 open-ended questions designed to collect qualitative data on individualized experiences within higher education. These questions focus on student experiences with UDL-aligned learning environments, barriers and enablers to academic success for non-traditional learners, and perceptions of flexibility, inclusivity, and accessibility in higher education. The responses to the following questions will be thematically analyzed to complement quantitative findings from the PLSI.

• Can you describe any obstacles or challenges you have faced during your coursework in your courses? What specific aspects of the coursework design contributed to these challenges?
• In your higher education experience, what aspects of your coursework have had the most significant impact on your learning and academic success? Please provide an example(s), such as assignments or situations where these activities made a positive impact.
• How do you think your university/college could improve coursework to better support non-traditional learners like yourself?

The purpose of the first question was to identify specific elements of UDL that may act as barriers to learning from the perspective of non-traditional students. The second question seeks to understand the enablers within UDL strategies that have effectively supported the academic needs of non-traditional learners. Finally, the final question invites suggestions for improvements, providing actionable insights that could help institutions refine their UDL approaches based on direct feedback from non-traditional learners.

The thematic analysis was conducted in several stages, including data familiarization, generating initial codes, searching for themes, reviewing themes, defining and naming themes, and producing the report. The study employed NVivo, a qualitative data analysis software to help with the coding and the thematization processes. This tool provided deep, rigorous, and transparent qualitative research that has improved the credibility of the findings and knowledge of this topic (Brandão, 2015). The qualitative data provides added insights into the lived experiences of non-traditional learners and their perceptions of barriers and enablers within UDL-aligned learning environments, enriching the quantitative findings. By using this approach, the interpretations are further grounded in participants’ real-world experiences (Creswell & Plano Clark, 2018).

This qualitative analysis aimed to explore non-traditional learners’ experiences within higher education coursework, specifically highlighting perceived barriers, positive impacts, and recommendations for improvement. The responses from the participants to three open-ended questions were rigorously analyzed through qualitative thematic analysis. This systematic approach involved initial descriptive coding, subsequently refined into focused thematic codes to capture significant patterns within participant experiences and suggestions.

For the first open-ended question, which addressed perceived challenges encountered during coursework, six distinct thematic codes were identified:

(1)

Online Learning Difficulties,

(2)

Course Content and Delivery Issues,

(3)

Financial and Institutional Barriers,

(4)

Balancing Responsibilities,

(5)

Accessibility and Support Challenges, and

(6)

Personal and Health-Related Interruptions.

These themes reflected a range of academic and personal barriers impacting non-traditional students’ learning experiences, providing insights into areas needing targeted interventions.

The second question explored factors with a significant positive impact on learning experiences. Five clear thematic categories emerged from participant feedback:

(1)

Interactive and Applied Learning,

(2)

Supportive Academic Environment,

(3)

Skill Development through Coursework,

(4)

Clarification of Career and Academic Goals, and

(5)

General Coursework Assignments.

These identified themes underscore the critical aspects of coursework design and instructional practices that have notably enhanced non-traditional students’ educational experiences and contributed positively to their academic success.

The final open-ended question solicited specific recommendations from participants regarding potential coursework improvements to better support non-traditional learners. Five primary thematic recommendations emerged from this analysis:

(1)

Enhanced Practical and Interactive Learning,

(2)

Curriculum and Instruction Improvement,

(3)

Support and Accessibility Enhancements,

(4)

General Education Quality and Rigor, and

(5)

Flexible Learning and Scheduling.

These thematic recommendations illustrate concrete and actionable strategies suggested by participants to optimize coursework, highlighting areas in alignment with Universal Design for Learning (UDL) principles.

Explanations of each of these thematic findings is presented in detail with exemplar participant quotes (

Table 8. Qualitative code and exemplar quotes.

Research Question	Thematic Code	Exemplar Quote
Challenges	Online Learning Difficulties	“Sometimes the activities online did not have clear instructions…”
	Course Content and Delivery Issues	“One of the primary challenges was the volume of material to memorize…”
	Financial and Institutional Barriers	“College fees are very high.”
	Balancing Responsibilities	“I THINK THE ONLY CHALLENGE I HAD WAS FIGURING OUT HOW TO BALANCE MY SCHOOLWORK AND MY JOB.”
	Accessibility and Support Challenges	“A lot of the coursework was word-driven, and I have a disability that makes it hard to read.”
	Personal and Health-Related Interruptions	“I have faced some employment and health issues causing interruptions in my studies.”
Positive Impact	Interactive and Applied Learning	“Interactive, case-based learning had the most significant impact…”
	Supportive Academic Environment	“Positive interactions and support from professors made coursework enjoyable and educational.”
	Skill Development through Coursework	“Group projects helped enhance teamwork and organizational skills.”
	Clarification of Career and Academic Goals	“Introductory counseling courses improved understanding and clarified career goals.”
	General Coursework Assignments	“Assignments.”

