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Article

A Web-Based Learning Model for Smart Campuses: A Case in Landscape Architecture Education

by
Gamze Altun
* and
Murat Zencirkıran
Landscape Architecture Department, Faculty of Agriculture, Bursa Uludağ University, Bursa 16059, Turkiye
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(24), 11203; https://doi.org/10.3390/su172411203 (registering DOI)
Submission received: 6 November 2025 / Revised: 30 November 2025 / Accepted: 9 December 2025 / Published: 14 December 2025
(This article belongs to the Special Issue Sustainability-Oriented Digital Transformation and Innovative Assessments in Higher Education)
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Abstract

This study presents the development and evaluation of a Quick Response (QR) code-integrated, web-based, and GIS-supported interactive learning model designed to enhance field-based plant learning in landscape architecture education. Conducted on the Görükle Campus of Bursa Uludağ University (BUU), the research systematically inventoried 6869 individual woody plants belonging to 172 taxa, georeferenced them using GPS, and visualized the data on an interactive campus map. Unique QR codes were generated for each taxon, providing instant access to plant profiles via a web platform and the Landscape Plants mobile application. The pedagogical effectiveness of the system was evaluated through a survey administered to 158 students, yielding a high internal reliability (Cronbach’s Alpha = 0.969). The findings indicated a high level of student satisfaction and a strong positive correlation between web-based and QR code applications (r = 0.941, p ≤ 0.001). This research represents the most comprehensive campus-scale digital plant learning system in Turkey, in terms of both species diversity and individual count. It provides a scalable and sustainable smart campus model which is applicable to nature-based disciplines worldwide.

