Spatial Evaluation of Primary Schools Using Biophilic Design Elements: A Multi-Criteria Decision-Making Approach

Abstract

The natural environment plays a vital role in children’s health, influencing their physical, emotional, social, psychological, and spiritual well-being. Maintaining a continuous relationship with nature is essential for children and is a key consideration for professionals, such as architects, urban and interior designers, and landscape architects. School design should balance students’ abilities and environmental challenges and offer opportunities to alleviate mental fatigue, supporting sustained learning. The well-known architectural approach, biophilic, fostering a stronger connection between nature and humans, can significantly enhance students’ learning experiences and mental health in school settings. However, implementing this style in Iranian primary schools has largely been overlooked despite its potential to develop a more peaceful and dynamic environment. This research ranked several schools in northern Iran based on biophilic criteria to help authorities identify which schools require improvements. To this end, biophilic design elements in schools were identified through a literature review and provided to research experts. The most important criteria for evaluating and prioritizing options (schools) were selected based on their opinions. Subsequently, each criterion’s importance (weight) was determined using pairwise comparisons, and, finally, the schools were prioritized using the TOPSIS method.

1. Introduction

Over the past years, a large number of studies have highlighted the critical role of the natural environment in promoting children’s health [1,2,3,4]. According to numerous studies, children’s emotional, social, physical, psychological, and spiritual well-being are positively affected by nature [5,6,7]. However, some researchers indicate that the number of children playing outside, particularly in nearby residential nature areas, is declining, resulting in a disconnection between children and nature [8]. When children interact less with nature, they are uninformed and ignorant of nature. In particular, children who lack hands-on experience with nature exhibit biophobia or negative sentiments toward nature [9]. Studies have also revealed that children are less interested in natural areas and activities when they have less direct physical contact with nature. In addition, children who lack exposure to nature will feel estranged from the natural world and less empathetic [10].

As children grow up in increasingly urbanized and indoor environments, a critical question arises: how can they form essential personal connections with nature and develop an appreciation for it if their formative years are spent detached from the natural world? Building this connection is vital to ensure that when these children become responsible members of society and leaders of tomorrow, they prioritize and protect the environment. Consequently, maintaining a meaningful relationship with nature is a fundamental aspect of environmental and social sustainability and a pressing concern for professionals who design our spaces, including architects, urban designers, landscape architects, and interior architects. It is imperative to thoughtfully design spaces where children spend their time, fostering positive interactions with nature.

Moreover, school design should balance students’ capabilities with environmental challenges and provide opportunities to reduce mental fatigue, supporting their learning ability [11]. Incorporating greenery into school playgrounds is a refuge for stress relief, enhances children’s social skills, and creates spaces for restoration. Additionally, classrooms with natural views have improved students’ perceptions of courses and boosted their academic performance [12]. Alongside various factors that influence learning [13], exposure to nature has been linked to increased productivity, focus, vitality, and reduced stress—factors collectively enhancing students’ learning [14].

Biophilic design translates this understanding into a practical approach to spatial design, aiming to foster a connection to nature and, in turn, improve human well-being. The biophilic design style, as an approach in architectural design that strengthens the connection between humans and nature, can positively impact students’ learning and mental health in schools [15]. Biophilic design can enhance students’ satisfaction and concentration by increasing interaction with natural elements. However, implementing this style in Iranian schools has largely been overlooked despite its potential to develop a more peaceful and dynamic environment.

In Iran, schools are generally categorized into two main types: public and private. Private schools, which finance their operations primarily through tuition fees paid by students and often aim for profitability, operate in a highly competitive environment. As a result, decision-makers (managers) in these schools strive to outperform their competitors in various aspects. This competitive atmosphere may be one reason, whether consciously or unconsciously, why certain elements of biophilic design (such as color, light, and form) are more commonly integrated into private schools. Figure 1a,b illustrates some examples of biophilic elements present in private schools in Iran.

On the other hand, public schools in Iran are funded by the government and typically do not charge tuition fees. Unfortunately, biophilic elements are almost entirely neglected in these schools, as shown in some examples in Figure 1c,d. This shortcoming has also been acknowledged by experts in the domestic research literature [16,17,18].

