Implementation fidelity of universal design for learning and effects on student achievement engagement and belonging

Abstract

This report examines the implementation of the Universal Design for Learning (UDL) framework in various educational settings, with a focus on accessibility modifications and their impact on learning outcomes. A mixed-methods longitudinal approach was employed to gather data from 2,473 learners across 87 educational facilities. Assessing the extent to which the UDL framework encourages more inclusive and adaptive instructional designs, the framework supports flexible learning environments that prioritize embracing more inclusive and adaptive instructional approaches. The study found that institutions that fully engaged all three UDL framework principles, means of engagement, representation, and action/expression, recorded an increase of 37.4% in overall learner performance and 42.8% in the performance of disengaged learners. The study found a significant relationship between UDL implementation and positive outcome differences (p < 0.001) in UDL performance principles, which was substantially more pronounced in the case of engaged learners than in disengaged learners. Empirical evidence suggests that five critical factors, as revealed by qualitative data, facilitate successful integration: strong administrative support, ongoing professional development, comprehensive technology availability, collaborative pedagogy, and flexible data use. These empirically validated routes of action assist the application of UDL principles and broaden the integration of equitable access within the educational field. Within the context of participating institutions, UDL integration was associated with improved educational environments and appeared to increase access for students experiencing participation barriers. These findings suggest that flexible, inclusive UDL approaches may support improved access and attainment across diverse student populations in settings with adequate implementation support, though further research is needed to confirm generalizability across different educational contexts and timeframes.

Introduction

Globally, education systems continue to face challenges in embracing learner diversity while maintaining high-quality instruction. The Universal Design for Learning (UDL) framework offers a promising solution by creating flexible and accessible learning environments tailored to diverse needs. Originating from the architectural concept of universal design (UD), UDL is grounded in scientific research and provides a proactive educational approach that addresses learner variability through three core principles: multiple means of engagement, representation, and action/expression^1–4. This philosophy aligns with the 2021 UNESCO Education 2030 Agenda, which advocates for inclusive, equitable, and high-quality education.

Unlike the traditional approach, which primarily accommodates disabilities reactively, Universal Design for Learning (UDL) emphasizes proactive design for all learners from the outset. It takes into consideration the Cognitive Load Theory along with the brain’s neural networks. It focuses on the brain’s affective, recognition, and strategic networks, as well as the importance of well-designed instruction^1,5,6. With an increase in the diversification of instructional materials and educational training systems, UDL views variability in engagement and content processing as the norm, rather than the exception^7,8. The use of UDL is skyrocketing in schools, from elementary to college levels. This rise is attributed to new laws, improved technology, and increased awareness of the need for fair teaching methods^9,10. However, significant questions remain about how UDL works, how true it remains to its goals, and what factors affect its outcomes¹¹. There remains a gap between UDL ideas and their actual implementation, as evidenced by numerous recent studies^8,11. Since its introduction in the late 1990s, the main ideas behind UDL have evolved significantly. Rose and Meyer² first outlined the three main rules of the framework, which remain the same, although their application has expanded. Initially designed to support students with disabilities, UDL now benefits all types of learners⁹. Experts such as Espada-Chavarria⁸ and Creaven¹² discuss how UDL helps make schools fair for everyone. Creaven, in fact, advocates for staying close to the fundamental goals of ensuring everyone is included, both in their personal experiences and in their social circles¹³.

While UDL has gained widespread support, it has also faced critical scrutiny. Murphy¹⁴ questioned its empirical foundations, while Boysen¹⁵ examined parallels between UDL and learning styles theory. However, a systematic review and meta-analysis by Almeqdad et al.¹⁶ confirmed the effectiveness of UDL while identifying areas that require further investigation. These scholarly debates have ultimately contributed to strengthening the conceptual and evidence base of the UDL framework.

UDL implementation is critical in the higher education context. According to Espada-Chavarria et al.⁸, implementing Universal Design for Learning (UDL) is recommended to address climate change issues within the university environment. According to Barrera and Moliner¹⁷, students with autism spectrum disorder were offered a speaking opportunity. It was about how UDL approaches support learning in higher education contexts. The literature discussing implementation often forgets this student perspective.

In several studies, the development of professors proved to be an important factor. According to research conducted by Kim et al.¹⁸ in their study, ‘Faculty Development Programs with a Diversity Focus,’ UDL training improved instructors’ abilities to create more inclusive learning environments. According to research conducted by Olivier and Potvin¹⁹, the implementation of structured faculty development programs has proved beneficial in the implementation of UDL. Furthermore, Azam et al.²⁰ investigated self-study as a professional learning approach for inclusive teacher educators. Hromalik et al.²¹ presented a comprehensive UDL academy at a community college, which successfully increased faculty knowledge and implementation of UDL principles.

Higher education increasingly integrates technology with UDL principles. Fleming²² examined how technology-enabled choices increased students’ sense of belonging, and Ismailov and Chiu²³ studied UDL and synchronous online education from a self-determination theory perspective. Garrad and Nolan’s²⁴ study focused on embedding UDL in online studies. The COVID-19 pandemic accelerated research in this area. Yang et al.²⁵ provided a systematic review of universal design in online education. Recent literature indicates that calls for the application of UDL are appearing in an increasing number of disciplines. Casebolt and Humphrey²⁶ applied UDL principles to public health courses, whereas Reyes et al.²⁷ analyzed chemistry education resources within a UDL framework. Nieminen and Pesonen²⁸ explored the applications of UDL in undergraduate mathematics from two perspectives. Discipline-specific studies identify challenges and opportunities within a particular content area, complementing broader research on implementation and policy.

Numerous studies have investigated the application of UDL for diverse students. Students with autism spectrum disorder have been the focus of Barrera and Moliner¹¹. More recently, Hyatt and Owenz²⁹ used UDL and artificial intelligence for disabled students. Owenz and Cruz³⁰ examined how Universal Design for Learning (UDL) is applied when creating tests aimed at reducing test stress. They demonstrated that UDL can be adapted to meet the diverse needs of learners while still benefiting all. New ways to use UDL keep coming up. For instance, Sofianidis et al.³¹ demonstrated how UDL rules can be combined with asking-based learning, other real-world games, and new real-world technologies. This approach to learning suggests that diverse students, including those with disabilities or autism, may benefit from engaging learning activities when the design encourages their cognitive and affective participation. Saborío-Taylor and Rojas-Ramírez³² examined how AI can aid in the growth of UDL tools, demonstrating how the plan can evolve with new school tools and approaches.

Despite these advancements, significant challenges remain in implementing UDL effectively. Zhang et al.¹¹ identified key barriers through a systematic review, including insufficient teacher training, a lack of resources, misaligned assessments, and broader institutional obstacles. In response, Fovet³³ proposed an ecological approach to UDL implementation, one that considers systemic influences across various levels of the education system. Additionally, research by Timuș et al.³⁴ compared faculty perceptions across NCAA and EU institutions, revealing differences in perceived institutional support and available resources for the adoption of UDL.

Recent literature has increasingly focused on policy considerations. A 2023 study on UDL policy in tertiary education in Ireland found that Irish institutions are not yet ready for the commitment to comprehensive implementation, as noted by Healy et al.³⁵. Liasidou and Liasidou³⁶ examined power inequities within the context of higher education disability accommodations and advocated for a more transformative response. Esoteric analysis helps in determining how closely aligned policy analysis and practices are in education. According to several studies, the perspectives and attitudes of faculty are important. Redshaw and Deehan³⁷ found academics support theoretically and pedagogically inclusive education, but there is a gap regarding reported practices. Shaw³⁸ employed Q-methodology to investigate lecturers’ attitudes toward the inclusion of students with disabilities. Gadsden and Goegan³⁹ offered important insights on barriers and facilitators to implementation from post-secondary instructors who identified as people living with learning disabilities themselves.

