07_Designing for Learning by VLK Architects

Designing for Learning: What School Leaders Should Know

About Stakeholder Space Preferences

School architecture plays a critical role in shaping educational experiences, influencing both the functionality and adaptability of learning environments while reflecting the pedagogical, contextual, cultural, and instructional values embedded within these spaces. As educational priorities evolve, there is growing interest in understanding how educational stakeholders prioritize key architectural features that shape school design. This study, part of a larger project, emerges from a participatory design process as a means of gathering early input to inform conceptual design. The image-based exercise allows participants to visually engage with different spatial configurations and identify features they perceive as ideal for teaching and learning. The study examined these preferences by analyzing the preference trends from stakeholders and thematic insights from qualitative analysis. The goal was to uncover the underlying values that inform architectural decision-making and instructional priorities in educational spaces.

Grounded in social constructivism and spatial theory, this study investigated how cultural contexts and instructional models shape educational stakeholders’ perceptions of architectural elements. The integration of visual anthropology, visual grounded theory, and statistical methods allowed for a multilayered analysis of how stakeholders’ spatial preferences align with broader educational and theoretical values. This research explored multiple questions, but this manuscript focuses primarily on one question: What architectural features are most preferred by stakeholders in educational spaces? By incorporating both qualitative coding and quantitative analysis, this study provides a comprehensive understanding of the role of architecture in

education, highlighting how schools are covisualized, evaluated, and shaped by educational stakeholders who participate in the design and use of educational spaces.

Literature Review

The design of educational spaces significantly influences teaching practices, student learning, and institutional culture. Increasingly, research in school architecture focuses not only on aesthetics or innovation but on how learning environments align with pedagogical priorities and user needs. Educators and school leaders are placing greater emphasis on flexible, adaptable, and collaborative spaces that support diverse instructional strategies (Higgins et al., 2005; Woolner, 2015). However, misalignments still occur between design intent and actual classroom use, often due to insufficient early involvement of educational stakeholders in the planning process (Blackmore et al., 2011).

Educator Preferences and Instructional Priorities

Educators consistently express preferences for learning environments that support interaction, autonomy, and real-time adaptability. These preferences align with well-established instructional models such as Project-Based Learning (PBL), Universal Design for Learning (UDL), and Flipped Classrooms (Saldaña, 2021; Heath et al., 2010). Features like movable furniture, writable surfaces, and reconfigurable group areas are consistently valued for their ability to support differentiated instruction and active learning.

Natural light, access to the outdoors, and visual transparency also emerge as high-priority design elements. These preferences are tied not only to comfort and well-being but to instructional visibility, supervision, and psychological safety critical concerns for school leaders balancing innovation with accountability and classroom management (Rose, 2016; Barrett et al., 2015). Oliveras, Bouillion, and colleagues (2021; 2024) emphasize that the

intentional design of flexible learning spaces can significantly enhance student engagement, teacher efficacy, and instructional alignment. Their findings highlight that stakeholders involvement in spatial planning particularly educators results in learning environments that are both adaptable and instructionally relevant. Moreover, Oliveras-Ortiz et al. (2023) stress the importance of translating teacher input into design elements that support visibility, acoustics, and differentiated learning modalities, reinforcing the argument that stakeholder-informed design leads to more effective spaces.

Spatial Design as a Leadership Decision

For school leaders, understanding these spatial preferences is essential for making informed decisions about capital projects, classroom renovations, and facility planning. Studies like Fouad and Sailer (2022) emphasize that physical space must afford instructional behaviors such as circulation, collaboration, and engagement rather than simply reflect design trends. Similarly, Syeed (2022) and Boys (2014) argue that inclusive and equitable learning spaces require intentional leadership focus, ensuring that design decisions reflect diverse needs across student populations.

From a policy perspective, international models such as the OECD’s Innovative Learning

Environments (ILEs) framework reinforce the leadership role in aligning space with pedagogy, advocating for environments that are learner-centered, personalized, and inclusive (OECD, 2013, 2017). This highlights a growing consensus: facility design is no longer a background concern but a strategic tool for improving instruction, fostering school culture, and supporting student success.

Gaps and Contributions

Despite growing literature on school design, there remains a gap in empirical studies that systematically analyze stakeholder preferences using mixed methods. Most existing studies are either conceptual or focus on post-occupancy observations. This study addresses that gap by examining how educators and other stakeholders interpret and prioritize spatial features, offering actionable insights for school leaders who must balance vision, budget, and instructional strategy. It reinforces the need for school leaders to lead design conversations grounded in pedagogical goals and supported by data on what users value most in their learning environments (Oliveras-Ortiz et al., 2023; Oliveras et al., 2024).