Regardless of the underlying causes of this neglect, which lie beyond the scope of the present study, the primary aim of this research is to evaluate and rank public schools based on biophilic criteria to assist authorities in identifying which schools require improvements. Accordingly, this study seeks to answer the following questions:

• What are the biophilic elements (criteria) for evaluating the spatial qualities of schools?
• How can schools be architecturally prioritized using a scientific approach?

To this end, biophilic design elements (criteria) in schools will first be determined through a literature review and provided to research experts. The most important elements for evaluating and prioritizing options (schools) will be selected based on their opinions. Subsequently, each element’s importance (weight) will be determined using pairwise comparisons, and, finally, the schools will be prioritized by applying the TOPSIS tool. Accordingly, the paper structure will be as follows: The research background will be reviewed in the following section. After that, the research method will be introduced in the next section, and then a case study will be examined. The final section of this document will present the results.

2. Literature Review

In the research background, a significant number of studies have addressed the topic of biophilic design from various aspects and, as a result, have introduced or utilized various components and elements for this type of design. Considering that many of the elements introduced or utilized in previous studies are similar, we will review some of these studies below. A summary of the identified elements from the reviewed studies is presented in

Table 1. Literature summary on biophilic elements.

Row	Biophilic Elements	Reference
1	Actual nature features (air, water, etc.); natural shapes and forms; natural patterns and processes; color and light; ecology; and human–nature relationship	[15]
2	Visual and non-visual connection with nature; non-rhythmic sensory and stimuli; thermal and airflow variability; presence of water; dynamic and diffuse light; connection with natural systems; biomorphic forms and patterns; material connection with nature; and complexity and order	[19]
3	Non-visual connection; presence of water; visual connection; light and space; and natural patterns	[20]
4	Visual and non-visual connection with nature; non-rhythmic sensory and stimuli; thermal and airflow variability; presence of water; dynamic and diffuse light; connection with natural systems; biomorphic forms and patterns; material connection with nature; complexity and order; prospect, refuge, mystery, and risk/peril	[21]
5	Visual connection with nature; complexity and order; prospect; mobility and wayfinding; and integration of parts to the whole	[22]
6	Light; plants; natural landscapes and ecosystems; nature image; natural colors; information richness (mysterious environments); natural geometries (fractals); landscape; and organized complexity (circulation)	[17]
7	Daylight, water, air, plants, animals, landscape, weather, fire, images, colors and texture, shape and forms, information richness, age, change, and the patina of time, natural geometrics, simulated natural light and air, biomimicry, prospect and refuge, organized complexity, mobility, transitional spaces, place, and integrating the parts to create the whole	[23]
8	Visual and non-visual connection with nature, non-rhythmic sensory stimuli, thermal and airflow variability, presence of water, dynamic and diffuse light, connection with natural systems, biomorphic forms and patterns, material connection with nature, complexity and order, prospect, refuge, mystery, and risk/peril	[24]
9	Environmental features (color, water, air, sunlight, plants, animals, natural material, views and vistas, etc.), natural shapes and forms, natural patterns and processes, light and space, place-based relationships, and human–nature relationships	[25]
10	Landscapes and ecosystems, natural colors, plants, natural ventilation, daylighting, images of nature, mobility and wayfinding “space layout”, natural shapes and forms, and natural materials	[26]
11	Natural light, water, plants, weather, natural scenery, ecosystems, the image of nature, natural materials, the geometry of nature, view and shelter, and attachment to cultural and ecological sites	[27]

. It is worth mentioning that the elements identified at this stage will be provided to research experts to select some of them as criteria for evaluating and ranking schools.