) to vividly illustrate non-traditional learners’ experiences and perspectives. This comprehensive thematic exploration is designed to provide robust insights and practical recommendations, ultimately addressing the overarching research questions and contributing to a richer understanding of how UDL-aligned strategies impact non-traditional students’ experiences in higher education.

5.3. Interpretation of “Challenges” Themes

Participants in this study highlighted significant barriers encountered during their coursework. Online learning difficulties, including unclear instructions and delayed communication, were prominent and suggest a gap in the design of online course components. Issues concerning course content and delivery, such as overwhelming material volumes and outdated course content, underline the need for instructional improvement. Financial concerns reflect systemic institutional barriers that non-traditional learners often face. Moreover, balancing multiple responsibilities like employment and academics indicates that non-traditional learners face substantial challenges managing competing demands. Accessibility issues due to disability and interruptions stemming from health and employment circumstances were additional critical factors impacting learners’ academic progress.

These findings directly address the manuscript’s intent of identifying barriers encountered by non-traditional learners, emphasizing that higher education institutions must strategically alleviate these barriers to enhance academic success. While the present sample was largely high-achieving, qualitative findings illuminate barriers that may be particularly acute for non-traditional learners with GPAs below 3.0, such as financial strain, competing work–family obligations, and gaps in institutional support. Institutions might adapt these insights into more proactive supports for academically struggling students, including: targeted academic coaching and early-alert advising; flexible pacing or modularized courses to reduce withdrawal risk; and integrated financial and mental-health counseling. Such tailored interventions, grounded in the very barriers identified in our qualitative analysis, could extend the benefits of UDL to students facing greater academic risk.

5.4. Interpretation of “Positive Impact” Themes

Learners articulated clear appreciation for interactive and applied learning methods, which significantly contributed to knowledge retention and enjoyment. The supportive academic environment, fostered by dedicated professors and effective teaching methods, played a pivotal role in enhancing learners’ educational experiences. Additionally, coursework that explicitly targeted skill development, such as writing-intensive tasks and group projects, effectively enhanced critical skills needed in both academic and professional contexts. Counseling courses specifically assisted learners in clarifying academic and career goals, emphasizing the importance of tailored course offerings. General assignments were noted as beneficial, underscoring the positive role played by structured coursework.

These themes align with the manuscript’s exploration of the positive impact of Universal Design for Learning (UDL), validating that interactive, supportive, and skill-oriented course designs substantially benefit non-traditional learners.

5.5. Interpretation of “Recommendations” Themes

Participants offered targeted suggestions for improvement. Enhanced practical and interactive learning opportunities, including increased hands-on experiences and frequent interactive assessments, were strongly recommended. Improvements in curriculum and instructional methods, like regular curriculum updates and clearer instructions, were highlighted as crucial for better learning alignment and retention. Enhancing institutional support and accessibility, including responsiveness to student needs, emerged as a significant recommendation. Participants also stressed enhancing the overall quality and rigor of education through challenging and meaningful assignments. Finally, flexibility in scheduling and clearer guidelines, particularly regarding group assignments, was underscored as essential to better accommodate non-traditional learners’ unique needs.

These recommendations directly respond to the manuscript’s objective of offering practical insights and actionable recommendations for institutions aiming to optimize learner engagement and success among non-traditional populations.

5.6. Summary

Overall, the findings from these qualitative responses robustly address the manuscript’s research questions and purpose. They highlight specific barriers faced by non-traditional learners, illuminate successful instructional practices, and present actionable strategies for educational improvement. Implementing these thematic insights can guide higher education institutions toward creating supportive, inclusive, and effective learning environments, ultimately enhancing academic outcomes for non-traditional learners.

Convergence of Quantitative and Qualitative Findings

The quantitative and qualitative data were then integrated during the data collection process using a convergent parallel mixed-methods approach to ensure that the data is fully analyzed and interpreted. The integration of quantitative and qualitative data reveals important patterns that align with recent UDL research findings. While the PLSI factors showed limited association with GPA, qualitative responses strongly emphasized the value of flexible instructional approaches, supporting Almeqdad et al.’s (2023) meta-analytic finding that UDL effectiveness extends beyond traditional academic metrics. For example, 78% of positive impact responses related to “Interactive and Applied Learning” and “Supportive Academic Environment,” themes that align closely with UDL’s emphasis on engagement and multiple means of representation.

The most frequently cited challenges, “Online Learning Difficulties” and “Balancing Responsibilities”, correspond to areas where UDL implementation may be insufficient. This finding resonates with Winfield et al.’s (2023) observations about post-COVID challenges in distance education quality. The disconnect suggests that while institutions may be adopting some UDL principles, full implementation across all three domains (engagement, representation, expression) may be lacking.