Research on the effects of Universal Design for Learning (UDL) implementation continues to grow. While Almeqdad et al.¹⁶ reported positive impacts of gamification on student learning, they also noted methodological limitations in existing studies. Manly⁴⁰ investigated the influence of varied content formats in adaptive learning activities using panel data analysis, and Sukhera⁴¹ introduced a participatory framework that supports equitable assessment through UDL-informed precision education strategies. A recurring theme in the literature is the importance of implementation fidelity. Estefan et al.⁴² emphasized the distinction between inclusive and equitable pedagogy, arguing that transformative UDL practice must address structural inequalities. Similarly, Hills et al.⁴³ examined enablers and obstacles to UDL adoption in higher education, identifying implementation fidelity as a critical concern. Collectively, these studies underscore the need to move beyond surface-level adoption and commit to more consistent and meaningful application of UDL principles.

Despite notable progress, important gaps remain in UDL research. A comprehensive review of the literature reveals a lack of focus on how implementation fidelity influences educational outcomes. Although numerous studies explore the potential of UDL in various contexts, few employ rigorous methods to evaluate how the quality of implementation affects learner achievement^{8,11,16,44,45}. Questions persist regarding which core UDL components must be implemented with strict fidelity and which can be adapted contextually without compromising effectiveness. King-Sears et al.⁴⁶ noted a significant limitation in meta-analyses: inconsistent implementation approaches make comparisons difficult.

Another key limitation is the predominance of small-scale studies, often limited to a single course, classroom, or institution. This narrow scope limits our understanding of scalability and the system-wide conditions necessary for successful UDL implementation^{8,26,34,47,48}. Cross-institutional comparisons are rare, despite evidence from Behling and Posey⁴⁹ suggesting that institutional context has a significant impact on implementation outcomes. Additionally, Healy et al.³⁵ highlight the need for broader, system-level implementation models that align policy and practice across the education sector.

The research area relating technological innovation to UDL principles is underexplored. Although interest is growing in enhancing UDL with technology, many of the approaches to integrating technologies remain disjointed and lack coherent and systematic theoretical grounding^{22,24,25,50,51}. Innovation in university classrooms through the UDL methodology has gained significant attention^4,48. Saborío-Taylor and Rojas-Ramírez³² note the rich potential for artificial intelligence to enhance the implementation of UDL further, but empirical investigations of these uses have only just begun. According to Hyatt and Owenz²⁹ and Morgan⁵², new technologies will have transformative possibilities for implementing UDL. They note, however, that no complete model for this currently exists. According to a recent study conducted by scholars Sofianidis et al.³¹ and Cope et al.⁵³, several practical applications of technology have been identified. However, it is also crucial to maintain a systematic approach to selecting and utilizing technology. Most importantly, this should be done within a UDL Framework.

There is also a need for comprehensive implementation models that involve institutional policies, faculty development, and classroom practices. Frameworks that consider UDL implementation as a multi-level system are underdeveloped, although individual implementation strategies are well documented^21,33,34. Our ecological framing is consonant with scholarship that conceptualizes inclusion as a system property distributed across classroom, institutional, and policy layers⁵⁴. This literature emphasizes policy enactment and organizational sensemaking, distributed leadership, and the mediating role of local routines and resources in translating equity commitments into classroom practice. It underscores that durable inclusion arises from coordinated alignment across levels rather than isolated classroom initiatives^55,56.

Research on organizational change and innovation diffusion has documented predictable sources of resistance at both institutional and personal levels, including cultural inertia, competing priorities, perceived loss of autonomy, and risk aversion⁵⁷. Change frameworks emphasize the importance of a clear vision, early wins, stakeholder engagement, and capacity building. Technology-adoption studies further demonstrate that perceived usefulness, ease of use, and supportive climates significantly influence implementation trajectories. These insights inform our interpretation of fidelity growth curves and sustainability patterns⁵⁸.

A convergent line of work positions collaborative professionalism, encompassing professional learning communities, peer observation, co-planning, and shared inquiry, as a critical mechanism for institutional improvement, specifically for strengthening educational inclusion. Our results on faculty collaboration and spillover effects align with this tradition, suggesting that collegial structures amplify the effects of administrative support and professional development^59,60.

Faculty development is significant but poorly integrated with more extensive implementation measures. Further elaboration is needed to substantiate assertions regarding the relationship between these practices and college or university policies, as well as student outcomes. Kim et al.¹⁸, Olivier and Potvin¹⁹, and Azam et al.²⁰. According to Gadsden and Goegan³⁹ and Shaw³⁸, while faculty perspectives play crucial roles in implementation success, systems to mitigate faculty concerns are scarce.

While the literature strongly emphasizes the implementation of Universal Design for Learning (UDL), there is relatively limited focus on its social and affective dimensions. Much of the current research focuses on cognitive accessibility, often overlooking the importance of social belonging and emotional engagement, both of which are vital to the learning experience^{12,17,23,61,62}. Notably, UDL principles have demonstrated potential in adult education settings, where learner variability introduces unique challenges and opportunities^8,63. Cruz and Owenz³⁰ highlighted the significant influence of test anxiety and emotional factors on student performance, yet little has been done to address these elements through a UDL-informed lens.

Spaeth and Pearson⁶⁴, along with Liasidou and Liasidou³⁶, advocate for greater attention to neurodiversity and power dynamics within educational environments, suggesting that UDL must move beyond technical implementation to encompass emotional and relational aspects of learning. Redshaw and Deehan³⁷ further assert that faculty perspectives on diversity shape their implementation practices, but structured models to address these social and affective dimensions remain scarce across educational contexts.

Methodologically, assessing the effectiveness of UDL poses another critical gap. Almeqdad et al.¹⁶ critique the limitations of many existing studies, while Manly⁴⁰ demonstrates the benefits of employing more advanced analytical methods. Sukhera⁴¹ offers an innovative assessment framework aligned with UDL and precision education but acknowledges the need for further validation. Estefan et al.⁴² argue that prevailing evaluation methods may not fully capture UDL’s contributions to educational equity. Similarly, Makuve⁶⁵ advocates for broader assessments that encompass governance and institutional factors in addition to pedagogy.

These challenges hinder the development of clear, evidence-based recommendations for UDL best practices across diverse contexts. While UDL practitioners are often adaptive mentors who iterate based on evolving best practices, a lack of systematic implementation persists across subjects. Although Nieminen and Pesonen²⁸, Reyes et al.²⁷, and Casebolt and Humphrey²⁶ report promising results in Mathematics, Chemistry, and Public Health, respectively, few studies have extended these applications across a broader range of disciplines. Models proposed by Machkour et al.⁶⁶ integrate UDL with diagnostic assessment processes, while Ramos Aguiar et al.⁶⁷ present tailored UDL applications for specific learner populations. These contributions highlight UDL’s flexibility but also underscore the need for more robust frameworks that ensure alignment with core UDL principles in diverse contexts.