Ref. [15] assessed the impact of biophilic design on student efficiency. It aimed to assess study environments for biophilic design and ascertain whether biophilic design influences students’ preferences and academic productivity. Ref. [19] explored children’s preferences for biophilic elements and their impacts on well-being, emphasizing the importance of incorporating direct and indirect biophilic experiences into educational environments. Ref. [20] examined children’s preferences for biophilic components in primary school architecture in Malaysia, Indonesia, and Thailand. The online survey questionnaire served as a qualitative approach to data collection. The results indicated that biophilic components benefit students from all three nations, but with varying preferences for specific elements. Ref. [21] examined biophilic architecture with preteens’ elementary school developmental changes. Along with their interdependence, it addressed the body’s tight link with its surroundings. The study examined biophilic design elements that support preteen (5–12) growth. Ref. [22] examined the presence of biophilic design elements and the extent of natural views through permeable openings in schools across Iraq by analyzing their spatial layouts and configurations. A mixed quantitative approach was employed, incorporating various tools such as questionnaires to compare students’ and teachers’ perceptions regarding the availability of biophilic features. The findings revealed that while such elements are present in schools, they exist in insufficient quantities. Using the Delphi method, ref. [17] explored the physical elements that effectively reduce stress in school settings. The results indicated that integrating nature and environmental features was the most significant. Light, plants, natural landscapes, and ecosystems followed as essential sub-criteria. Ref. [23] elucidated the fundamentals of biophilic design theory and evaluated its design patterns within educational settings to determine which features most effectively enhance user productivity and well-being. The quantitative descriptive technique utilized a questionnaire to facilitate the design and practical implementation of the biophilic elements by architects and designers. The study determined that certain architectural features are more critical than others for educational buildings, including daylight, water, air, vegetation, landscapes, mobility, and the integration of components to form a cohesive whole. Ref. [24] examined the biophilic components inside a campus and three educational buildings to assess the propensity for interaction between nature and humans. A survey was administered to assess the knowledge of biophilic components among inhabitants of educational buildings and campuses. Ref. [25] explored the growing body of research and emerging aspects of biophilic design in architecture, which could deepen the understanding of biophilic design principles and clarify the cognitive benefits of biophilia in creating educational spaces for children. The research employed a mixed-method approach, including a background review, analysis of global case studies, and individual interviews. Ref. [26] examined biophilic design strategies for enhancing student health and performance in elementary schools. The results showed that natural elements like light and vegetation are crucial for creating conducive learning environments. Ref. [27] proposed a spatial design methodology for children’s libraries, incorporating various natural features grounded in biophilic design intent. A survey questionnaire was distributed to 261 caregivers associated with children’s libraries. The findings revealed that children’s libraries must provide a natural experience, taking into account the natural environment.

3. Methodology

The proposed approach applies a combination of Pairwise Comparison Matrix (PCM) and TOPSIS to solve the problem. This combination is the most widely used approach among MCDM techniques in other fields [28,29,30,31,32,33], and accordingly, this approach is also employed in this study. It is worth noting that in this study, a group of experienced individuals in the relevant field are considered research experts. The biophilic elements identified through the literature review will be presented to these experts so they can select the evaluation criteria and determine the relative importance (weight) of each. Additionally, the values of the decision matrix will also be provided by the research experts. The 9-step algorithm is as follows:

• Step 1: Constructing the PCM

Initially, the evaluation criteria, which will be used to assess the decision options, are organized into a PCM (See Equation (1)).

$$C=\left[\begin{array}{cccc}1& c_{12}& c_{13}& \begin{array}{cc}\cdots & c_{1n}\end{array}\\ {\displaystyle \frac{1}{c_{12}}}& 1& c_{23}& \begin{array}{cc}\cdots & c_{2n}\end{array}\\ {\displaystyle \frac{1}{c_{13}}}& {\displaystyle \frac{1}{c_{23}}}& 1& \begin{array}{cc}\cdots & c_{3n}\end{array}\\ \begin{array}{c}⋮\\ {\displaystyle \frac{1}{c_{1n}}}\end{array}& \begin{array}{c}⋮\\ {\displaystyle \frac{1}{c_{2n}}}\end{array}& \begin{array}{c}⋮\\ {\displaystyle \frac{1}{c_{3n}}}\end{array}& \begin{array}{cc}\begin{array}{c}\ddots \\ \cdots \end{array}& \begin{array}{c}⋮\\ 1\end{array}\end{array}\end{array}\right].$$

(1)

where rows and columns represent the criteria being compared, and each entry reflects the criterion’s relative importance over another. In Equation (1), if c_ij is the value comparing criterion i to criterion j, then

$$c_{ij}={\displaystyle \frac{1}{c_{ji}}}.$$

(2)

An ordinal scale is used to compare two criteria, as described in

Table 2. The comparison scale [32].

Intensity	Strength Preference
1	No preference
3	Slight preference
5	Significant preference
7	High preference
9	Maximum preference
2, 4, 6, 8	Values between the main categories
Reciprocal	${\displaystyle \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$x$}\right.}$ for inverse values

.