1. Introduction

In recent years, higher education institutions have increasingly adopted e-learning and web-based systems to align their educational practices with the “smart campus” paradigm [1]. Smart campuses are living laboratories that integrate digital technologies with physical infrastructure to support data-driven decision-making across administrative, academic, and operational domains, thereby improving the overall quality of learning [2]. Through digital infrastructures that manage energy and resource efficiency, security, transportation, course content, and student performance monitoring, smart campuses enable the detection of latent issues affecting user experience, support evidence-based solutions, and foster more accessible and user-friendly learning environments [3,4].
The development of e-learning programs has profoundly transformed traditional educational approaches at all levels, from early childhood to higher education and adult learning [5,6]. In contemporary learning environments, students require continuous access to course materials [7], the ability to progress at their own pace [8,9], opportunities to sustain learning across different times and spaces outside the classroom [10], the possibility to review complex content beyond class hours [11], and the capacity to individualize their learning processes [12]. These needs are particularly pronounced in nature-based disciplines such as landscape architecture, botany, and environmental education. Students in these fields must understand the morphological and ecological characteristics of plants not only at a theoretical level but also in relation to hands-on field experience; develop spatial awareness; and reinforce their knowledge by repeatedly observing plant species. E-learning environments effectively meet these continuous learning needs by offering location-independent access to information, temporal flexibility, opportunities for repeated learning, and digital content that supports field-based activities. For these reasons, higher education institutions are increasingly adopting e-learning as a flexible, accessible, and effective instructional strategy, utilizing it as a robust infrastructure that supports diverse learning paces, individualized learning pathways, and out-of-class engagement [13]. Moreover, e-learning systems provide significant potential for strengthening institutional communication, enhancing staff training, and improving performance, thereby evolving beyond an alternative to traditional instruction into a holistic solution that supports institutional learning and development strategies [14]. Face-to-face learning is constrained by spatial and temporal limitations; therefore, institutional information flow, access, content updates, and staff training typically occur at specific intervals and with limited participation. In contrast, e-learning systems ensure continuous and accelerated internal information sharing through asynchronous communication, digital content management, real-time updating, large-scale accessibility, and data monitoring capabilities [15,16]. Additionally, enabling staff to access training at their own pace and on demand eliminates the time and location constraints inherent in face-to-face instruction, thereby increasing the effectiveness of institutional learning processes [17].
The rapid development and diversification of e-learning applications, together with the widespread use of mobile technologies, have transformed mobile learning (m-learning) into a practical and accessible learning approach delivered through smartphones and tablets [18]. In this context, mobile learning has evolved beyond traditional content delivery into a structure that integrates interactive and immersive technologies—such as augmented reality (AR), virtual reality (VR), and quick response (QR) codes—and directly connects learners with their surroundings. These technologies merge digital information with real-world objects, spaces, and natural environments, offering contextual, in situ, and experiential learning opportunities that enable students to explore educational content on-site, observe and interpret phenomena, and construct meaningful knowledge [19,20,21].
QR codes reduce cognitive load by enabling students to access instructional materials instantly from field environments [22], while also supporting practical skills such as pattern recognition, observational attention, and classification. Owing to these features, QR codes allow students to interact with real specimens in natural settings—particularly in activities related to plant morphology and systematics—thus facilitating the concretization of abstract concepts. This interaction encourages students to recognize plant diversity and contributes to mitigating the phenomenon known as “plant blindness” [23,24]. Consequently, QR code-based mobile learning provides a holistic learning experience that supports learner autonomy and intrinsic motivation through both the management of cognitive load and direct environmental engagement.
Digital transformation has made learning processes more interactive and contextual in nature-based disciplines such as botany, landscape architecture, horticulture, and environmental education, integrating knowledge with the stages of observation, analysis, and application [25,26]. Within this framework, the concept of landscape represents not only physical space but also the dynamic relationships among ecological structure, visual patterns, and human perception [27]. Plants constitute the most fundamental component shaping the visual, ecological, and functional integrity of landscapes. While sustaining essential ecosystem functions [28], plants also form the formal composition of the landscapes such as color, texture, rhythm, spatial hierarchy, and scale [29]. Therefore, understanding plant form, function, and spatial distribution is critical not only for maintaining ecological balance but also for developing flexible, adaptive, and esthetically coherent design solutions in landscape architecture [30].