Recommendations from participants strongly emphasized “Enhanced Practical and Interactive Learning” and “Flexible Learning and Scheduling,” directly supporting the theoretical rationale for comprehensive UDL approaches, even where quantitative measures did not reach statistical significance. This pattern aligns with Beck Wells’ (2022) findings that student perspectives on UDL often focus on accessibility and engagement benefits that traditional achievement measures may not capture.

Because this study was conducted at a single Hispanic-Serving Institution (HSI). The HSI context may have influenced outcomes in important ways. For example, dedicated multilingual learner services, bilingual tutoring, and culturally responsive programming, hallmarks of many HSIs, could help explain the comparatively strong performance of multilingual and English-language learners observed here. Future research should examine whether similar patterns hold in non-HSI settings or in institutions with fewer language-support structures to assess transferability.

6. Results and Conclusions

The results of this study have provided a comprehensive evaluation of the Personalized Learning Supporting Instrument (PLSI) using robust quantitative and qualitative approaches. The following section presents the findings from confirmatory factor analysis (CFA) and structural equation modeling (SEM), which were used to validate the theoretical structure and psychometric properties of the PLSI. Special attention is given to the stepwise refinement process that reduced the instrument from 25 to 12 items while maintaining content validity and model fit. Quantitative results are supplemented by a detailed summary of item elimination and its implications for measurement reliability and validity. The section concludes with a discussion of the relationship between UDL-aligned instructional practices and academic outcomes, as well as limitations and recommendations for future research.

6.1. Limitations and Methodological Considerations

This study has several important limitations that affect interpretation and generalizability, consistent with challenges identified in recent non-traditional student research (Brozina et al., 2024). The cross-sectional design prevents causal inferences about UDL effectiveness, capturing only a snapshot of student experiences rather than longitudinal impacts that Almeqdad et al. (2023) suggest are necessary for detecting UDL effects. The reliance on self-reported data introduces potential response bias, though the anonymous survey format and inclusion of open-ended questions aimed to elicit candid responses.

Sample limitations include recruitment from a single Hispanic-serving institution, which may limit generalizability to other institutional contexts. Ren (2023) noted similar contextual factors in online instructor experiences with non-traditional learners, suggesting that institutional culture and student demographics significantly influence UDL implementation effectiveness.

The instrument refinement process, while methodologically sound, resulted in a shorter measure that may have reduced sensitivity to detect relationships. The lower reliability for the Supporting Learner Variability (SLV) factor (α = 0.598) suggests this construct may need further development, particularly given King-Sears et al.’s (2023) emphasis on comprehensive UDL measurement approaches.

Additionally, the study did not control for instructor-level variables or specific UDL training, which could significantly influence implementation fidelity. The variability in how UDL principles are enacted across different courses and instructors represents an important confounding factor that future research should address through multi-level modeling approaches.

In regard to the final measurement model, it demonstrated strong psychometric properties and model fit, validating the underlying structure of the PLSI instrument. Item reduction was carried out judiciously to maximize model fit while maintaining conceptual breadth (Marsh et al., 2004; DeVellis, 2017). The lower reliability for the SLV factor suggests the need for further item development or refinement in future research. Additionally, while the four PLSI factors did not significantly predict GPA, these constructs may be more strongly related to other important educational outcomes such as engagement, persistence, or self-efficacy (Richardson et al., 2012). Overall, these results provide evidence that the PLSI is a valid and reliable tool for assessing perceived supports and barriers in higher education, with some limitations regarding the measurement of learner variability.

Achieving good model fit in CFA/SEM should always be balanced with maintaining the theoretical and practical meaning of each construct (Marsh et al., 2004; Kline, 2016). Over-reduction of items can artificially improve fit at the cost of content validity (DeVellis, 2017). The stepwise, theory-informed approach used here ensures the resulting instrument is both statistically robust and conceptually comprehensive.

6.2. Educational Equity Implications

The findings of this study have significant implications for educational equity policy and practice. The qualitative emphasis on flexibility and supportive environments suggests that UDL implementation may help institutions move beyond deficit-based models that view non-traditional students as academically “at-risk” toward asset-based approaches that recognize the diverse strengths and experiences these learners bring.

However, the high academic performance of study participants raises important questions about educational equity. If UDL-aligned environments primarily benefit already successful non-traditional learners, institutions must examine whether their implementation strategies adequately reach students facing the most significant systemic barriers. The underrepresentation of students with GPAs below 3.0 in this sample (4.38%) may indicate that current UDL approaches are insufficient for addressing the most profound educational inequities.

Furthermore, the variation in institutional support needs across different non-traditional demographics suggests that equity-focused UDL implementation requires differentiated approaches rather than one-size-fits-all solutions. Veterans may need trauma-informed pedagogical practices, while first-generation students may benefit more from explicit instruction in academic cultural norms.