• Step 2: Calculating the Criteria Weights

The criteria weights are obtained using Equation (3):

$$w_i={\displaystyle \frac{1}{n}}\sum _{j=1}^n{\displaystyle \frac{c_{ij}}{{\sum }_{i=1}^n{c}_{ij}}}.$$

(3)

In Equation (3), every element in a column is first divided by the sum of that column. Next, the criteria weights are determined by calculating the average of the rows in the matrix.

It is worth noting that at the end of this step, the inconsistency ratio (IR) of the decision matrix must be calculated. If the IR exceeds 0.10, the pairwise judgments should be reviewed, and any evaluation inconsistencies must be resolved.

• Step 3: Constructing the Decision Matrix

After defining the options (O_m) and the criteria (C_n), the decision matrix is formed according to Equation (4):

$$X=\left[\begin{array}{cccc}{o}_{11}& o_{12}& \cdots & o_{1n}\\ o_{21}& o_{22}& \cdots & o_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ o_{m1}& o_{m2}& \cdots & o_{mn}\end{array}\right].$$

(4)

where o_ij is the i-th option value concerning the j-th criterion.

• Step 4: Normalizing the Decision Matrix

Using Equation (5), the decision matrix is normalized as follows:

$$r_{ij}={\displaystyle \frac{o_{ij}}{\sqrt{{\sum }_{i-1}^m{o}_{ij}^2}}}.$$

(5)

The normalized matrix is R = $\left[r_{ij}\right].$

• Step 5: Weighting the Normalized Matrix

In this step, based on Equation (6), the weights obtained in Step 2 are multiplied by the normalized matrix from Step 4 to form the weighted normalized matrix.

$$v_{ij}=w_j.{\mathrm{r}}_{ij}$$

(6)

The weighted normalized matrix is V = [v_ij], where w_j is the j-th criterion weight (∑w_j = 1).

• Step 6: Forming Positive and Negative Ideal Solutions

The positive and negative ideal solutions ($O^{+}$ and $O^{-}$) are formed by applying Equations (7) and (8), respectively.

$$O^{+}=\left\{v_1^{+},{\mathrm{v}}_2^{+},\dots , {\mathrm{v}}_n^{+}\right\},{\mathrm{v}}_j^{+}=\max \left(v_{ij}\right) \mathrm{for} \mathrm{favorable} \mathrm{criteria}, \min \left(v_{ij}\right) \mathrm{for} \mathrm{unfavorable} \mathrm{criteria}.$$

(7)

$$O^{-}=\left\{v_1^{-},{\mathrm{v}}_2^{-},\dots , {\mathrm{v}}_n^{-}\right\},{\mathrm{v}}_j^{-}=\min \left(v_{ij}\right) \mathrm{for} \mathrm{favorable} \mathrm{criteria}, \max \left(v_{ij}\right) \mathrm{for} \mathrm{unfavorable} \mathrm{criteria}.$$

(8)

• Step 7: Calculating Separation Distances (SDs)

Each option’s distance from the ideal solutions can be computed using Equations (9) and (10), respectively.

$${SD}_i^{+}=\sqrt{\sum _{j=1}^n(v_{ij}-v_j^{+}{)}^2}.$$

(9)

$${SD}_i^{-}=\sqrt{\sum _{j=1}^n(v_{ij}-v_j^{-}{)}^2}.$$

(10)

Equation (9) shows each option’s distance from O⁺, while Equation (10) shows its distance from O⁻.

• Step 8: Calculating the Relative Closeness to the Ideal Solution (RCIS)

In this step, each option’s closeness to the ideal solution (CL_i) can be computed using Equation (11).

$${CL}_i={\displaystyle \frac{{SD}_i^{-}}{{SD}_i^{+}+{SD}_I^{-}}}, 0\le {CL}_i\le 1.$$

(11)

A higher CL_i indicates greater closeness to the ideal solution.

• Step 9: Ranking the Options

The options can ultimately be ranked using the CL_i index, with the most favorable option assigned the highest CL_i value, and the others are arranged in descending order.

4. Findings

This study primarily focuses on ranking all existing schools in one of the counties in the eastern part of Gilan Province, Iran, based on their utilization of biophilic elements. Despite the presence of environmental advantages due to the region’s temperate and humid climate and its rich flora and fauna (which provide a competitive edge compared to other parts of the country), the design of schools in this area has not adequately incorporated these features. As the main decision-making body on such matters, the local Department of Education needs to pay particular attention to this issue. Accordingly, this study examines and ranks all six schools that exist in the region based on their integration of biophilic elements.