This study fills holes in past work by giving a complete look at how UDL is used in different school settings. In detail, it provides methods for evaluating how well UDL is followed, using a framework that encompasses both setup and steps¹¹. With a mix of ways and long-term plans used on 2,473 students in 87 places, this work gives a wide and in-depth view of how well UDL works and how it changes how students learn. By moving beyond the narrow views of studies in one place⁸, this approach presents a comprehensive picture of what is needed for effective UDL use.

We develop and test a comprehensive model for the use of tech-rich UDL that aligns technology tools with teaching concepts. This paper examines how current approaches to integrating UDL are fragmented^22,25. We will then create a use plan, based on our findings, that incorporates a system with multiple levels: rules (schools), people (teachers), and methods (classrooms), all interconnected. According to Fovet³³ and Timuș et al.³⁴, this descriptive framework will help university leaders and practitioners to take action. Embeds Social-Emotional Dimensions: We explicitly incorporate social belonging and affective engagement into our implementation model, providing concrete strategies to address these often-overlooked aspects of inclusive learning environments^{12,17,68–70}. Our study makes multiple original contributions to the field. To begin, the Implementation Fidelity Model is an evidence-based instrument that helps measure and enhance the quality of UDL implementation in various contexts. Moreover, the methodological weaknesses of earlier studies are addressed by the evidence of the implementation effects measured by a large-scale longitudinal design. The integrated technology-pedagogy model enables teachers to effectively utilize digital tools and apply UDL principles in their teaching. Fourth, the whole implementation framework links theory and practice at all levels of the educational system. In conclusion, by laying such explicit emphasis on the social-emotional dimensions, UDL becomes relevant not only for the social-emotional challenges of learning but also for the cognitive.

Looking ahead, the remainder of this paper is organized as follows. Section 2 presents the research methodology, which describes the mixed-methods longitudinal research design, theoretical framework integrating UDL principles with implementation science and ecological systems theory, participant selection criteria, data collection procedures, and analytical approaches used in this investigation. Section 3 presents the comprehensive results, including findings on implementation fidelity measures, student achievement outcomes, critical implementation factors, and the empirically derived implementation framework that emerged from our analysis across the 87 participating institutions. Section 4 provides the conclusion, discussing theoretical and practical implications of our findings, study limitations, recommendations for educational policy and practice, and directions for future research in UDL implementation science.

Research questions

Building on the integrated theoretical framework and identified gaps, the study addressed the following questions:

• RQ1. To what extent is implementation fidelity of UDL (structural, process, outcome dimensions) associated with student outcomes, achievement, engagement, and social belonging, over 24 months?

• RQ2. How do fidelity trajectories and corresponding student outcomes vary by institutional type (K–12 vs. postsecondary) and support level (administrative commitment, professional development, technology infrastructure)?

• RQ3. Which institutional and contextual factors (administrative support, professional development, collaborative pedagogy, data use) best predict high-fidelity implementation and its sustainability?

• RQ4. Does the quality of technology integration moderate the association between UDL fidelity and student outcomes, beyond mere tool adoption?

• RQ5. What mechanisms, as perceived by educators and students, explain observed quantitative patterns, and how do these mechanisms illuminate the conditions under which UDL is most effective?

Research methodology

This study employed a comprehensive mixed-methods longitudinal research design to investigate the implementation of the Universal Design for Learning (UDL) framework across diverse educational contexts and examine its impact on student achievement and engagement. The methodological approach integrates quantitative measurement of implementation fidelity and student outcomes with qualitative exploration of implementation processes and contextual factors.

Research design and theoretical framework

The study employed a convergent parallel mixed-methods design, combining both quantitative and qualitative data collection methods. Both types were collected simultaneously. UDL principles by¹, the theory of implementation science, and the theory of ecological systems. This indicates that changes in education occur at multiple levels, and the implementation of UDL involves a complex web of relationships that encompass personal, school, and policy levels.

Figure 1 outlines the conceptual model of the study. It shows how the three main ideas shape the UDL use steps. UDL’s basic thoughts on entering, sharing, and acting. UDL ideas, science, and systems thinking join to create this model. The main point is to check how good UDL tools are. This method enables us to observe the practicality of ideas when they are put into practice. The key UDL points on joining in, showing stuff, and doing things help us teach all.

Fig. 1.

Integrated theoretical framework for UDL implementation research.

Implementation science is about applying new teaching methods to change how people behave. The key aspects of this are staying true to the plan, making adjustments as needed, and maintaining momentum over time. These help us understand why it is key. The ecological systems view provides a framework for understanding how these changes occur on multiple levels. This view recognizes that changes occur in small areas, such as classrooms, in larger areas, like schools, and even in broad areas, like government regulations.

As illustrated in Fig. 1, the integration model highlights the mediating role of implementation fidelity in the relationship between the core UDL principles and student outcomes. Achieving meaningful application of these principles involves more than simply adopting UDL techniques; it demands critical reflection on how these principles are enacted within specific contextual constraints and opportunities.

The lower portion of Fig. 1 highlights how educational systems operate across multiple nested levels, including individual, institutional, and broader policy contexts. These layers are interdependent, and the challenges at each level have a mutual influence. Effectively distinguishing between these interconnected factors is vital for informing both policy and practical implementation efforts.

This multi-level, ecological perspective is fundamental for understanding why UDL implementation outcomes vary across different settings. It also forms the basis for designing comprehensive strategies that address systemic barriers and leverage facilitators at every level. According to this framework, successful UDL implementation hinges on coordinated alignment across system layers, with fidelity of implementation serving as the key link between adoption and positive educational outcomes.

Notably, the framework acknowledges that implementation quality will vary by context, and that understanding this variability is crucial for developing scalable and sustainable implementation models. From this ecological viewpoint, factors such as institutional culture, resource availability, faculty preparedness, policy alignment, and administrative support all interact to shape the success of UDL initiatives.

Participants and settings

A selected method was used to pick people from a broad mix of possible choices. The study involved 2,473 students and 342 teachers from 87 institutions spanning K–12 (elementary through high school) and a small subset of postsecondary sites (community colleges and teacher education programs). These schools demonstrated a wide range of UDL use, from early adopters to those far ahead, illustrating the diverse ways it is applied. To demonstrate different ways UDL is put into use, people came from city, town, and rural areas from various places.

Student participants had different traits, such as:

• Varying skill levels

• Different money backgrounds

• Various languages

• Different past school experiences

Faculty participants were:

• New interns

• Entry-level workers

• Experienced people

• Trainers skilled in UDL

The places they came from also differed in:

• Size

• Available resources

• Admin help for inclusion

• Tech setup

This wide and well-planned mix allowed us to examine how well UDL worked in various types of school settings.

Participant selection within institutions

We employed a purposive, criterion-based sampling strategy to select participants within each institution. Instructor selection prioritized variability in (a) UDL exposure (none/introductory/comprehensive), (b) teaching experience (novice/intermediate/expert), and (c) disciplinary area, as verified by institutional liaisons. Instructors were invited via email to participate in information sessions, and their participation proceeded upon informed consent. Classroom selection targeted courses taught by participating instructors during the study waves; where multiple sections existed, we selected sections to maximize schedule and demographic diversity. Student participation was inclusive of all enrolled learners in selected classes, with opt-out procedures in place and parental/guardian consent required for minors. To mitigate selection bias within institutions, we monitored uptake by department and experience level, and when imbalances emerged, we sought additional instructors to restore heterogeneity. This protocol ensured transparency in identifying individual participants within the broader institutional sample.