Theoretical Framework

The theoretical framework for this study is grounded in social constructivism and spatial theory, which emphasize the interaction between educators’ experiences, instructional models, and physical learning environments. Social constructivism, rooted in the works of Vygotsky (1934) and expanded by Lefebvre (1974) into spatial domains, suggests that learning spaces are not passive containers but active participants in the educational process. School design is a collaborative effort that should reflect the needs of educators and learners, ensuring alignment between architectural intent and educational function (Woolner, 2015). Similarly, Higgins et al. (2005) emphasizes that the quality of school environments significantly impacts learning outcomes, reinforcing the necessity of integrating educator preferences into spatial planning.

Through this study, the author developed a conceptual model to illustrate how architectural features, educational stakeholders’ pedagogical values, and statistical preference distribution interrelate. This study was a mixed methods study aimed to systematically analyze the impact of school environments on educational stakeholders’ preferences. Previous research

emphasizes the measurable effects of spatial configurations on learning, validating the need for statistical analysis of the preference distribution. Additionally, Woolner (2015) emphasizes that educators play a critical role in shaping learning spaces, necessitating empirical approaches to understand their spatial priorities.

Quantitative Methods within Theoretical Framework

While social constructivism and spatial theory provide a foundation for understanding how educators perceive and interact with learning spaces, the use of statistical methods (ANOVA and clustering) was essential for validating these theoretical constructs with empirical evidence. Spatial theory posits that physical environment shapes user behaviors; thus, analyzing the preference distribution quantitatively allows us to assess whether specific architectural features influence educational stakeholders’ preferences in a measurable way Social constructivism emphasizes contextual learning and shared meaning-making; statistical analysis, such as clustering through Principal Component Analysis (PCA) and Analysis of Variance (ANOVA), enabled the identification of patterns in how different educational stakeholders prioritize architectural features. By integrating qualitative insights from thematic analysis with quantitative validation of the preference distribution, this study ensured a comprehensive approach to understanding stakeholders preferences, aligning with the theoretical foundations of spatial influence and constructivist learning.

Conceptual Model

Understanding Educational Stakeholders Preferences in School Architectural Design

To synthesize the relationships between architectural design elements, stakeholders’ perceptions, and preference trends, this study proposes a conceptual model that integrates qualitative coding, statistical analysis, and educational stakeholders’ self-reported preferences.

The model (Figure 1) provides a structured framework for understanding how stakeholders evaluate learning environments and how their preferences are shaped by both design features and instructional priorities. The conceptual model consists of three interdependent layers

Layer I: Architectural Design Features (Independent Variables)

These are the physical attributes of learning spaces that influence educational stakeholders’ perceptions and preferences. Through the coding of images and thematic analysis, five key architectural design elements were identified:

Collaboration and Social Learning. Open spaces, group work areas.

Flexibility and Adaptability. Moveable furniture, multiuse spaces.

Natural Light and Outdoor Integration. Large windows, nature-inspired elements.

Technology-Enhanced Spaces. Smartboards, device-friendly setups.

Traditional vs. Progressive Design. Lecture-based vs. student-centered layouts.

Layer II: Mediating Factors (Perceived Values & Instructional Models)

Educational stakeholders do not assess architectural elements in isolation; rather, they interpret them based on pedagogical needs and practical constraints. The thematic analysis of stakeholders’ comments revealed four mediating factors that shape preferences.

Student Engagement and Active Learning. Spaces that encourage peer interaction and hands-on experiences received higher preference.

Supervision and Classroom Management. Open spaces raised concerns about noise and behavioral monitoring, affecting how educational stakeholders rated these images.

Instructional Versatility. Spaces that allowed for multiple instructional configurations were rated higher.

Emotional and Psychological Comfort. Natural lighting and openness were associated with positive psychological effects, increasing preference.

Layer III: Preferences & Statistical Trends (Dependent Variables)

The outcome of educational stakeholders’ evaluations was measured through statistical analysis, revealing distinct patterns in preference distribution.

Greater Endorsement of Social and Flexible Spaces. Stakeholders demonstrated a significant preference for spaces that support collaboration and flexibility.

Design-Based Preference Grouping. Cluster analysis (PCA) revealed that stakeholders’ preferences were not random but instead followed structured patterns based on spatial priorities.