To address the research problem, we assembled a team of experts, including two experienced education administrators with over 15 years of professional experience and an architecture specialist with over 10 years of academic experience as a university faculty member. We provided them with biophilic criteria derived from the literature and asked them to identify the most significant ones. Nine final criteria were selected during a session with these experts (see

Table 3. Criteria selected by experts.

Criteria	Supporting References
Color	[15,19,20,21,22,23,24,25,26,27]
Light	[15,20,22,23,25,26]
Plant	[15,20,22,23,25,26,27]
Ecology	[15,19,20,21,23,24,25,26,27]
View	[19,20,21,22,23,24,25,26,27]
Fresh Air	[19,20,21,22,23,24,25,26,27]
Water	[19,20,21,22,23,24,25,27]
Animal	[15,20,22,23,25]
Shapes	[15,19,20,21,22,23,24,25,26]

).

In line with the methodology outlined in the previous section, the study experts initially prepared a pairwise comparison matrix to specify the criteria weights (importance). The results of this step are presented in

Table 4. PCM.

PCM	Color	Light	Plant	Ecology	View	Fresh Air	Water	Animal	Shapes
Color	1	0.20	0.25	5	2	0.33	0.50	4	3
Light	5	1	2	9	6	3	4	8	7
Plant	4	0.50	1	8	5	2	3	7	6
Ecology	0.20	0.11	0.12	1	0.25	0.14	0.17	0.50	0.33
View	0.50	0.17	0.20	4	1	0.25	0.33	3	2
Fresh Air	3	0.33	0.50	7	4	1	2	6	5
Water	2	0.25	0.33	6	3	0.50	1	5	4
Animal	0.25	0.12	0.14	2	0.33	0.17	0.20	1	0.50
Shapes	0.33	0.14	0.17	3	0.50	0.20	0.25	2	1

.

In the second step, using Equation (3), the criteria weights were computed as displayed in

Table 5. Criteria weights.

Criteria	Color	Light	Plant	Ecology	View	Fresh Air	Water	Animal	Shapes
Weights	0.08	0.31	0.22	0.02	0.05	0.15	0.11	0.03	0.04

.

Table 5 indicates that, according to the experts, the “light” criterion, with a weight of 0.31, and the “plant” criterion, with a weight of 0.22, are the most critical factors in evaluating and ranking schools. It is worth noting that the IR was 0.03 (less than 0.10), indicating no inconsistency in the experts’ judgments, making the results reliable.

The decision matrix, displayed in

Table 6. The decision matrix.

Options/ Indicator	Color 1	Light 2	Plant 3	Ecology 4	View 5	Fresh Air 6	Water 7	Animal 8	Shapes 9
1	85	300	40	9	8	50	7	3	7
2	70	500	35	8	6	45	8	4	8
3	90	250	50	7	9	60	6	2	6
4	80	400	45	10	7	40	9	5	8
5	75	350	30	6	5	55	5	3	5
6	95	450	55	9	9	35	8	6	9

¹ Percentage of compliance with biophilic color standards. ² Intensity of natural light indoors (Unit: lux). ³ Percentage of green space relative to the total school area. ⁴ Sustainability index of the ecological system (Unit: 1 to 10). ⁵ Quality of natural surroundings visible from the school (Unit: 1 to 10). ⁶ Average indoor air quality index (Unit: AQI, Air Quality Index). ⁷ Quantity and quality of water resources available in the school (Unit: 1 to 10). ⁸ Number of animal species available or accessible. ⁹ Usage level of natural shapes in school design (Unit: 1 to 10).

, was created in the third step.

In the fourth step, using Equation (5), the data in the table were normalized. The results of this step are displayed in

Table 7. The normalized decision matrix.