Research sites and implementation background

The 87 participating institutions spanned urban, suburban, and rural contexts across multiple administrative regions within the study jurisdiction. In line with IRB and institutional confidentiality requirements, we report regional rather than institution-identifying details. Sites included K–12 schools (elementary through secondary) and a subset of postsecondary providers.

UDL adoption was not simultaneous nor motivated by a single driver. Institutions joined the project in staggered cohorts reflecting authentic system conditions:

• Cohort A (project inception): sites with pre-existing inclusion or accessibility initiatives;

• Cohort B (~ 6 months): sites aligning UDL with emerging policy or accreditation requirements;

• Cohort C (~ 12 months): sites entering via faculty-led or department-level innovation.

Baseline UDL maturity varied from novice (limited awareness and ad hoc accommodations) to early adopters (pilot use of UDL-aligned practices) to advanced (coordinated use of UDL principles with some institutional support). Entry rationales, documented in site logs, included equity and inclusion strategies, compliance (policy/accreditation), instructional quality improvement, and technology-enabled pedagogy. This heterogeneity explains the variability observed in implementation fidelity trajectories and outcome profiles. Where relevant, analyses adjust for cohort and baseline maturity to enhance causal interpretability.

Research design and data collection procedures

The study was conducted over a 24-month period, during which data were collected and analyzed. As shown in Fig. 2, a mixed-methods approach was employed, with components developed in parallel. This means we gathered both numerical and detailed data simultaneously. Using this plan helps maintain the study’s timeline, which is crucial for understanding how UDL is implemented step by step.

Fig. 2.

Mixed-methods longitudinal research design for UDL implementation study.

Figure 2 identifies five key points to check, beginning with initial information gathering and revisiting every six months. These times were set to examine both the immediate results of starting and the lasting impact of UDL methods. Using this plan that takes into account time, we gain a better understanding of how things unfold at multiple stages, identifying key times when we might need extra support or a notable boost.

As illustrated in Fig. 2, the quantitative component of the study encompassed four core measurement domains, allowing for a comprehensive evaluation of both implementation processes and outcomes. Student achievement was assessed using standardized assessments for cross-context comparison, alongside course-specific performance indicators aligned with discipline-based learning objectives. Student engagement was evaluated using a combination of behavioral observation protocols and validated self-report instruments, capturing its multifaceted nature.

Fidelity of implementation was assessed through structured observations and detailed checklists, allowing for the evaluation of adherence to the three UDL principles: engagement, representation, and action/expression. The analysis of institutional factors focused on contextual elements, including resource availability, administrative support, and technological infrastructure, all of which are known to influence effective implementation. The qualitative aspect was a good background to complement and enlarge the quantitative results. The interviews with the faculty provided information on the implementation experience, identifying both successes and difficulties. UDL practices were studied in student focus groups to determine the extent to which they influenced accessibility and participation. Real-time instructional dynamics and social interactions were observed through ethnographic observations, and document analysis of institutional policies and training materials provided a deeper insight into the formal supports for implementation. The integration phase involved synthesizing results from both data strands using advanced analytical methods, such as joint display and meta-inference methods, which highlighted areas of agreement and disagreement. The data were also nested, and thus, multi-level modeling was used to examine the relationship between variables of implementation and outcomes at individual, classroom, and institutional levels. This combined discussion was used to inform the development of evidence-based frameworks and models, facilitating the future implementation of UDL in various educational settings.

Measures and instruments

The researchers employed a combination of well-established tools and innovative measures to capture the complex impact and implementation of Universal Design for Learning (UDL). Student performance was assessed using context-appropriate standardized assessments, course-specific performance indicators, and authentic assessment portfolios that reflected real-world learning outcomes.

To evaluate student engagement, the study utilized the Student Engagement Scale, behavioral observation protocols, and learning analytics gathered from educational technology platforms. Social belonging was measured using a modified version of the Psychological Sense of School Membership scale, tailored to fit diverse educational settings.

Implementation fidelity was assessed using a newly developed multidimensional framework that addressed structural, process, and outcome-level indicators. This framework combined systematic classroom observations, educator self-reports, and artifact analysis to evaluate the extent to which UDL principles—engagement, representation, and action/expression—were integrated into teaching practices. Instruments developed by the UDL Implementation and Research Network were also employed to assess faculty knowledge, attitudes, and confidence, supplemented by implementation confidence scales and knowledge-based assessments.

Institutional factors were measured through comprehensive organizational surveys that examined administrative support, availability of resources, professional development opportunities, and policy alignment. Evaluation of digital infrastructure and technology usage analytics revealed an upward trend in the integration of classroom technology by teacher educators.

Qualitative instruments included semi-structured interview protocols for faculty and administrators, focus group guidelines for student participants, and ethnographic observation frameworks. These tools captured the lived experiences of implementation, revealing the contextual factors and classroom processes that influenced the success of UDL strategies.

Measurement instruments and source citations

The Multi-Dimensional UDL Implementation Fidelity Framework (developed in this study; see Sect. 2.5 and Fig. 3) was operationalized via (a) structured classroom observation rubrics aligned to the three UDL principles; (b) artifact reviews (lesson plans, assessments, student work) to evidence structural and process indicators; and (c) student experience surveys indexing outcome fidelity (engagement, belonging, perceived access).

Fig. 3.

Multi-dimensional UDL implementation fidelity framework.

We administered instruments adapted from the UDL Implementation and Research Network (UDL-IRN) to assess knowledge of UDL principles, attitudes toward inclusive design, and confidence in implementation. Subscales covered engagement, representation, action/expression, and general inclusion beliefs; items used 5- or 7-point Likert formats with higher scores indicating more substantial alignment. Internal consistency for primary subscales met accepted thresholds (α and ω ≥ 0.80).

Engagement was measured via a validated student engagement scale (behavioral, emotional, and cognitive components), augmented by behavioral observation protocols and learning analytics (LMS interaction patterns). Social belonging was assessed using a modified Psychological Sense of School Membership instrument, adapted for both K–12 and postsecondary contexts. All instruments underwent psychometric checks (confirmatory factor analysis, measurement invariance across key subgroups) prior to primary analyses; reliability estimates (α, ω) were acceptable across waves.

Student achievement was evaluated through combined standardized assessments for cross-site comparability and course-embedded indicators aligned with discipline outcomes, harmonized to standard scales for analysis. For longitudinal modeling, repeated measures were standardized within site-wave.

Implementation fidelity framework

Among the most significant developments of the methodology used in this study was the creation of an extensive framework for implementation fidelity that extends beyond compliance measures to consider UDL implementation. Figure 3 outlines the structure of this framework, which builds upon the multidimensional implementation fidelity framework. This framework describes the relationship between the structural implementation of UDLs and their success in terms of process or outcome. According to Fig. 3, three dimensions relate to fidelity assessment, and they are closely related to each other, although they are distinct in nature. This dimension of the structural fidelity assesses the presence of UDL components. This involves determining whether there is a systematic assessment of the availability of different resources for learning and engaging students.

Figure 3 presents the Process Fidelity Dimension, which focuses on the quality of UDL implementation practices. The mere presence of UDL components does not guarantee effective execution. This dimension assesses whether instructional design practices promote coherent and meaningful learning experiences, and whether there is a structured approach to evaluating the thoroughness of applying UDL principles. It also includes a student-centered implementation assessment, which looks at the extent to which educators demonstrate responsive, individualized instruction that reflects an authentic understanding of learner variability.