However, modern lifestyles and decreasing interaction with nature have led to the emergence of a phenomenon known as “plant blindness” among students, characterized by a diminished ability to notice and appreciate the plant diversity in their surroundings [31]. This phenomenon carries direct implications for landscape architecture education, as recognizing plant form, function, species differences, and spatial distribution lies at the core of professional competence [32]. Therefore, enhancing students’ botanical literacy serves as a fundamental means of strengthening both ecological awareness and design sensitivity [33]. In this context, the use of digital tools that make plant learning more accessible, interactive, and spatially meaningful holds the potential to reconnect students with nature, enhance observational awareness, and reinforce biodiversity consciousness [20,21,34].
The integration of digital platforms with field-based practices enables students to learn in a more interactive and contextual manner within natural environments. In this regard, the use of QR codes in labeling campus trees and developing digital inventory-mapping systems emerges as an innovative approach that strengthens experiential e-learning in nature-based disciplines [18,19,35,36]. QR codes allow students to examine individual plants in situ and instantly access scientific, morphological, ecological, and cultural information, thereby enriching field-based learning [18,35]. However, for such digital content to be effective, individual plants must be accurately linked to their real spatial locations. At this point, Geographic Information Systems (GIS) provide a fundamental spatial data infrastructure that enables the holistic analysis of environmental layers, such as topography, soil characteristics, microclimate, hydrological structure, and vegetation [37,38]. In landscape architecture, GIS plays a critical role in species selection, spatial organization, and the representation of plant–soil interactions, thereby contributing to the spatial accuracy and contextual integrity of QR code-based learning models [39].
In recent years, there has been a notable increase in efforts to support field-based learning experiences in nature-oriented higher education programs that utilize digital tools. In this context, QR codes are frequently preferred due to their capacity to deliver location-based content and their ease of integration with mobile devices. Patil et al. (2020) [40] digitized a botanical collection by labeling campus plant materials with QR codes linked to an online taxonomy database, allowing students to scan individual plants in the field and instantly access species information. Although some practical limitations related to mobile device use were reported, the study emphasized the system’s functionality in supporting field observations and facilitating access to information. Similarly, Pratiwi et al. (2024) [34] labeled trees on a university campus in Indonesia with web-linked QR codes, allowing users to access species information instantly. Their experimental findings demonstrated that the system was effective in presenting information and significantly supported students’ botanical learning processes. Rani et al. (2025) [41] developed a botanical information system comprising QR codes linked to species profiles across the campus, with field evaluations reporting high scanning reliability and strong user satisfaction. Collectively, these studies indicate that real-time data access enhances students’ interaction with vegetation and strengthens learning processes, demonstrating that QR code-based solutions contribute substantially to improving experiential learning outcomes in disciplines such as botany, horticulture, and environmental science.
Although the literature includes various applications integrating QR codes, most of these studies conceptualize QR codes primarily as tools that support species identification and the presentation of basic information, while integrated approaches combining spatial analysis and Geographic Information Systems (GIS) remain relatively limited. In plant learning contexts, a research gap exists regarding not only introducing species but also making their spatial distributions visible at the campus scale, which would render learning more contextual and holistic. This gap underscores the originality of the present study in both national and global contexts. Accordingly, the interactive learning system designed and implemented in this research—comprising three core components: (i) a campus-scale field inventory and digital plant database, (ii) detailed plant information cards accessible through a web-based interactive map, and (iii) a mobile learning infrastructure supported by QR codes in the field—necessitated the development and evaluation of a learning framework that integrates digital platforms into plant identification and use within the discipline of landscape architecture.
With this purpose, the current study—being the first of its scale in Turkiye in terms of species diversity and number of plant individuals—offers an innovative approach that supports students’ processes of recognizing, comprehending, and interpreting woody landscape plants within a spatial context by integrating GIS with QR code technology, thereby synchronizing field experience with digital content.