Options/ Indicator	Color	Light	Plant	Ecology	View	Fresh Air	Water	Animal	Shapes
1	0.418	0.318	0.377	0.444	0.436	0.423	0.392	0.302	0.392
2	0.345	0.531	0.330	0.395	0.327	0.381	0.448	0.402	0.448
3	0.443	0.265	0.471	0.345	0.491	0.508	0.336	0.201	0.336
4	0.394	0.425	0.424	0.493	0.382	0.338	0.504	0.503	0.448
5	0.369	0.372	0.283	0.296	0.273	0.465	0.280	0.302	0.280
6	0.468	0.478	0.518	0.444	0.491	0.296	0.448	0.603	0.504

.

In the fifth step, using Equation (6), the normalized weighted matrix was constructed. The results of this step are displayed in

Table 8. The normalized weighted decision matrix.

Options/ Indicator	Color	Light	Plant	Ecology	View	Fresh Air	Water	Animal	Shapes
1	0.032	0.098	0.082	0.008	0.023	0.065	0.043	0.008	0.015
2	0.026	0.163	0.072	0.007	0.017	0.059	0.049	0.010	0.017
3	0.034	0.081	0.103	0.007	0.026	0.078	0.037	0.005	0.012
4	0.030	0.130	0.093	0.009	0.020	0.052	0.055	0.013	0.017
5	0.028	0.114	0.062	0.006	0.015	0.072	0.030	0.008	0.010
6	0.036	0.147	0.113	0.008	0.026	0.046	0.049	0.016	0.019

.

In the sixth step, the ideal solutions were determined using Equations (7) and (8). Since all criteria are favorable in this study, the maximum value was used for the positive and the minimum value for the negative ideals (the row of maximum values per column and the row of minimum values per column, respectively).

$$O^{+}=\left[0.036, 0.163, 0.113, 0.009, 0.026, 0.078, 0.055, 0.016, 0.019\right]$$

$$O^{-}=\left[0.026, 0.081, 0.062, 0.006, 0.015, 0.046, 0.030, 0.005, 0.010\right]$$

In the seventh step, the SDs, shown in

Table 9. SDs.

Options	${\mathit{S}\mathit{D}}_{\mathit{i}}^{+}$	${\mathit{S}\mathit{D}}_{\mathit{i}}^{-}$
1	0.075	0.037
2	0.048	0.086
3	0.085	0.055
4	0.047	0.064
5	0.077	0.042
6	0.037	0.087

, were calculated using Equations (9) and (10).

In the eighth step, using Equation (11), the RCIS was calculated. Finally, the ranking was performed according to the (CL_i) index.

Table 10. Final ranks.

Options	CLi	Rank
1	0.330	6
2	0.640	2
3	0.391	4
4	0.576	3
5	0.351	5
6	0.703	1

shows the results of steps 8 and 9.

Based on Table 10, it can be observed that School No. 6 is the closest option to the ideal and is ranked first, followed by Schools No. 2 and No. 4 in second and third places, respectively. Additionally, the results indicate that Schools No. 3, No. 5, and No. 1 (ranked fourth to sixth, respectively) have a significant distance from the ideal option and therefore require more attention.

5. Discussion

The findings of this study can be discussed from several perspectives. First, as demonstrated in the literature review, biophilic elements that can be considered in school design are diverse. Therefore, selecting the most important elements (in this study, the evaluation criteria) for ranking schools is crucial, as it directly affects the analysis results. In this study, the research experts selected the biophilic elements from among those identified in the literature. It is also important to note that, according to the decision-making literature [32], the problem owner(s) may themselves take on the role of experts in defining the criteria, assigning weights, and completing the decision matrix. In such cases, the aforementioned concerns may not apply.

Another point of discussion relates to the determination of the criteria weights. In this study, the experts used pairwise comparisons to assign weights. Given the significant influence of these weights on the outcomes, several considerations are worth noting. The method used in this study is basic, intuitive, and easy to implement. However, it has certain shortcomings. One common issue is the large difference it often produces between weights (e.g., the “light” criterion with a weight of 0.31 versus the “view” criterion with 0.05), even though such stark differences may not reflect reality. Another limitation is its exclusive reliance on expert judgment. Although objective weighting methods (such as entropy) are available in the decision-making literature, a combination of subjective and objective approaches may provide a more balanced outcome. Additionally, accounting for uncertainty in expert judgments—using fuzzy or extended fuzzy sets—could improve the reliability of the results.