Figure 1. Conceptual Model: Understanding Stakeholders Preferences in School Architectural Design

Interpreting the Model

This model demonstrates that educational stakeholders’ preferences for school architectural design are structured by how spaces align with their instructional needs. Statistical trends confirm that these preferences are consistent across selection patterns, reinforcing the validity of the qualitative findings By integrating these insights, the model serves as a framework for school leaders and policymakers in advocating for learning environments that align with contemporary educational priorities. The findings suggest that school design efforts should consider not only aesthetic and functional design but also how spaces support instructional best practices. This conceptual model provides a data-driven foundation for educational stakeholders, ensuring that school environments are optimized for both teaching and learning.

Methodology

In this study, data were collected through two primary sources. However, for the purpose of this manuscript, only the findings aligned to visual data, including images of school spaces that stakeholders evaluated through a preference selection process, will be reported. While these data sources provided a comprehensive understanding of stakeholders, it is essential to clarify how each type of data was analyzed in relation to the study’s research questions To ensure a structured approach, the qualitative and quantitative analyses were aligned with specific research sub-questions:

Qualitative Analysis (Visual Grounded Theory & Thematic Analysis)

The following research questions were investigated through coding and thematic analysis of the educational stakeholders’ preference selection process:

RQ1: What architectural features are most preferred by educational stakeholders in educational spaces?

RQ2: What roles do aesthetics and functionality play in educational stakeholders’ preferences for school architecture?

Quantitative Analysis

RQ3: What are the similarities and differences between the architectural features of images with the highest and lowest levels of preference?

To answer the third research question, a one-way ANOVA tested whether the allocations of preference markers varied significantly across different architectural categories, identifying whether specific space types were more highly preferred. Clustering (PCA) were applied to detect patterns in the preference distribution, grouping stakeholders with similar architectural preferences. By explicitly linking each research question to its corresponding analysis method, this structure clarified how qualitative and quantitative data were used to answer different aspects of the study’s overarching inquiry into stakeholders’ architectural preferences.

Mixed Methods Approach

This study employed a mixed methods approach that integrates visual anthropology, visual grounded theory, and quantitative statistical analysis to examine educators, students, parents, and other stakeholders’ preferences and the contextual and social significance of architectural elements in educational spaces. The chosen design provided a comprehensive understanding of how architectural elements reflect educational and theoretical values, considering both subjective interpretations and quantifiable trends in preference patterns. This combination of methods allowed the study to explore not only what stakeholders preferred, but

also why certain features resonated with them capturing both the affective and functional dimensions of school design

Data Collection

The images used in this study were selected from data previously collected and collated between 2017 and 2022 to gather insight into stakeholders’ preferences for different types of school spaces. The images included a variety of spaces and were chosen based on stakeholders’ self-reported preferences. Participants were encouraged to provide narratives to support or challenge their preferences, adding depth to the process. Initially, images with the lowest level of preference, or those with no supporting data, were excluded from the study. However, to ensure that the visual grounded theory accurately reflected valid preferences, all images including those with low or no preference were included in the data set to facilitate the comparison and analysis of architectural elements in both preferred and non-preferred spaces. The visual data were coded using grounded theory techniques (Charmaz, 2014; Heath et al., 2010; Rose, 2016). This involved open coding to identify initial themes, followed by axial and selective to refine and integrate these themes into a coherent theoretical framework. The goal was to develop a grounded theory that explains how and why certain architectural features are valued in educational contexts.

First, in alignment with grounded theory techniques, the visual data were organized by level of preference across all types of spaces without imposing preexisting categories, such as the type of learning spaces (Charmaz, 2014; Heath et al., 2010; Rose, 2016). Each image was documented with metadata, including contextual details and textual data such as stakeholders’ comments and the reported level of preference. Descriptive statistics were used to summarize the reported preferences and identify trends in preferences and styles. Each image was analyzed

independently, noting any architectural elements, patterns, or features, including but not limited to, the use of space, color schemes, windows, and lighting. The images were coded, and emerging themes were noted. Descriptive statistics were used to summarize the reported preferences and to identify trends in preferences and styles. Next, the qualitative data, comments linked to each image, were analyzed to identify key themes and factors influencing educational stakeholders’ preferences for various architectural elements. The stakeholders’ comments were integrated into the coding to connect visual elements with the expressed preferences.

Once initial codes were established, axial coding was used to identify relationships between the codes and group them into higher-level categories or themes (Saldaña, 2021). For instance, codes like "open space," "natural light," and "flexible seating" were grouped under a broader theme of "Flexible and Adaptable Environments," reflecting a focus on spatial qualities that promote engagement, comfort, and instructional versatility. Similarly, codes such as "collaborative areas," "student-centered design," and "technology integration" were categorized under "Active Learning Spaces," representing spaces designed to support interaction, mobility, and multimodal instruction.