2. Materials and Methods

2.1. Study Area

The study was conducted on the Görükle Campus of Bursa Uludağ University (BUU), located in the Nilüfer district of Bursa, Turkiye (Figure 1). The campus is situated at 40°13′26″ N and 28°52′14″ E, covering an area of approximately 14.4 km2 (1440.3 ha). Comprising educational buildings, dormitories, green spaces, and recreational areas, the campus forms a complex landscape structure that serves both as an open-air learning environment suitable for cultivating diverse woody plant species and as a testbed for implementing digital learning technologies.

2.2. Material

The material of this study consisted of woody landscape plant taxa, including tree and shrub forms, identified within the landscape areas of Bursa Uludağ University Görükle Campus. These taxa were used as the primary material for developing the QR code-integrated web-based learning system. Each taxon was documented through systematic field observations and subsequently transferred to the digital learning environment.

2.3. Methodology

2.3.1. Study Design

In this study, a mixed-methods approach was employed to evaluate the digital plant learning platform developed for the smart campus context. Mixed-methods research, defined as the process of presenting, analyzing, and integrating findings within a coherent framework using different methodological techniques, was employed to broaden the research data and provide more effective and comprehensive explanations of the examined phenomena [42,43,44]. In this context, the study was structured in two main stages. These stages include:
  • The development and implementation of the digital system
  • The functioning of the system and user evaluations
The first stage consisted of three components: field inventory, GIS integration of woody landscape plants, and the development of a web-based application and QR code system. Within the scope of the Field Inventory, the geographic coordinates of 6869 individual plants belonging to 172 taxa present in the landscape areas of Bursa Uludağ University’s Görükle Campus were recorded between 2023 and 2024. All data were then transferred into a digital environment, accompanied by visual documentation. Each taxon was integrated into the digital database with its scientific name, family, origin, morphological characteristics, phenological cycle, and ecological requirements. In addition, the recorded coordinates were imported into the GIS environment, and attribute tables were organized to create separate layers for each taxon, enabling the mapping of the spatial distribution of woody landscape plants (Figure 2).
Within the scope of developing the Web-Based Application and QR Code System, the website was built using Vue 3 and TypeScript, while the interface design was created with Vuetify 3 components. All data operations were managed through a Supabase (PostgreSQL)-based infrastructure. The software comprises two modules: the administrator panel and the user interface, which support core functionalities such as adding, updating, and deleting plant records, as well as managing QR codes. For each plant individual, a unique URL was generated; these URLs were created through dynamic parameters (species_id and tree_id) within Vue 3 and subsequently converted into machine-readable QR codes using a third-party API. The website and mobile application operate within the same interface. For mobile access, the same content was deployed as in the “Landscape Plants” mobile application.
In the second stage, the developed digital system was made accessible to users through two different formats: the web-based platform and the QR code-based interface. User access was provided directly via a referral link published on the Landscape Architecture Department website of our university, and the application was also made available through the App Store and Android-based mobile devices (Figure 3).
Within the scope of the QR code applications, taxon-specific QR codes were generated through the developed software. A total of 500 QR codes were attached to plant individuals representing 172 taxa. These QR codes were placed primarily on plants located around faculty buildings; however, due to the extensive size of the campus and high user density, 2–3 QR codes were assigned to certain taxa. The labels were produced as small, neutral-colored, and weather-resistant aluminum plates using an engraving method. They were attached to plant stems or branches in a manner that ensured visibility for users while maintaining the landscape’s esthetics and preventing damage to the plants (Figure 4). By scanning these QR codes, users are directed to the detailed digital plant information card provided on the web platform (Digital plant information card example, Supplementary Materials).
To obtain user feedback, a survey was administered to students in the Department of Landscape Architecture. Within the scope of document analysis—which enables the systematic examination and evaluation of printed and electronic materials [46,47], a total of 13 survey questions was developed by drawing on various sources [5,30,35,48]. The questions were structured according to a five-point Likert scale (e.g., 1 = Strongly disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly agree) and integrated into the digital system (Table 1).
In this study, the Likert-type scale was employed as a key psychometric tool, enabling the reliable and measurable assessment of attitudes and perceptions. The scale allows users to numerically express their thoughts and perceptions, facilitates the transformation of complex social phenomena into quantitative data, possesses a simple and comprehensible structure, increases response rates, and supports statistical analyses such as reliability, correlation, and regression based on total item scores [49].
Each question presented in Table 1 was directed separately for the web-based application and the QR code application, and the survey was conducted in relation to the courses included in the Landscape Architecture curriculum, such as “Landscape Plants,” “Planting Design,” “Landscape Design,” and “Landscape Planning.” Students were coordinated by the research team and guided to walk around different areas of the campus at various times to ensure active use of the system. They were instructed to access the system through their preferred method (web application or QR code application). Additionally, they were encouraged to use the system outside class hours at their own discretion.
Ethical approval for the survey was obtained from the Bursa Uludağ University Directive on Research and Publication Ethics Committees. In determining the required sample size, the number of registered students in the Department of Landscape Architecture was considered, and the following formula was used [50].
n = N · t2 · p · qd2 · (N − 1) + t2 · p · qn
where N = population size (number of landscape architecture students: 212), n = sample size, p = probability of occurrence (0.20), q = probability of non-occurrence (0.80), d = accepted margin of error (0.05), and t = table value for 95% confidence level (1.96).
Based on these parameters, the required sample size was calculated as n = 211. The survey was then administered to a total of 158 students who actively used the application. Although the number of responses remained below the calculated sample size, the participation rate—approximately 75%—indicates that the collected data still adequately represent the population. Non-participation was observed primarily during out-of-class activities and was likely due to factors such as lack of interest, access limitations, and concerns about privacy.

2.3.2. Data Analysis

The data obtained from the study were analyzed using the SPSS 28 statistical software. A reliability analysis was conducted to determine whether the scale formed a coherent structure capable of explaining a homogeneous construct, and the reliability of the items was assessed through the calculation of Cronbach’s alpha coefficient. Cronbach’s alpha, which indicates the reliability of instruments based on total scores—such as Likert-type scales, was interpreted according to the commonly accepted thresholds below [51,52].
0.00 ≤ α < 0.40: the scale is not reliable
0.40 ≤ α < 0.60: the scale has low reliability
0.60 ≤ α < 0.80: the scale is considerably reliable
0.80 ≤ α < 1.00: the scale is highly reliable
Additionally, the distribution of users’ preferences for the web-based and QR code applications, as well as the scores assigned to each application individually, was analyzed through frequency analysis. The evaluations for both applications were conducted based on question groups categorized under different parameters (Table 2).
A correlation analysis was conducted to examine the relationship between the applications. This analysis is a statistical method used to determine the direction and strength of the association between two variables, with correlation coefficients ranging from −1 to +1. A positive correlation (+) indicates that both variables tend to increase or decrease together, whereas a negative correlation (−) signifies that one variable increase while the other decreases. The significance level for the analysis was set at p < 0.001 [53].