Additionally, this dimension assesses ongoing instructional improvement, recognizing that high-quality implementation is not a static state. It emphasizes the importance of continuous teacher reflection and adaptation of strategies based on student performance and feedback.

The Fidelity of Outcomes Dimension, also illustrated in Fig. 3, examines whether the intended goals of UDL have been realized. High-quality implementation should result in measurable improvements in students’ experiences and learning outcomes. Academic achievement is evaluated to determine whether intended learning objectives are being met across diverse learner groups. Student engagement is assessed through both behavioral indicators and subjective self-reports of participation and motivation.

Finally, the inclusive outcomes assessment evaluates whether UDL practices contribute to equitable academic success and foster a sense of belonging, particularly among historically marginalized student populations. This ensures that implementation is not only practical but also equitable.

The measurement methods portion of Fig. 3 illustrates the comprehensive approach taken for data collection, enabling a reliable and valid assessment of all three fidelity dimensions. Multiple trained observers administer the structured rubrics used for classroom observations to enhance inter-rater reliability and ensure coverage of implementation practices. The analysis of the documents examines lesson plans, assessments, and student products to provide objective evidence of the implementation components. The quality of the components can also be assessed. Faculty self-assessment tools allow educators to share their perspectives on their efforts to implement while building decoration capacity to support continuous improvement. Surveys and focus groups that gather student experiences and learner voice evidence, indicating implementation effectiveness as seen by the recipient. The use of learning analytics yields objective measures of behaviour which supplement the observational and self-reports.

The fidelity scoring system, as represented in Fig. 3, enables the categorization of implementation quality across institutions to provide feedback for formative improvements and summative research purposes. The four-level system recognizes that effective UDL implementation exists on a continuum. It also recognizes that different contexts may require different approaches, as long as the core principles are maintained. The composite scoring method consistently combines all three fidelity dimensions. However, they can be weighted contextually to reflect the different implementation priorities and constraints across settings.

Effective implementation of UDL requires adherence to core principles and their adaptation to local contexts. The model integrates a set of measurement plans, including structured classroom observations with validated rubrics, evaluations of instructional resources and assessments, educator self-report tools, and student experience surveys. Each principle of UDL is evaluated using various indicators to reflect the differences in implementation across various situations and learner groups. It is essential to note that the framework acknowledges that high-fidelity implementation can be implemented in various ways, depending on the environment, while still adhering to the primary principles of Universal Design for Learning.

Data analysis procedures

The quantitative analysis was used as the initial stage of the explanatory mixed-methods study, in conjunction with a qualitative study. Such a design facilitated a multi-layered interpretation of the data, combining the two strands to provide a more detailed view of the findings. In the quantitative part, multilevel techniques were used to capture the nested nature of the data, which involved students within classrooms and classrooms within institutions. Hierarchical Linear Modeling (HLM) was used to investigate the association between the implementation fidelity of UDL and student outcomes, along with a control of both the individual and institutional level factors. Growth curve modeling was employed to assess longitudinal student achievement and engagement over 24 months.

Furthermore, latent profile analysis was conducted on implementation fidelity scores to identify distinct institutional patterns in the application of UDL. These profiles were subsequently incorporated into multilevel models to predict differential student outcomes based on specific implementation typologies. Effect sizes were calculated using Cohen’s conventions, with an emphasis on practical significance, especially given the large sample size. To strengthen causal inferences and reduce selection bias, propensity score matching was also employed.

Qualitative data were analyzed using both deductive and inductive coding methods. Initial codes were informed by UDL theory and literature on implementation science, while emergent themes were developed through constant comparative analysis. The NVivo software supported the coding process and facilitated pattern recognition. Inter-rater reliability was established with an agreement rate of over 85% across independently coded subsets. Quantitative and qualitative findings were synthesized using joint display techniques, meta-inference, and convergent synthesis to identify areas of alignment and divergence. Targeted follow-up analyses were conducted to explore inconsistencies, many of which revealed the nuanced complexity of implementation in real-world settings. The integrated analysis led to empirically grounded recommendations for UDL implementation, combining statistical rigor with a contextual understanding of educational practice.

Assumption checks and supplemental t-tests

In addition to the primary multilevel models (HLM) and growth curve analyses, prespecified pairwise contrasts were examined using independent-samples t-tests as complementary evidence. Prior to each t-test, distributional assumptions were evaluated: normality via Shapiro–Wilk tests, skewness/kurtosis diagnostics, and Q–Q plots; homogeneity of variances via Levene’s test. When variance homogeneity was violated, Welch’s unequal-variances t-test (Welch–Satterthwaite df) was used. If normality was seriously violated or influential outliers were detected, nonparametric sensitivity analyses using Mann–Whitney U and permutation/bootstrapped t-tests (10,000 resamples) were conducted. Given the nested design (students within classes; classes within institutions), supplemental t-tests were either (a) performed on cluster-level aggregates (e.g., class or institution means) or (b) accompanied by cluster-robust standard errors to mitigate inflated Type I error from within-cluster dependence. Where families of related comparisons were reported, Holm-adjusted p-values are provided as a conservative control of familywise error. Results from these supplemental tests were interpreted in conjunction with the HLM estimates to assess convergence and robustness.

Effect size reporting

For all pairwise comparisons, effect sizes are reported as Cohen’s d with Hedges’ g correction when group sizes were small or unbalanced; for pre–post designs summarized at the same level, ​ (using the average SD) is provided. When nonparametric tests were primary or used in sensitivity analyses, Cliff’s delta (Δ) is reported. For multilevel models, we report standardized coefficients, semi-partial for fixed effects, Snijders–Bosker indices for within- and between-cluster variance explained, and the intraclass correlation coefficient (ICC) to quantify clustering. For longitudinal growth models, standardized mean-change indices and standardized slope differences are presented. 95% confidence intervals accompany all effect sizes; where clustering or non-normality warranted, CIs were obtained via cluster bootstrap. Interpretation follows conventional thresholds (small/medium/significant) while emphasizing practical significance in the context of UDL implementation and equity-relevant outcomes.

Qualitative analysis procedures and triangulation

Qualitative data (faculty/administrator semi-structured interviews, student focus groups, ethnographic observations, and institutional documents) were analysed using thematic analysis that combined deductive coding (guided by UDL principles, implementation science, and the ecological framework) with inductive identification of emergent patterns. An initial codebook was developed from theory and study questions, piloted on ~ 20% of transcripts, and refined through two calibration rounds. Two trained analysts, independent of field implementation, coded all materials while blinded to the sites’ fidelity classifications. Discrepancies were resolved through analytic discussion, with adjudication by a third reviewer as needed. Intercoder agreement exceeded 85% on independently coded subsets; Krippendorff’s alpha, which was targeted at > 0.80, was documented. Credibility was strengthened through data triangulation (interviews, focus groups, observations, documents), analyst triangulation (dual coders plus senior audit), member checking with key informants, reflexive memoing, and maintenance of an audit trail. Thematic saturation was declared when no substantively new codes/themes emerged across three successive data sources. Integrated mixed-methods interpretation employed joint displays to align qualitative themes with quantitative indicators, highlighting areas of convergence and divergence that informed the final implementation framework.