Lastly, the choice of the TOPSIS method for prioritization is also worth discussing. In this study, we evaluated and ranked all schools in a small city consisting of six schools. The TOPSIS method does not limit the number of alternatives and can be easily applied to a larger set using Excel or more specialized software. However, the nature of the method itself warrants attention. TOPSIS is classified as a compensatory method within the family of multi-criteria decision-making techniques. In compensatory methods, poor performance on one criterion can be offset by strong performance on another. For example, School No. 2 performed poorly on the “plant” criterion yet ranked second overall. In some decision-making scenarios, certain criteria may be so critical that underperformance in those areas cannot be compensated for by strengths elsewhere. In such cases, using TOPSIS may not be advisable.

6. Conclusions

Biophilic design incorporates natural elements into buildings and interior spaces, enhancing the bond between individuals and the natural world. It should be considered in schools because it enhances learning by improving focus and creativity, promotes well-being by reducing stress and anxiety, encourages social interaction among students, fosters environmental awareness and stewardship, improves indoor air quality, and creates positive associations with the learning environment. By incorporating biophilic design, schools can significantly enhance the educational experience, contributing to students’ overall development and well-being.

In this study, due to the limited attention of urban authorities to incorporating biophilic elements, we evaluated and prioritized schools in the examined region. We first identified biophilic criteria by reviewing the research literature. These criteria were then presented to the study’s experts, who selected the most important ones. After discussion and review, the experts identified nine primary criteria. Subsequently, a pairwise comparison matrix determined each criterion’s importance (weights). In the next step, a decision matrix was formed for six regional schools, and the schools were prioritized using the TOPSIS method. The results showed that Schools No. 6, No. 2, and No. 4 had relatively better conditions, while the other schools require more attention to incorporate biophilic criteria.

According to the experts in this study, the combined weight (importance) of the two criteria, light and plants, exceeds the total weight of the remaining seven criteria. Therefore, schools that perform poorly in these two areas, specifically Schools No. 3, No. 1, and No. 5, should prioritize improvements in these aspects. Regarding the other highly influential criterion, plants, Schools No. 5, No. 2, and No. 1 require significant improvements. Based on the data, School No. 2, which ranked second overall, could potentially move into the top position if it improves its performance on this criterion. In terms of the fresh air criterion, School No. 6, despite ranking first overall, needs substantial improvement, as its weakest performance (among all criteria and in comparison with the other schools) was observed in this criterion. Schools No. 4 and No. 2 also need to take corrective action concerning this criterion. Concerning the next influential criterion, water, no major weaknesses were found; however, School No. 5 could take steps to enhance its performance in this area. For the remaining criteria, which had lower weights in this study, all schools showed relatively good performance.

Overall, decision-makers and relevant authorities can use the results to identify existing weaknesses in schools and take action to improve conditions. Moreover, this method allows for the comparison of schools based on various criteria. School administrators themselves can benefit by identifying their strengths and weaknesses (particularly in comparison to other schools), working not only to address their deficiencies but also to maintain their advantages. Of course, it should also be noted that when generalizing the study’s findings, it is important to consider that cultural, social, and other contextual differences may affect the selected criteria, their weights, and the resulting rankings. In addition, the perspectives of key stakeholders, namely students, might differ from those of the experts.

For the first time, this study ranked schools in a specific geographic region using multi-criteria decision-making approaches based on biophilic indicators. Despite its various advantages, this study also has limitations that could be addressed in future research. The selection process can vary depending on the expert’s personal preferences (and, of course, experience). Thus, when addressing a multi-criteria decision-making problem like the one presented in this study, two points should be considered: first, the selection of research experts should be limited to individuals with sufficient knowledge and experience (which itself can be framed as a multi-criteria decision-making problem); second, once the expert group is formed, the process and criteria for selecting a limited number of elements from a larger pool can also be structured as a separate problem.

In the present study, biophilic criteria were selected during a session with the experts; in the future, systematic approaches such as the Delphi method could be employed for criterion selection. Additionally, the criteria weights were determined crisply, which does not account for the uncertainty and ambiguity in the experts’ judgments. Therefore, using fuzzy sets and their extensions could be considered in future studies. Furthermore, applying other techniques, like the Best-Worst Method (BWM), and comparing their results with the present study could prove beneficial. In our next study, we plan to identify biophilic design elements for schools and determine their relative importance using a systematic method that combines objective and subjective models with the capability of handling uncertainty in the experts’ verbal judgments.