3. Results

3.1. Analysis and Evaluation of Questions Related to the Web-Based and QR Code Applications

When the reliability of the survey questions was analyzed, the Cronbach’s Alpha coefficient was calculated to be 0.969, indicating a high level of reliability, as it falls within the recommended range of 0.80 ≤ α < 1.00.
This result indicates strong internal consistency among the survey items, demonstrating that the obtained data can be used with confidence. Accordingly, when the use of the Web and QR code applications by the participating students was evaluated, it was observed that the students’ levels of use of the QR code and web-based systems were nearly equal.
It was observed that 57.3% of the participants preferred the QR code application, while 54.7% preferred the web-based application. On the other hand, there were no participants who chose not to use either application; that is, no participant selected the “Strongly Disagree” option (0%) (Figure 5).
Analysis of the student responses concerning the web-based application revealed that 76.7% of the participants strongly agreed with Q13 (Mobile applications contribute to my professional development), 71.7% with Q2 (I find mobile applications that provide access to information on woody landscape plants valuable), and 70.4% with Q3 (Mobile applications have been useful in accessing practical information about woody landscape plants). In contrast, 8.2% of the participants disagreed with Q11 (Mobile applications were effectively used in my planning courses), while 7.5% disagreed with Q12 (Mobile applications supported the development of original compositions in my project-based courses). Additionally, 2.5% of the participants strongly disagreed with both Q1 (I frequently use the mobile application to access information on woody landscape plants) and Q10 (Mobile applications were effectively used in my project-based courses) (Figure 6).
Analysis of the student responses concerning the QR code application revealed that 76.7% of the participants strongly agreed with Q13 (Mobile applications contribute to my professional development), 71.7% with Q3 (Mobile applications have been useful in accessing practical information about woody landscape plants), and 71.1% with Q2 (I find mobile applications that provide access to information on woody landscape plants valuable). In contrast, 8.8% of the participants disagreed and 4.4% strongly disagreed with Q11 (Mobile applications were effectively used in my planning courses). Furthermore, 18.9% of the respondents gave a neutral response to Q1 (I frequently use the mobile application to access information on woody landscape plants) (Figure 7).
In addition, a correlation analysis was conducted to determine whether there was a relationship between the web-based and QR code applications. The results revealed a strong positive correlation between the two applications at a significance level of p ≤ 0.001, with a correlation coefficient of r = 0.941 (Figure 8).

3.2. Analysis of Question Groups Related to Web-Based and QR Code Applications

3.2.1. Web-Based Applications

Analysis of the responses related to the web-based application revealed that participants predominantly selected the “Agree” option across all parameters defined in Table 2. Among these, the parameter “Knowledge Acquisition and Learning Process” showed the highest level of agreement (65.4%). In contrast, a very small proportion of participants (0.6%) selected the “Strongly Disagree” option for the parameter “Functionality in the Academic Process” (Figure 9).
Correlation analysis revealed that positive and statistically significant correlations (p ≤ 0.01 **) existed among all parameters related to the web-based application (Table 3). A strong positive correlation was observed between “Access to and Understanding of Information” and “Knowledge Acquisition and Learning Process” (r = 0.805). Similarly, high correlations were identified between “Functionality in the Academic Process” and “Knowledge Acquisition and Learning Process” (r = 0.822) and between “Functionality in the Academic Process” and “Access to and Understanding of Information” (r = 0.705) (Figure 10).

3.2.2. QR Code Application

Analysis of the responses related to the QR code application revealed that participants predominantly selected the “Strongly Agree” option across all parameters (Table 2). The parameter “Knowledge Acquisition and Learning Process” recorded the highest agreement rate at 68%. On the other hand, a small proportion of participants (0.6%) selected the “Strongly Disagree” option for the parameter “Functionality in the Academic Process” (Figure 11).
Correlation analysis revealed that positive and statistically significant correlations (p ≤ 0.01 **) existed among all parameters related to the QR code application (Table 4). A strong positive correlation was identified between “Access to and Understanding of Information” and “Knowledge Acquisition and Learning Process” (r = 0.814). Similarly, “Functionality in the Academic Process” was found to be highly correlated with “Knowledge Acquisition and Learning Process” (r = 0.812) and showed a strong relationship with “Access to and Understanding of Information” (r = 0.776) (Figure 12).