Additionally, the identified categories were analyzed for their relationships with one another, ensuring that thematic groupings accurately captured the recurring patterns in the data. This process facilitated a deeper understanding of how stakeholders conceptualize functional and aesthetic priorities in school spaces, without imposing preexisting frameworks. The coding structure allowed for an iterative refinement process, ensuring that emergent themes remained closely grounded in the qualitative data and provided a robust foundation for further analysis. As suggested by the literature (Saldaña, 2021; Stanczak, 2007), the next step in this visual grounded theory study was to identify the core themes that represent the central ideas of the analysis. The

overarching factors, which reflect the influence architectural preferences have in educational settings, were used to develop a conceptual model to explain the relationships between the identified themes, supported by the visual data, stakeholders’ comments, and quantified preferences. The data analysis process was repeated and continued until the images or comments no longer yielded new insights or themes, reaching theoretical saturation. Reaching theoretical saturation demonstrates that the analysis was sufficiently exhaustive, ensuring that subtle elements of the visual data were explored and not overlooked, capturing the complexity of the architectural elements and stakeholders’ preferences (Flick, 2018; Strauss & Corbin, 1998). This iterative process ensured that the grounded theory was fully developed and the conclusions valid (Flick, 2018; Strauss & Corbin, 1998).

Throughout the data analysis process, the researcher also engaged in reflexivity practices by noting thoughts, decisions, and the progression of the analysis. In visual grounded theory, reflexivity refers to the process by which researchers critically examine their own influence on the research, particularly how their biases, perspectives, and interactions with the visual data shape the interpretation and outcomes of the study (Mason, 2018; Pink 2013). Reflexivity involves continuous self-awareness and self-questioning throughout the research process, ensuring that the analysis of visual data is as objective and transparent as possible (Mason, 2018; Pink 2013). This process ensured the development of a theory that is well-documented and grounded in the data. The findings from both quantitative and qualitative analyses were integrated during the interpretation phase. This integration involved comparing and contrasting the results to identify how the different data types complemented each other. This comparison was crucial in determining whether the qualitative reasons aligned with the quantitative trends or if discrepancies existed.

Coding Framework for Visual Data Analysis

To systematically analyze stakeholders’ preferences for school architectural elements, this study employed a structured coding framework rooted in visual grounded theory and thematic analysis. The coding process involved the images of school spaces that were rated by participants The goal was to identify patterns in stakeholders’ preferences

Coding Process for Image Analysis

The image dataset consisted of photographs of school spaces that stakeholders evaluated. Each image was analyzed using an iterative coding process, consisting of the following stages:

Open Coding: Identifying Key Architectural Features

Each image was examined to identify observable architectural characteristics that could influence stakeholders’ preferences. Key elements coded at this stage included:

• Spatial configuration: Open-plan vs. enclosed classrooms, small-group areas, flexible layouts.

• Furniture and adaptability: Fixed seating vs. modular furniture, presence of writable surfaces.

• Lighting and visibility: Natural light, transparency, and sightlines between spaces.

• Technology integration: Presence of smartboards, device-friendly spaces, digital learning tools.

• Connection to nature: Outdoor access and green spaces.

Axial Coding: Grouping Features into Thematic Categories

Once individual elements were identified, they were grouped into higher-level themes that reflected educational stakeholders’ conceptualization of ideal learning environments:

• Collaboration and social learning: Spaces designed for student interaction, group work.

• Flexibility and adaptability: Movable furniture, multipurpose spaces, flexible seating.

• Traditional vs. progressive design: Lecture-based classroom layouts vs. student-centered configurations.

• Nature-inspired learning spaces: Integration of greenery, natural light, and outdoor learning.

• Technology-enhanced spaces: Presence of interactive technology, blended learning tools.

Selective Coding: Linking Themes to Educational Stakeholders Preferences

To determine how these themes influenced preference patterns, the images were crossreferenced with stakeholders’ preferences. Trends were identified based on the frequency and intensity of selections per theme: Did images with collaborative spaces receive higher preferences? Was there a preference for traditional or flexible designs? Did natural light and outdoor spaces correlate with higher preferences?

Connection Between Qualitative Codes and Quantitative Analysis

To validate the qualitative findings, coded themes were compared to selection trends and analyzed using statistical tests. If certain themes appeared frequently in high-ranked images, this indicated a direct relationship between coded features and preference patterns. Clustering through PCA was analyzed against qualitative themes. If statistical clustering revealed groups of stakeholders who consistently preferred similar images, these clusters were cross-referenced with dominant qualitative themes to determine which design factors shaped selection trends. By integrating visual grounded theory, thematic coding, and statistical validation, this coding framework ensured that stakeholders’ preferences were systematically analyzed in relation to architectural trends and instructional models. The iterative coding process revealed clear

patterns in preferred learning spaces, which were further tested through statistical methods, creating a cohesive mixed-methods analysis of school design preferences.