Validity and reliability considerations

To ensure the trustworthiness of both the methods and findings, the study employed a series of rigorous validation strategies. Quantitative instruments underwent a thorough psychometric evaluation, including confirmatory factor analysis to verify construct validity. Measurement invariance testing was also conducted across diverse participant groups to confirm the consistency of the instruments. The aspect of internal consistency reliability was measured using the alpha and omega coefficients, with all aspects demonstrating acceptable reliability. Moreover, the test-retest reliability was also checked at various times with the main variables. The study employed several rigorous methods to enhance the validity of the qualitative findings. These methods included multiple analyst triangulation, member checking with key informants, and lengthy participation in the research environment. The reliability of the interpretations was further enhanced by discussions with subject matter experts, which gave credence and validity to the analytical conclusions. It was also described in rich and detailed terms to help the reader understand how the findings can be applied to other contexts.

The negative case analysis method helped the researchers detect and discuss cases that do not follow the predominant patterns, ensuring a more nuanced and holistic interpretation. In addition to these methods of qualitative validation, the study also placed a significant focus on the quality of integration between its quantitative and qualitative aspects. The mixed-methods design was intended to provide a multidimensional approach to the research questions, aiming at convergence; however, it pursued complementarity instead. To further substantiate the validity of the study findings, the researchers thoroughly addressed the validity issues associated with each methodological element. They ensured that the interpretations of the methods were transparent and consistent. Such an integrative approach also significantly enhanced the credibility, depth, and practical relevance of the overall study findings.

Ethical considerations

The study protocol was approved by the Ethics Committee of Philippine Christian University prior to participant recruitment and data collection. The study was conducted in full accordance with all applicable ethical guidelines, including the principles of the Declaration of Helsink. All participants were strictly informed about the procedures. For students who are not yet 18 years old, written consent from both parents or legal guardians, as well as the consent of the minors themselves, is required. Sensitive populations, such as students with disabilities, were given special protection so that their rights and well-being were not violated. The consent forms were distributed in various languages and in convenient formats to suit the interests of every participant. The participants were told that they could withdraw at any time without repercussions. All personally identifiable data were removed from the datasets to ensure privacy and confidentiality. All materials were stored securely in accordance with institutional guidelines and national data protection laws. The identities of the participants were maintained in the qualitative reporting through the use of pseudonyms, ensuring coherence in the narrative. Special focus was placed on the confidentiality of faculty participants, as implementation practices are a sensitive professional issue.

The principle of reciprocity guided the study, ensuring a mutually beneficial research process that was also ethical. The participating institutions received personalized reports on their adoption of UDL, along with evidence-based recommendations tailored to their specific needs. Secondly, faculty participants received professional development workshops based on the study’s findings, which facilitated both the development of instruction and the acknowledgement of their roles in the study. The purpose of this study was to minimize the burden on participants and maximize the benefits for educational communities participating in the study. The methodology of the research developed by the Consortium was a rigorous and thorough analysis of how UDL is implemented in a wide variety of educational institutions. This method ensured a high level of validity and reliability in its performance, which ensured both scientific and practical relevance. Through this inclusive and integrative model, both theoretical and practical results were achieved in the study, enabling educators, practitioners, and policymakers to enhance accessibility and student achievement with the aid of successful UDL practices.

Results

The section summarizes the significant results of a longitudinal study conducted over 24 months to investigate the implementation of Universal Design for Learning (UDL) in 87 schools. The findings are organized based on the main research questions of the study, which include implementation fidelity, student outcomes, the primary success factors, and the development of an evidence-based implementation framework. The discussion shows that the successful implementation of high-fidelity UDL is tightly connected with significant improvement in student performance and involvement. The data also reveal interesting trends in the way implementation is conducted and emphasize the importance of situational forces, such as institutional culture, administrative support, and faculty preparedness, in the effectiveness and sustainability of UDL practices.

Implementation fidelity patterns and institutional variation

The findings highlighted considerable differences in both the depth and quality of UDL implementation across the institutions studied. As shown in Fig. 4, scores across the three core fidelity dimensions, structural, process, and outcome, revealed clear patterns in how effectively UDL principles were embedded into institutional practices. These dimensions captured varying degrees of success in translating UDL into educational design and delivery, illustrating the diverse ways institutions approached and executed implementation across contexts.

Fig. 4.

Distribution of UDL implementation fidelity scores across three dimensions.

According to the fidelity assessment results:

• 18% of institutions achieved exemplary fidelity (91–100%),

• 23% demonstrated high fidelity (71–90%),

• 31% fell within the moderate fidelity range (41–70%),

• Moreover, 28% exhibited low fidelity (0–40%).

These findings underscore a critical insight: high-quality UDL implementation is multi-dimensional, requiring consistent alignment across structural supports, instructional practices, and measurable outcomes. The study highlights those institutions reaching the highest fidelity levels did so by attending to all three dimensions, suggesting that integrated and comprehensive approaches are essential for achieving meaningful UDL adoption.

Figure 5 illustrates the temporal examination of implementation fidelity development over the 24-month period, revealing essential patterns of institutional progression. The graph demonstrates the time series of fidelity to implementation by type of institution and context. Most institutions demonstrate an improvement in fidelity to implementation over time. The rate of improvement and capability to sustain improvements, however, vary by institution type and context. Initial implementation progressed faster in postsecondary sites (community colleges and teacher education programs) but plateaued after approximately 12 months. In contrast, K–12 institutions exhibited slower initial uptake with steadier gains across the study period. Institutions that had UDL coordinators and received sustained professional development throughout the study period showed a trend of upward improvement. In contrast, institutions that relied primarily on the initiative of one or several faculty members tended to exhibit more variability and less upward-trending patterns.

Fig. 5.

Longitudinal trajectories of implementation fidelity by institutional type and support level.

Student achievement outcomes and implementation effectiveness

The correlation between implementation fidelity and student achievement signifies strong evidence that high-quality implementation of UDL is effective. Figure 6 illustrates the performance of students at various implementation levels. Students showed significant differences in their achievement depending on the quality of implementation. Students in high-fidelity implementation contexts achieved an average increase of 37.4% compared to baseline measures. In comparison, those in moderate-fidelity implementation contexts achieved 18.2%, and implementation in low-fidelity contexts increased by only 6.8%. These effect sizes are practically significant, with high-fidelity implementation yielding a Cohen’s d value of 0.89 for overall achievement. In other words, the study’s results produce significant effects.

Fig. 6.

Student achievement gains by implementation fidelity level.

The differential impact analysis of UDL, presented in Fig. 7, investigates whether UDL is effective for all students, particularly for those who have been traditionally underrepresented, including students with disabilities, English language learners, and students from low SES backgrounds. The results show that when UDL is actually implemented, it has disproportionately positive effects on students who have been barred. Students with identified disabilities made achievement gains of 42.3% in the high-fidelity condition and 12.1% in the low-fidelity condition. In high-fidelity implementations, English language learners improved by 39.7% and 14.6% in lower fidelity. The disparity in education between marginalised and non-marginalised students was lowered by 34% for those UDL models that had ‘high-fidelity’ implementation. This provided the most significant evidence yet that UDL might help close the educational gap.

Fig. 7.

Differential achievement impact by student population and implementation fidelity.

Student engagement and social belonging outcomes

An analysis of student engagement outcomes reveals that the use of UDL (Universal Design for Learning) is significantly improving various dimensions of student engagement. The effect size is firm for marginalized learners. Figure 8 illustrates the comprehensive engagement analysis, encompassing behavioral, emotional, and cognitive components, which reveals that a high-fidelity UDL implementation significantly enhances all forms of engagement. In settings characterized by high-fidelity implementation, marginalized learners exhibited a 42.8% increase in overall engagement scores, while non-marginalized learners exhibited a 21.3% increase. UDL implementation may effectively address the engagement barriers that learners traditionally face who are underserved by conventional approaches.