3.2.3. Integrated Evaluation of the Web-Based and QR Code Applications

The analysis of the responses related to both the web-based and QR code applications revealed that participants predominantly selected the “Strongly Agree” option across all parameters (Table 2). The “Knowledge Acquisition and Learning Process” parameter exhibited the highest level of agreement, with a rate of 65.5%. In contrast, 16.4% of the participants responded “Neutral,” while 3.8% responded “Disagree” to the “Functionality in the Academic Process” parameter (Figure 13).
The relationship among the question groups related to the web-based and QR code applications was found to be significant at the p ≤ 0.001 level (Table 5). A strong positive correlation was identified between the parameters “Access to and Understanding of Information” and “Knowledge Acquisition and Learning Process” (r = 0.839). In addition, the parameter “Functionality in the Academic Process” showed a high level of correlation with both “Knowledge Acquisition and Learning Process” (r = 0.829) and “Access to and Understanding of Information” (r = 0.766) (Figure 14).

4. Discussion

The findings of this study reveal that both the web-based and QR code applications made significant contributions to the students’ learning experience. In particular, the high levels of agreement observed in the “Knowledge Acquisition and Learning Process” parameter (65–68%) indicate that digital tools play an effective role in supporting students’ learning processes. Consistent with the literature, digital learning tools have been emphasized to enhance learners’ motivation and engagement by providing greater autonomy [6,49]. In alignment with these findings, the results of the present study confirm that students actively engage in learning using digital tools.
The strong correlations identified between the parameters of the web-based and QR code-based applications (r = 0.705–0.839) indicate that the two tools function in a complementary manner. While the web platform provides structured and comprehensive information, the QR code application reinforces the learning experience by offering instant and contextual information in the field. Similarly, the study conducted by Lai et al. (2013) [54] demonstrated that QR codes enable interactive learning across different environments, extending the learning process beyond the classroom. Therefore, the findings of this study suggest that the combined use of digital tools creates a more holistic and interactive learning experience.
Research has shown that QR code-based learning environments enhance students’ active participation by supporting both independent and collaborative learning [13,30]. In the present study, the low rate of negative feedback regarding the QR code applications indicates that this technology enhances interaction through its advantage of providing rapid access to information. This finding is consistent with the literature, which emphasizes that the flexibility and instant accessibility offered by QR codes increase students’ interest and motivation [26,55].
In the context of field-based learning, the integration of digital tools has also proven effective in enhancing students’ professional skills. The present study enabled students not only to identify plant species but also to evaluate them within a spatial context across the campus scale. In this respect, the study contributes a new dimension to the existing literature. Similarly, Tukiran (2021) [30] demonstrated that QR codes enhance the knowledge levels of landscape design students, while Jadhav et al. (2025) [56] reported that such applications strengthen ecological awareness.
Recent studies have shown that QR code systems enhance students’ interaction with nature, thereby improving their environmental awareness and knowledge of biodiversity [34,55]. The findings of the present study also support these results, making an original contribution to the literature by enabling students—particularly those using an interactive map—to not only identify plant species but also evaluate them within their spatial context.

5. Conclusions

This study focuses on the development and evaluation of a QR code-integrated, web-based learning system designed to support field-based learning in landscape architecture education. The findings indicate that both the web-based and QR code-based applications facilitated students’ access to information, effectively supported their learning processes, and achieved a high level of user satisfaction. In particular, the QR code applications enhanced students’ observation, analysis, and application skills by providing instant and contextual information in field settings, thereby making the learning experience more interactive and personalized. The system improves the effectiveness of digital learning practices and promotes plant literacy in nature-based disciplines, while also presenting a model for designing sustainable and interactive learning environments within smart campus frameworks.
For future research, it is recommended that the system be implemented across different university campuses and various disciplines to monitor its long-term learning impacts and conduct a more detailed analysis of user experience. Additionally, enriching mobile applications with augmented reality (AR) and artificial intelligence (AI)-supported content stands out as a promising direction to further enhance student engagement and deepen learning. In conclusion, QR code-integrated web-based learning systems can be regarded as pedagogically effective, user-centered, and sustainable digital education tools for nature-based disciplines. Therefore, this study not only contributes to landscape architecture education but also demonstrates how digital learning can play a transformative role across all nature-oriented fields, presenting a scalable and sustainable model for smart campuses.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su172411203/s1.