Statistical Examination of Preference Patterns

While the qualitative components of this study captured the interpretive and contextual aspects of architectural preferences, a quantitative analysis was conducted to identify statistical trends in preference distributions across different school space images. The quantitative analysis was designed to detect patterns in stakeholders’ preferences for school architectural design and determine whether statistically significant differences existed between groups. To achieve this, the study utilized two key statistical tests: one-way ANOVA and K-Means clustering with PCA. These methods were selected based on the research questions and the nature of the preference data, ensuring alignment between the qualitative themes and statistical validation of preferences.

Differences in Preference Across Architectural Boards

A one-way ANOVA was used to evaluate whether statistically significant differences existed in the preferences across different architectural board categories. Since multiple categories were compared, ANOVA was chosen instead of multiple independent t-tests to reduce the risk of Type I errors (false positives). This test helped assess whether stakeholders favored specific types of learning spaces, aligning with the thematic groupings identified in the qualitative coding process.

Identifying Preference Patterns

To explore underlying patterns in preference behaviors, K-Means clustering was applied. This technique grouped images into clusters based on similar preference patterns, revealing latent trends in how stakeholders prioritized different architectural elements. PCA was used alongside clustering to reduce the dimensionality of the data, helping to visualize the major

drivers of variation in preferences. These methods provided a data-driven categorization of preferences, complementing the qualitative themes identified in the visual grounded theory analysis

Integration of Quantitative and Qualitative Findings

While the qualitative components of this study captured the interpretive and contextual aspects of architectural preferences, a quantitative analysis was conducted to identify statistical trends in preference behaviors across different school space images. The quantitative analysis in this study was designed to identify patterns in stakeholders’ preferences for school architectural design and to determine whether statistically significant differences existed between groups. The statistical tests selected one-way ANOVA and K-Means clustering with PCA were chosen based on the specific research questions and the nature of the preference allocation data.

By employing these statistical methods, the study was able to analyze broad trends (ANOVA) and hidden patterns in preference distribution (clustering and PCA). The quantitative findings reinforced the qualitative insights, ensuring that both explicitly stated preferences (stakeholders’ comments) and implicit decision-making patterns (statistical selection trends) were captured in the analysis of school architectural preferences. This mixed-methods approach enhances the reliability of the study by providing empirical validation of the coded themes, ensuring that educational stakeholders’ expressed preferences for collaboration and flexibility are supported by statistical evidence. The results demonstrate a structured relationship between architectural features, instructional models, and educational stakeholders’ spatial priorities, providing a comprehensive framework for understanding how school design decisions align with pedagogical needs.

Results

Qualitative Analysis of Implied Teacher Preferences and Instructional Models

The use of image-based preferences allowed for a layered understanding of how stakeholders interact with architectural representations. Image selections revealed strong thematic leanings The qualitative analysis of images revealed distinct patterns in stakeholders’ preferences, suggesting that they favor specific architectural features that align with studentcentered instructional models and collaborative learning environments. By examining the preference distribution data and conducting a visual analysis of the most and least preferred images, several key themes emerged, providing insight into how educational stakeholders conceptualize ideal learning spaces.

Features of the Most Preferred Images

Flexible Seating and Adaptable Spaces. Preferred images included movable furniture, modular seating arrangements, and reconfigurable layouts, supporting instructional flexibility. These features align with Universal Design for Learning (UDL) principles, allowing educators to modify the learning space based on students’ needs and activities (Oliveras-Ortiz, 2023; Oliveras-Ortiz et al , 2021 & 2023; Oliveras et al., 2024)

Collaborative Workspaces. Images depicting small group discussion areas, shared tables, and open collaboration zones received higher ratings. This reflects a preference for cooperative learning models, such as Project-Based Learning (PBL) and Socratic seminars, which emphasize peer interaction and student agency (Oliveras-Ortiz, 2023; Oliveras-Ortiz et al., 2021 & 2023; Oliveras et al., 2024).

Integration of Nature and Natural Light. Images that showcased outdoor learning environments, green spaces, or large windows were among the most preferred. This preference

aligns with nature-inspired design in education, which has been shown to enhance student wellbeing, focus, and engagement (Oliveras-Ortiz et al., 2021; Oliveras et al., 2024).

Technology-Integrated Environments. Images featuring interactive whiteboards, device-friendly workstations, and integrated technology zones ranked high. This reflects the influence of Blended Learning models, where technology enhances instruction rather than replacing teacher-led facilitation.