6.3. Implications for Practice and Policy

Despite limitations, this study offers several actionable insights for higher education practitioners, building on recent research in UDL and non-traditional student support.

6.3.1. For Faculty Development

The strong qualitative emphasis on interactive and applied learning aligns with Almeqdad et al.’s (2023) finding that comprehensive UDL implementation requires all three principles working together. Professional development should focus on practical UDL implementation strategies, moving beyond awareness to systematic application across course design elements.

6.3.2. For Institutional Policy

The prominence of scheduling and accessibility challenges in qualitative responses, consistent with Beck Wells’ (2022) findings on virtual learning barriers, indicates that UDL implementation must extend beyond individual courses to institutional systems. This includes flexible scheduling options, multiple pathway programs, and comprehensive support services that address the holistic needs identified by Winfield et al. (2023) in their post-COVID analysis.

6.3.3. For Assessment Practices

The disconnect between quantitative GPA measures and qualitative reports of positive learning experiences suggests that institutions should consider alternative measures of success for non-traditional learners. Competency-based assessments, portfolio evaluations, and career-relevant project work may better capture the value these students derive from their education, particularly given their focus on practical skill application noted by Ren (2023).

6.3.4. Future Research

The partial support for UDL effectiveness, particularly around flexible instructional methods, warrants continued investigation using the longitudinal designs recommended by Brozina et al. (2024), multiple institutional contexts, and alternative outcome measures beyond GPA that capture the full spectrum of non-traditional learner success indicators

Several methodological and theoretical opportunities emerge from this study’s findings that warrant systematic investigation. The Supporting Learner Variability (SLV) construct demonstrated lower reliability than desired (α = 0.598), indicating a need for item refinement and expansion. Following established psychometric principles (Nunnally & Bernstein, 1994), future research should develop additional SLV items that better capture the complexity of how non-traditional learners experience variability in their educational contexts. This could include items addressing temporal flexibility, multiple role management, and adaptive learning preferences specific to adult learners.

The current study’s focus on GPA as the primary outcome measure may have limited the detection of UDL’s broader educational impacts. Richardson et al. (2012) demonstrated that academic success encompasses multiple dimensions beyond grades, including engagement, persistence, and satisfaction. Future investigations should expand the dependent variable framework to include these alternative outcomes, which may be more sensitive to UDL interventions and more meaningful for non-traditional learner populations who often prioritize skill acquisition and practical application over traditional academic metrics.

The generalizability of the refined PLSI model requires validation across diverse institutional contexts and student populations. Bandalos and Finney (2019) emphasize that psychometric properties established in one sample may not transfer to different settings without empirical confirmation. Cross-validation studies should examine the model’s stability across different types of institutions, varying levels of UDL implementation, and diverse non-traditional learner demographics to establish broader applicability.

An important methodological consideration involves survey design effects that may have influenced results. The placement of SLV items at the survey’s conclusion potentially introduced response fatigue, possibly contributing to the observed lower reliability and unexpected associations. Participant fatigue in online surveys can lead to satisficing behaviors, including rapid or careless responding that compromises data quality. Future studies should systematically vary item ordering and examine whether survey position effects influenced the SLV construct’s performance, while also implementing attention check items and response time monitoring to identify and address satisficing behaviors.

These research directions collectively address both the psychometric refinement of measurement tools and the substantive understanding of UDL effectiveness for non-traditional learners, positioning future work to provide more comprehensive and methodologically robust evidence for evidence-based practice in higher education.

6.4. Closing

This mixed-methods study contributes to our understanding of how Universal Design for Learning principles impact non-traditional learners in higher education. While quantitative analyses revealed limited direct relationships between UDL implementation and academic performance as measured by GPA, the integration of qualitative findings suggests that UDL principles address critical needs of adult learners in ways that traditional metrics may not capture.

The study’s most significant contribution lies in demonstrating the complexity of measuring UDL effectiveness for non-traditional populations. These students’ educational experiences are shaped by multiple competing demands and diverse motivations that extend beyond traditional academic achievement. The strong qualitative emphasis on flexibility, practical application, and supportive learning environments aligns with UDL’s theoretical foundation, even where statistical significance was not achieved.

While this study reveals both promise and limitations in UDL’s effectiveness for non-traditional learners, it underscores the critical need for educational innovation that moves beyond one-size-fits-all approaches. The significant relationship between instructional flexibility and academic performance, combined with qualitative evidence of student appreciation for adaptive teaching methods, suggests that inclusive pedagogical frameworks like UDL represent important steps toward educational equity. However, the persistence of achievement gaps for certain populations (particularly first-generation students) indicates that pedagogical innovation must be accompanied by broader institutional transformation addressing systemic barriers to educational access and success.