Author Contributions

Conceptualization, G.A. and M.Z.; methodology, G.A.; software, G.A.; validation, G.A. and M.Z.; formal analysis, G.A.; investigation, G.A.; resources, G.A.; data curation, G.A.; writing—original draft preparation, G.A.; writing—review and editing, G.A. and M.Z.; visualization, G.A.; supervision, M.Z.; project administration, G.A. and M.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by Bursa Uludag University Research Projects Coordination Office under the Grant Number FDK-2024-1796. The authors thank the BUU BAP Unit for their support.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Bursa Uludag University Research and Publication Ethics Committee (protocol code: 2023-08/3, approval date: 27 November 2023).

Informed Consent Statement

Informed consent for participation was obtained from all subjects involved in the study. Participation was voluntary and all survey data were collected anonymously.

Data Availability Statement

The web-based plant learning platform developed in this study is available at: https://peyzajbitkileri.uludag.edu.tr/ (accessed on 31 October 2025). The mobile application “Landscape Plants” is available for download at: Google Play: https://drive.usercontent.google.com/download?id=1PHbJJ-J8hv7F-7U7VphmVsdzFvvwNWSX&export=download&authuser=1./ (accessed on 31 October 2025). App Store: https://apps.apple.com/tr/app/landscape-plants/id6657990250?l=tr./ (accessed on 31 October 2025). Survey data can be obtained from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
BUUBursa Uludag University
GISGeographic Information Systems
GPSGlobal Positioning System
QRQuick Response
SPSSStatistical Package for the Social Sciences