Fig. 8.

Student engagement improvements by dimension and student population.

The longitudinal analysis of social belonging, presented in Fig. 9, demonstrates how the implementation of UDL affects students’ sense of connection and membership within their educational communities. The results show steady improvement in social belonging scores over the study period, with the most dramatic gains occurring in the second year of implementation as UDL practices became more established and comprehensive. Students with disabilities demonstrated significant improvements in social belonging, with scores increasing by 51% over 24 months in high-fidelity implementation settings. The data also reveal important interaction effects between implementation quality and time, with high-fidelity implementations showing accelerating improvements over time, while lower-fidelity implementations reached plateau effects after initial modest gains.

Fig. 9.

Longitudinal changes in social belonging by implementation fidelity and student characteristics.

Critical implementation factors and success predictors

The comprehensive analysis of factors that predict successful UDL implementation identified five critical domains that consistently distinguish high-performing institutions from those struggling with implementation efforts. Figure 10 presents the factor analysis results and relative importance of each implementation factor, derived from both quantitative institutional assessments and qualitative analysis of implementation experiences. Administrative support emerged as the strongest predictor of implementation success, with institutions demonstrating strong leadership commitment showing 3.2 times higher likelihood of achieving high-fidelity implementation. Ongoing professional development ranked second in importance, with institutions providing comprehensive, sustained training programs achieving significantly better implementation outcomes than those relying on one-time training events.

Fig. 10.

Critical implementation factors and their relative importance for UDL success.

The interaction analysis presented in Fig. 11 examines how different combinations of implementation factors create synergistic effects that amplify implementation success. The results demonstrate that no single factor alone is sufficient for achieving high-fidelity implementation; instead, successful institutions consistently demonstrate strength across multiple factors simultaneously. Institutions scoring high on both administrative support and professional development achieved exemplary implementation rates of 67%, compared to only 12% for institutions weak in both areas. The presence of robust technological infrastructure amplifies the effects of other factors; institutions that combine strong technology support with faculty collaboration achieve the highest overall implementation fidelity scores.

Fig. 11.

Synergistic effects of implementation factor combinations.

Technology integration and digital innovation patterns

The analysis of technology integration within UDL implementation reveals important patterns in how digital tools and resources support or constrain implementation efforts. Figure 12 presents the comprehensive assessment of technology use patterns across different implementation fidelity levels, demonstrating that high-fidelity implementers utilize technology more strategically and systematically to support UDL principles. Rather than simply adopting more technology tools, successful implementers demonstrate more thoughtful integration of technology that directly supports multiple means of engagement, representation, and action/expression. The analysis reveals that technology integration quality, rather than quantity, serves as a key differentiator between high and low-fidelity implementations.

Fig. 12.

Technology integration patterns by implementation fidelity level.

Faculty development and capacity building outcomes

The longitudinal analysis of faculty development and capacity building reveals a clear progression in educators’ knowledge, confidence, and implementation skills over the course of the study period. Figure 13 presents the trajectory of faculty UDL competency development across various professional development models, revealing significant differences in outcomes based on the comprehensiveness and design of capacity-building efforts. Faculty participating in comprehensive, multi-year professional development programs showed steady improvement throughout the study period, with knowledge scores increasing by 78% and implementation confidence growing by 65%. In contrast, faculty receiving only brief introductory training showed initial improvement that plateaued after six months, with minimal sustained growth in either knowledge or implementation effectiveness.

Fig. 13.

Faculty UDL competency development by professional development model.

The analysis of faculty collaboration patterns, presented in Fig. 14, demonstrates how collegial support and shared implementation efforts contribute to sustained adoption of UDL. Institutions that systematically fostered faculty collaboration through learning communities, peer observation, and shared planning achieved significantly higher implementation fidelity and sustainability. Faculty working in collaborative implementation environments reported 47% higher implementation confidence and demonstrated 34% better classroom observation scores compared to those implementing UDL in isolation. The data also reveal important spillover effects, with faculty collaboration on UDL implementation contributing to broader improvements in inclusive teaching practices and institutional culture.

Fig. 14.

Impact of faculty collaboration on implementation quality and sustainability.

Institutional context and policy alignment effects

The analysis of institutional context factors reveals how organizational characteristics and policy alignment influence the success of UDL implementation. Figure 15 presents the comprehensive assessment of how different institutional characteristics predict implementation outcomes, demonstrating that certain organizational features consistently support or constrain UDL adoption efforts. Institutions with explicit inclusion policies and diversity commitments achieved high-fidelity implementation at rates 2.8 times higher than those without such formal commitments. Resource allocation patterns also proved highly predictive, with institutions dedicating specific funding to UDL implementation achieving significantly better outcomes than those relying on existing budget allocations.

Fig. 15.

Institutional characteristics as predictors of implementation success.

Sustainability and long-term implementation patterns

The analysis of implementation sustainability over the 24-month study period provides important insights into factors that support long-term maintenance of UDL practices. Figure 16 presents the sustainability analysis, examining which institutions maintained or improved their implementation fidelity over time versus those that showed a decline in implementation quality. Institutions with embedded UDL practices in formal policies and evaluation systems demonstrated 89% sustainability rates, compared to 34% for those relying primarily on individual faculty commitment. The analysis also reveals important threshold effects, with institutions achieving moderate fidelity (60% or higher) showing a substantial likelihood of sustained implementation, while those below this threshold frequently experienced implementation decay over time.

Fig. 16.

Implementation sustainability patterns and predictive factors.

Qualitative insights and implementation experiences

The qualitative analysis offers extensive context that enhances and elucidates our quantitative findings regarding the implementation experience. Through interviews and focus groups with faculty, three major themes emerged: a gradual and supported implementation process, changing the institution’s culture rather than just the practices of individuals, and the importance of student feedback in maintaining implementation efforts. Faculty consistently reported that successful UDL implementation required a fundamental shift in our thinking about learner variability and teaching responsibility as opposed to simply using techniques and tools. Analysis of student focus group data provided valuable insights into learners’ experiences with the implementation of UDL. In environments characterized by high-fidelity UDL practices, students consistently reported a greater sense of choice and control over their learning, which had a powerful impact on their motivation and engagement.

Notably, students with disabilities emphasized the value of universal design strategies that benefit all learners, rather than those aimed at specific accommodations. This perspective supports the idea that inclusive design enhances the learning experience for everyone.

The qualitative data also revealed several positive, unanticipated outcomes, including stronger peer relationships, increased participation in classroom activities, and enhanced awareness of personal learning strengths. Ethnographic observations further illuminated how UDL principles were enacted in real classroom settings, particularly through the lens of social interactions and instructional dynamics.

The high UDL integrated classrooms characterized instruction as being flexible, responsive, and adaptable to meet the needs of diverse learners, while maintaining high expectations for all learners. They were environments that encouraged the use of collaborative learning designs, enhanced learner agency, and featured authentic and differentiated assessments aligned to the capabilities and preferences of learners.

Besides, the observational data also indicated the effectiveness of incremental implementation strategies. In particular, effective faculty tend to concentrate on one UDL principle and then continue to incorporate other approaches that employ multiple principles.