Student-Centered Configurations. Stakeholders favored images that minimized the presence of a dominant teacher’s desk, reinforcing student ownership of learning. This preference aligns with Flipped Classroom and Inquiry-Based Learning models, where teachers function as facilitators rather than lecturers (Oliveras-Ortiz et al., 2021 & 2023; Oliveras et al., 2024)

Relationship Between Preferred Features and Instructional Models

The architectural features most preferred by stakeholders strongly align with modern instructional frameworks that emphasize active learning, student engagement, and adaptability. The following instructional models closely match the spatial preferences observed in the qualitative analysis These results also suggest that educational stakeholders’ visual preferences may be influenced as much by how comfortable, engaging, or inviting a space looks as by how well it aligns with a specific teaching method. While many of the selected images reflect studentcentered instructional practices, stakeholders may be drawn to them because the spaces appear to support the kinds of activities they value like collaboration, quiet study, or flexibility rather than because they consciously represent a particular educational theory.

Inquiry-Based Learning

• Emphasis on collaborative spaces, hands-on learning, and real-world problem-solving

• Preference for group-friendly configurations, writable surfaces, and technology-rich zones.

Flipped Classroom Approach

• Encourages flexible seating, mobile technology, and blended learning stations

• Preference for fluid spaces where students rotate between direct instruction, peer collaboration, and independent work.

Universal Design for Learning (UDL)

• Preference for adjustable workspaces, diverse seating options, and multimodal learning stations.

• Encourages accessibility, inclusivity, and choice in learning environments

Socratic Seminar & Discussion-Based Learning

• Requires circular seating arrangements, visibility, and shared discussion areas.

• Preference for transparent partitions and spaces designed to foster critical dialogue. The analysis indicates that educational stakeholders favor environments that support pedagogical flexibility, collaboration, and experiential learning over traditional, teacher-centered models. The findings from this qualitative analysis reinforce the idea that educational stakeholders’ spatial preferences are deeply connected to instructional values (Barrett et al., 2015). Spaces that promote student interaction, adaptability, and accessibility are preferred over rigid, hierarchical classroom designs. This suggests that school leaders engaged in the design of new learning spaces should advocate for flexibility, natural elements, and collaborative spaces to better support modern teaching and learning practices.

Quantitative Results

ANOVA Results and Hypothesis Testing

To assess whether the degree of reported preferences varied significantly across different categories, a one-way analysis of variance (ANOVA) was conducted. Table 1 presents the total number of preferences per category, the number of images analyzed per category, and the average preferences per image for each category.

Table 1

Preferences by Category

Note: Due to copyright restrictions related to the categories and processes, the specific category names have been redacted.

The test examined whether observed differences in preference distribution were due to meaningful preferences for specific categories or simply the result of random variation. The null hypothesis (H₀) posited that there was no significant difference in the mean number of selections across the different categories, suggesting that any variations were incidental rather than indicative of a true preference. Conversely, the alternative hypothesis (H₁) proposed that at least one category had a significantly different mean preference distribution, implying that participants favored certain types of categories over others.

The ANOVA results revealed an F-statistic of 1.19 and a p-value of 0.312, with 7 degrees of freedom for the categories and 199 for the residual variance. Since the p-value exceeds the

standard significance threshold of 0.05, we fail to reject the null hypothesis, indicating that there is no statistically significant difference in preference distribution across the categories. This suggests that participants did not exhibit a strong preference for any particular category in terms of selection patterns. While some differences in the preference distribution were observed, they are more likely attributable to random variation rather than a meaningful trend. These findings align with the broader analysis, reinforcing those other variables such as specific design features may have played a more influential role in selection behavior than the category itself.

The box plot visualization, Figure 2, illustrates the distribution of selections across different categories, providing insights into variability and central tendencies. The median selection count for each category is represented by the central line within each box, while the interquartile range (IQR) highlights the spread of selections. Categories with a wider IQR exhibit greater variability in preferences, while those with narrower IQRs indicate more consistent selection patterns. Additionally, the presence of outliers, represented by rhombus (diamond) markers outside the whiskers of the box plot, suggests that certain images within specific categories received significantly higher or lower selections than the majority. These outliers exceed 1.5 times the IQR beyond the quartiles, indicating that while most selections fall within a predictable range, a few images attracted significantly more or fewer selections than expected.