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Figure 1. Location of the research area (Created by the authors).
Figure 1. Location of the research area (Created by the authors).
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Figure 2. Coordinate map of woody landscape plants on the Görükle Campus of Bursa Uludağ University (Created by the authors).
Figure 2. Coordinate map of woody landscape plants on the Görükle Campus of Bursa Uludağ University (Created by the authors).
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Figure 3. User interface of the QR code-integrated web-based plant learning system: (a) interactive campus map, (b) species search screen [45].
Figure 3. User interface of the QR code-integrated web-based plant learning system: (a) interactive campus map, (b) species search screen [45].
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Figure 4. Physical placement of QR codes (Created by the authors).
Figure 4. Physical placement of QR codes (Created by the authors).
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Figure 5. Student usage of the web-based and QR code learning applications.
Figure 5. Student usage of the web-based and QR code learning applications.
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Figure 6. Evaluation chart of Landscape Architecture students regarding the web-based application.
Figure 6. Evaluation chart of Landscape Architecture students regarding the web-based application.
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Figure 7. Evaluation chart of Landscape Architecture students regarding the QR code application.
Figure 7. Evaluation chart of Landscape Architecture students regarding the QR code application.
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Figure 8. Correlation analysis between the web-based and QR code applications (Correlation ** p ≤ 0.001).
Figure 8. Correlation analysis between the web-based and QR code applications (Correlation ** p ≤ 0.001).
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Figure 9. Distribution of participant responses across all parameters related to the web-based application.
Figure 9. Distribution of participant responses across all parameters related to the web-based application.
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Figure 10. Correlation analysis among the parameters within the website application (** p ≤ 0.001).
Figure 10. Correlation analysis among the parameters within the website application (** p ≤ 0.001).
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Figure 11. Distribution of participant responses across all parameters related to the QR code application.
Figure 11. Distribution of participant responses across all parameters related to the QR code application.
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Figure 12. Correlation analysis among the parameters within the QR code application (** p ≤ 0.001 level).
Figure 12. Correlation analysis among the parameters within the QR code application (** p ≤ 0.001 level).
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Figure 13. Distribution of participant responses across all parameters related to the web-based and QR code applications.
Figure 13. Distribution of participant responses across all parameters related to the web-based and QR code applications.
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Figure 14. Correlation analysis among the parameters within the web-based and QR code applications (Correlation is significant at the ** p ≤ 0.001 level).
Figure 14. Correlation analysis among the parameters within the web-based and QR code applications (Correlation is significant at the ** p ≤ 0.001 level).
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Table 1. Survey questions for Landscape Architecture students.
Table 1. Survey questions for Landscape Architecture students.
QuestionStudent Feedback Form on the Mobile Application and QR Code Experience in Woody Landscape Plant Learning
Q1I frequently use the mobile application to access information on woody landscape plants.
Q2I find mobile applications that provide access to information on woody landscape plants valuable.
Q3Mobile applications have been useful in accessing practical information about woody landscape plants.
Q4Mobile applications have increased my awareness and knowledge of woody landscape plant species.
Q5Mobile applications were effectively used in my courses related to plant materials.
Q6Mobile applications have helped me learn the ecological requirements (e.g., light, soil, moisture) of woody landscape plants.
Q7Mobile applications have helped me learn the general characteristics (e.g., origin, height, crown diameter) of woody landscape plants.
Q8Mobile applications were effectively used in my planting design courses.
Q9Mobile applications have been useful in understanding the design uses of woody landscape plants (e.g., in coastal areas, gardens, as solitary elements).
Q10Mobile applications were effectively used in my project-based courses.
Q11Mobile applications were effectively used in my planning courses.
Q12Mobile applications supported the development of original compositions in my project-based courses.
Q13Mobile applications have contributed significantly to my professional development.
Table 2. Categorization of survey parameters and their corresponding questions.
Table 2. Categorization of survey parameters and their corresponding questions.
ParametersRelated Questions
Access to and Understanding of InformationQ1, Q2, Q4, Q13
Knowledge Acquisition and Learning ProcessQ3, Q6, Q7, Q9
Functionality in the Academic ProcessQ5, Q8, Q10, Q11, Q12
Table 3. Correlation analysis of the parameters related to the web-based application.
Table 3. Correlation analysis of the parameters related to the web-based application.
ParametersAccess to and Understanding of InformationKnowledge Acquisition and Learning ProcessFunctionality in the Academic Process
Access to and Understanding of Information1
Knowledge Acquisition and Learning Process0.805 **1
Functionality in the Academic Process0.705 **0.822 **1
** Correlation is significant at the ** p ≤ 0.001 level.
Table 4. Correlation analysis of the parameters related to the QR code application.
Table 4. Correlation analysis of the parameters related to the QR code application.
ParametersAccess to and Understanding of InformationKnowledge Acquisition and Learning ProcessFunctionality in the Academic Process
Access to and Understanding of Information1
Knowledge Acquisition and Learning Process0.814 **1
Functionality in the Academic Process0.776 **0.812 **1
** Correlation is significant at the ** p ≤ 0.001 level.
Table 5. Correlation analysis of the parameters related to the web-based and QR code applications.
Table 5. Correlation analysis of the parameters related to the web-based and QR code applications.
ParametersAccess to and Understanding of InformationKnowledge Acquisition and Learning ProcessFunctionality in the Academic Process
Access to and Understanding of Information1
Knowledge Acquisition and Learning Process0.839 **1
Functionality in the Academic Process0.766 **0.829 **1
** Correlation is significant at the ** p ≤ 0.001 level.
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Altun, G.; Zencirkıran, M. A Web-Based Learning Model for Smart Campuses: A Case in Landscape Architecture Education. Sustainability 2025, 17, 11203. https://doi.org/10.3390/su172411203

AMA Style

Altun G, Zencirkıran M. A Web-Based Learning Model for Smart Campuses: A Case in Landscape Architecture Education. Sustainability. 2025; 17(24):11203. https://doi.org/10.3390/su172411203

Chicago/Turabian Style

Altun, Gamze, and Murat Zencirkıran. 2025. "A Web-Based Learning Model for Smart Campuses: A Case in Landscape Architecture Education" Sustainability 17, no. 24: 11203. https://doi.org/10.3390/su172411203

APA Style

Altun, G., & Zencirkıran, M. (2025). A Web-Based Learning Model for Smart Campuses: A Case in Landscape Architecture Education. Sustainability, 17(24), 11203. https://doi.org/10.3390/su172411203

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