To enhance transparency and integration, qualitative materials were thematically analysed using a hybrid deductive–inductive approach grounded in UDL and implementation science. Two trained analysts, blinded to sites’ fidelity classifications, independently coded transcripts and field notes; disagreements were resolved through adjudication, with intercoder agreement exceeding 85% on independently coded subsets and Krippendorff’s alpha ≥ 0.80. We employed data triangulation (interviews, focus groups, observations, documents) and analyst triangulation (dual coders plus senior audit), complemented by member checking with key informants and maintenance of an audit trail and reflexive memos. Thematic saturation was declared when no substantively new codes emerged across three successive sources. Qualitative themes were then integrated via joint displays with quantitative indicators (fidelity scores, engagement outcomes) to co-interpret mechanisms and boundary conditions, thereby strengthening the mixed-methods coherence of the study.

Discussion

Contextualisation with prior studies

Our findings are broadly consistent with and extend the existing UDL evidence base. The positive associations between high-fidelity UDL implementation and student achievement and engagement align with meta-analytic indications of effectiveness while addressing prior methodological limitations noted by Almeqdad et al.¹⁶. The prominence of implementation fidelity as a driver of outcomes converges with calls by Hills et al.⁴³ and Estefan et al.⁴² to move beyond surface-level adoption toward coherent, equity-oriented practice. The differential benefits observed for historically marginalized learners (students with disabilities and multilingual learners) substantiate claims that UDL can narrow access and attainment gaps^28,30, providing large-scale, longitudinal evidence across diverse institutions. Our multi-level patterning of effects resonates with ecological perspectives advocated by Fovet³³ and Timuș et al.³⁴, demonstrating that institutional and policy layers condition classroom-level enactment. With respect to discipline-specific literatures, the present cross-institutional results complement positive implementations reported in Mathematics, Chemistry, and Public Health^26–28 by showing that robust outcomes emerge when structural, process, and outcome fidelity are simultaneously addressed. Finally, our analysis of technology use accords with research on technology-enabled UDL^22–25, emphasizing that quality of pedagogical integration, not tool proliferation, differentiates high- from low-fidelity sites.

Explanatory factors and mechanisms

Three interrelated factors appear to account for the observed effects. Teacher training and professional development function as capability builders: comprehensive, multi-year programs were associated with sustained gains in faculty knowledge and implementation confidence, which plausibly translate into more consistent enactment of multiple means of engagement, representation, and action/expression^18–21. Institutional support operates as an enabling context, with administrative commitment, targeted resources, and policy alignment predicting higher fidelity and durability of implementation. This aligns with system-level readiness concerns identified by Healy et al.³⁵ and governance analyses by Liasidou and Liasidou³⁶. Academic culture provides the social substrate for persistence and spread, as evidenced by collaborative pedagogy, peer observation, and learning communities that cultivate shared norms for inclusive design, with spillovers to broader teaching practices^37–39. Mechanistically, these factors likely enhance (a) instructional coherence (common design language and rubrics), (b) educator self-efficacy (repeated practice with feedback), and (c) data-informed iteration (routine use of student engagement/achievement analytics), producing reinforcing cycles that elevate process and outcome fidelity over time.

Implications for inclusive educational practice

Several concise implications follow. First, institutions should embed UDL within formal policy and quality assurance, specifying minimum fidelity benchmarks across structural, process, and outcome dimensions, with periodic audit and feedback cycles. Second, professional development should be multi-year and practice-proximal, combining workshops with coached implementation, peer observation, and artifact review to accelerate skill transfer. Third, technology adoption should be pedagogy-led, prioritizing tools that directly support flexible engagement, multimodal representation, and diverse action/expression pathways, accompanied by guidance on accessible design. Fourth, to realize UDL’s equity promise, social-emotional and belonging supports should be integrated into course design (choice architectures, low-stakes formative assessments, community-building routines), rather than being treated as adjunct services. Finally, routine mixed-methods monitoring, linking fidelity indicators with outcome dashboards and student voice, can guide adaptive improvement and maintain alignment between institutional intent and classroom practice. Collectively, these steps operationalize UDL as a system property rather than an individual instructor’s choice, increasing the likelihood of a durable and scalable impact.

Mixed-methods integration and co-interpretation

The mixed-methods integration advances beyond supplemental triangulation to explain observed quantitative patterns. For example, the steeper early fidelity gains in postsecondary sites co-occurred with qualitative evidence of more substantial local decision autonomy and shorter curricular revision cycles, whereas steadier K–12 gains aligned with district-level coaching and policy reinforcement. Similarly, the disproportionate improvements among historically marginalized learners in high-fidelity sites converged with student-reported increases in choice, formative feedback, and community-building routines. These joint inferences clarify how professional learning structures, administrative commitments, and collaborative cultures interact to produce the outcome profiles observed in our multilevel models. By explicitly linking themes to metrics in joint displays, the study strengthens explanatory validity and reduces limitations typically associated with treating qualitative data as merely illustrative.

Conclusion

This paper has explored how the concept of Universal Design for Learning (UDL) can be applied across the 87 participating educational institutions and identified substantial evidence within this study context that UDL implementation fidelity is closely related to improved student performance. Nevertheless, the results also report that the quality of implementation is a central factor in deriving these benefits. Implementation of high-fidelity UDL was linked to significant gains in academic achievement, student engagement, and social belonging, and the most significant gains were found amongst marginalized learners. Specifically, academic achievement improved by an average of 37.4%, while engagement among marginalized students increased by 42.8%, reflecting both statistically and practically significant results. The study also offers a significant theoretical contribution by introducing and validating a multi-dimensional implementation fidelity framework. Filling a critical gap in the existing literature, this framework offers a comprehensive and structured approach to evaluating implementation quality, moving beyond binary indicators of UDL presence. By distinguishing among structural, process, and outcome fidelity, the framework clarifies how each dimension independently contributes to student outcomes and identifies targeted areas for improvement. It further underscores the importance of addressing adherence, competence, and dosage in a coordinated manner, challenging overly simplistic models of UDL adoption. The study advocates for a comprehensive, system-level strategy to ensure the meaningful and sustainable integration of UDL practices. While these findings are robust within the participating institutions and timeframe studied, continued research is warranted to establish the extent to which these implementation patterns and outcomes generalize across different institutional contexts, cultural settings, and extended implementation periods.

Study limitations

Several limitations should be considered when interpreting these findings. First, the study was conducted primarily in institutions that volunteered to participate, which may introduce selection bias toward institutions more motivated to implement UDL. Second, although our sample encompassed diverse contexts, generalizability to all educational settings warrants further investigation. Third, the 24-month follow-up period, while substantial, may not capture long-term sustainability effects beyond this timeframe. Fourth, implementation fidelity measures, though comprehensive, relied partially on self-report data, which may introduce social desirability bias. Finally, while we controlled for many variables, unmeasured confounding factors may influence the observed relationships between UDL implementation and student outcomes.

Author contributions

Pu Guo (PG), Zeyu Wang (ZW); Methodology: PG, ZW; Formal analysis: PG; Investigation & Data curation: PG, ZW; Writing—original draft: PG, ZW; Writing—review & editing: PG, ZW; Visualization: PG; Supervision & Project administration: ZW.

Data availability

The data presented in this study are available on request from the corresponding author.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

All procedures involving human participants were performed in accordance with the ethical standards of this committee and with the 1964 Helsinki declaration and its later amendments. The experimental protocols were reviewed and approved by the Ethics Committee of Philippine Christian University prior to participant recruitment and data collection. Written informed consent was obtained from all participants prior to their inclusion in the study, ensuring their voluntary participation and understanding of the research objectives.