Box Plot Analysis

Notably, some categories demonstrate higher median selection counts, while others show greater dispersion. However, as indicated by the earlier ANOVA results, the differences in selections across categories are not statistically significant. This suggests that while some categories may have received slightly more selections on average, there is no strong preference for one category over another. The lack of a distinct pattern in the box plots aligns with the clustering results, reinforcing the idea that other factors may have played a larger role in influencing participant selection behavior. The presence of outliers further suggests that some images may have had unique features that resonated strongly positively or negatively with participants, warranting further qualitative analysis to determine what specific characteristics drove these extreme selections.

Integration of Qualitative and Quantitative Findings

The research design provided a multilayered understanding of how educational stakeholders engage with school architecture. Qualitative findings describe the subjective meanings and theoretical values embedded in architectural preferences, while quantitative results show the statistical trends in preference patterns, revealing broader preferences and potential biases in the evaluation of educational spaces. By combining interpretive and statistical perspectives, this study enhanced the reliability of its findings, ensuring that both explicitly stated preferences and implicit selection behaviors contribute to the final analysis. The mixed methods approach thus allowed for a more comprehensive exploration of how educational stakeholders conceptualize and prioritize architectural elements in school environments.

Emergent Grounded Theory

Supported by the data and findings, the emergent grounded theory in this study refines existing understandings of school design by emphasizing that stakeholders’ preferences for school design are driven by the degree to which spaces support collaboration, adaptability, and social engagement, rather than by conventional architectural categories. This aligns with social constructivist perspectives (Vygotsky, 1934; Lefebvre, 1974), which argue that learning environments actively shape pedagogical practices. Additionally, the findings extend Woolner’s (2015) argument that school design should be a collaborative effort informed by educational stakeholders’ preferences. The theory suggests that future school designs should incorporate student-centered and flexible features, reinforcing the claim (Higgins et al.’s, 2005) that school environments directly impact learning outcomes.

Discussion

The clustering analysis, in combination with qualitative coding, suggests stakeholders gravitate toward spaces perceived to offer instructional structure and opportunities for collaboration. Since the study did not categorize images by spatial openness, these insights should be considered interpretive rather than definitive. While preference patterns indicate an inclination toward defined instructional zones, the study does not provide direct statistical evidence comparing fully open spaces to more structured environments. While this study did not explicitly measure alignment with frameworks such as Project-Based Learning (PBL) or Universal Design for Learning (UDL), the emphasis on flexible, student-centered environments suggests that educational leaders should continue to advocate for learning spaces that facilitate a range of pedagogical approaches.

Enhancing Instructional Functionality Through Design

One of the most significant findings is that stakeholders value learning environments that balance flexibility with instructional structure. Clustering analysis, in combination with qualitative feedback, suggests a perceived preference for design features that support supervision and structured instructional zones while allowing for collaboration. However, while these trends emerge from the data, they should not be interpreted as definitive evidence of a universal preference for structured environments over fully open ones. Instead, they suggest that adaptability in design is essential enabling instructional structure when needed while maintaining openness to support collaboration.

Additionally, the study highlights the need for structured flexibility. While open, adaptable spaces foster collaboration, research and category clustering data indicate that fully open-plan environments present challenges for classroom management, noise control, and

instructional structure (Hunter, 2019). This reinforces the idea that school leaders must advocate for instructional functionality at every level, ensuring that space supports pedagogical purpose. The study also reinforces that natural light and acoustics are critical components of effective learning environments. While natural light was frequently identified as a top priority in educational stakeholders preferences, concerns about noise control also emerged in qualitative feedback. Stakeholders emphasized that acoustics play a crucial role in instructional effectiveness, particularly in collaborative environments.

While no single category emerged as statistically preferred, several highly rated images featured configurations that supported both individual and collaborative learning. This suggests that stakeholders value multifunctional environments that support multiple teaching strategies rather than fixed, one-size-fits-all layouts. As school leaders envision new and renovated spaces, they should prioritize spaces that enable teachers to seamlessly shift between teacher-led instruction, small group discussions, and hands-on project work. Prioritizing structured flexibility, social engagement, optimal lighting and acoustics, and diverse instructional models will lead to spaces that genuinely enhance learning outcomes.

Leveraging Data to Inform Design Innovation

For school leaders making decisions about future facilities or renovations, this study offers timely insights into how physical learning environments can better support instructional goals. The findings show that stakeholders particularly educators tend to favor specific architectural features like flexibility, visibility, and natural light, rather than fixed design categories. This suggests that school leaders should prioritize adaptable, student-centered environments that align with a range of teaching strategies rather than rigid classroom templates. This study encourages school leaders to see facility design not just as a construction project, but

as a strategic investment in instructional innovation and student outcomes. By integrating data and evolving pedagogical needs, schools can create learning environments that are as responsive and dynamic as the education they aim to deliver.

Supporting Future Research and Policy

The findings of this study emphasize the critical importance of educators’ advocacy during the planning of school facilities. When school leaders are engaged, learning environments are more likely to reflect both instructional priorities and the day-to-day realities of classroom use. This alignment helps ensure that new or renovated spaces are not only visually compelling but functionally effective for teaching and learning. Without this kind of advocacy, there is a risk that well-intentioned design decisions may not fully support key educational outcomes, such as student engagement, instructional adaptability, and classroom management. School leaders can help prevent mismatches between design vision and real instructional needs. The results of this study offer actionable insights for schools and districts seeking support for new facilities. Even though statistical tests did not show significant preference differences across categories, the consistent stakeholder focus on adaptable, collaborative, and light-filled environments reinforces the broader demand for flexible and student-centered spaces. These themes can strengthen the educational rationale behind bond initiatives, grant proposals, or facility master plans demonstrating that investments in school infrastructure are grounded in current pedagogical research. Ultimately, this research encourages school leaders to treat facility planning as a core part of instructional strategy, ensuring that future investments in space directly contribute to student success and educational innovation.

Conclusion

This study deepens the understanding of how school architecture shapes educational experiences, emphasizing that effective learning environments are co-imagined by those who design, use, and lead within them. The findings highlight the need to view school design not merely as a matter of construction or aesthetics, but as an instructional endeavor that brings together educators, administrators, and community stakeholders. Learning spaces are not neutral containers; they actively influence pedagogy, behavior, and engagement. As such, they must be treated as extensions of the curriculum and the instructional mission of the school. The results point to a strong and consistent preference for environments that support interaction, flexibility, and instructional versatility, qualities that align with modern pedagogical approaches such as project-based learning, inquiry-based instruction, and differentiation. School leaders can draw from these insights to guide planning efforts that prioritize spaces capable of adapting to multiple teaching strategies and learning modalities, particularly those that promote engagement, collaboration, and student-centered learning.

As districts continue to invest in new or reimagined facilities, this study encourages educational leaders to ground those efforts in instructional priorities and local pedagogical values. Doing so not only increases the likelihood of community buy-in but also ensures that capital investments serve long-term educational goals. Tailoring design choices to reflect the unique cultural, instructional, and developmental needs of each school community will ensure that future learning environments are not only innovative in appearance but also deeply aligned with how teaching and learning happens every day. Ultimately, this research affirms that school architecture is educational leadership in physical form. When leaders recognize space as a

strategic tool for learning, they can more effectively shape environments that inspire, include, and elevate every learner.

References

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07_Designing for Learning

DESIGNING FOR

LEARNING:

WHAT SCHOOL LEADERS SHOULD KNOW ABOUT STAKEHOLDER SPACE PREFERENCES

Designing for Learning: What School Leaders Should Know

About Stakeholder Space Preferences

Abstract

Designing for Learning: What School Leaders Should Know

About Stakeholder Space Preferences

Literature Review

Educator Preferences and Instructional Priorities

Spatial Design as a Leadership Decision

Gaps and Contributions

Theoretical Framework

Quantitative Methods within Theoretical Framework

Conceptual Model

Understanding Educational Stakeholders Preferences in School Architectural Design

Layer I: Architectural Design Features (Independent Variables)

Layer II: Mediating Factors (Perceived Values & Instructional Models)

Layer III: Preferences & Statistical Trends (Dependent Variables)

Interpreting the Model

Methodology

Qualitative Analysis (Visual Grounded Theory & Thematic Analysis)

Quantitative Analysis

Mixed Methods Approach

Data Collection

Coding Framework for Visual Data Analysis

Coding Process for Image Analysis

Open Coding: Identifying Key Architectural Features

Axial Coding: Grouping Features into Thematic Categories

Selective Coding: Linking Themes to Educational Stakeholders Preferences

Connection Between Qualitative Codes and Quantitative Analysis

Statistical Examination of Preference Patterns

Differences in Preference Across Architectural Boards

Identifying Preference Patterns

Integration of Quantitative and Qualitative Findings

Results

Qualitative Analysis of Implied Teacher Preferences and Instructional Models

Features of the Most Preferred Images

Relationship Between Preferred Features and Instructional Models

Inquiry-Based Learning

Quantitative Results

ANOVA Results and Hypothesis Testing

Box Plot Analysis

Integration of Qualitative and Quantitative Findings

Emergent Grounded Theory

Discussion

Enhancing Instructional Functionality Through Design

Leveraging Data to Inform Design Innovation

Supporting Future Research and Policy

Conclusion

References