Next Article in Journal
Implementation Effect, Long-Term Mechanisms, and Industrial Upgrading of the Low-Carbon City Pilot Policy: An Empirical Study Based on City-Level Panel Data from China
Previous Article in Journal
Impact of Big Data on Carbon Emissions: Empirical Evidence from China’s National Big Data Comprehensive Pilot Zone
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 16
Issue 19
10.3390/su16198315
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Innovating Built Environment Education to Achieve SDG 4: Key Drivers for Integrating Augmented Reality Technologies

by
Opeoluwa Akinradewo
1,2,*,
Mohammed Hafez
1,
Clinton Aigbavboa
3,
Andrew Ebekozien
3,
Peter Adekunle
3 and
Osamudiamen Otasowie
3
1
Civil Engineering Department, Faculty of Engineering and Quantity Surveying, INTI-International University, Nilai 71800, Malaysia
2
Department of Quantity Surveying and Construction Management, Faculty of Natural and Agricultural Sciences, University of the Free State, Bloemfontein 9301, South Africa
3
cidb Centre of Excellence, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg 2006, South Africa
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(19), 8315; https://doi.org/10.3390/su16198315
Submission received: 7 August 2024 / Revised: 15 September 2024 / Accepted: 19 September 2024 / Published: 24 September 2024
(This article belongs to the Topic Building a Sustainable Construction Workforce)
Versions Notes

Abstract

:
Augmented Reality Technologies (ARTs) are increasingly pivotal in transforming various industries, with notable implications for the built environment sector. This article delves into the drivers of ART adoption for education and training within the built environment, focusing on its role in enhancing educational delivery and operational efficiency. Utilising a structured survey distributed among professionals in South Africa’s built environment, this study employs descriptive and inferential statistics to analyse the data, identifying key trends and correlations. Our results demonstrated that ART significantly enhances task accuracy, fosters better collaboration and mitigates misinformation, thereby improving training and education outcomes. Professionals particularly highlight ART’s capacity to bridge the gap between theoretical knowledge and practical site experience, underscoring its value in preparatory education and on-site training. Furthermore, the analysis revealed that the integration of ART into educational curricula and professional practices not only augments learning experiences but also propels safety and quality in construction projects. Given these findings, this study strongly recommends that stakeholders in the construction and educational sectors in South Africa prioritise the adoption of ART to fully leverage its benefits for innovation and competitive advantage in the built environment.

1. Introduction

The adoption of augmented reality has been established across numerous industries, proving that it can be used for purposes other than entertainment and gaming [1]. As a result, a 77% compound annual growth rate (CAGR) is anticipated for the AR/VR industry from 2019 to 2023. Retailers use augmented reality technologies (ARTs) to enhance the online shopping experience by allowing customers to visualise products (e.g., furniture and appliances) in their own space [2]. ART is used in the medical field to simulate surgeries or visualise organs. To increase accuracy and results, surgeons can project 3D models of a patient’s anatomy using ART [3]. AR is also creating a stir in the building sector. When used effectively, ART can increase project wins, team collaboration, and even safety [4]. Understanding ART’s capabilities and use cases is essential for utilising it in the construction industry.
Technology and experiences known as “ART” place computer-generated items in the user’s actual world [5]. Mobile applications, headgear, and other smart devices that project digital things into the actual world often enable ART. ART can be used in construction for a variety of purposes, including communications and project planning [6]. It serves as a crucial project presentation tool. ART can add specific features and components to a building plan so that interested parties can better comprehend the project [7]. Additionally, 3D models and tours can be displayed via ART, providing clients with a clear concept of what a structure will look like before it is constructed. It gives the client a visual representation of how a new installation of an existing structure would appear on the property. After ART is used to display projects, another application is used to track project progress [8]. ART can be used to monitor and record the development of projects. The industry offers a variety of technologies that enable construction professionals to track projects’ progress. These apps recognise the project’s progress in the floor plan using the ART capabilities of the user’s mobile smartphone, and they also automatically take images at each capture point. By ensuring that team members consistently capture at the same location throughout time, progress capture accuracy and efficiency are improved.
The construction industry needs ART because it improves collaboration, safety, and, ultimately, construction training [9]. By enabling teams to communicate 3D images and videos with team members who aren’t nearby, ART can improve cooperation in distant work settings. With the use of ART, stakeholders may see photographs or videos in greater detail, allowing them to see mistakes or problems without having to physically be in the building. The use of ART can increase workplace safety [10]. Some ART tools (such as eyewear or mobile devices) may read tags or labels that have been attached to particular items or regions. The information about safety or hazards can then be communicated via these labels by bringing up text or even 3D models. Amazingly, ART can help instructors by providing lifelike demonstrations so students may view equipment in operation before visiting the site while teaching individuals how to utilise sophisticated equipment or large machinery [11]. Businesses can also employ ART to show how dangerous business operations or materials are without putting team members in danger.
In order to properly engage the user’s senses, Radu [12] claimed that education is seen through a range of different mediums, such as books, media, and interactive technologies. The author further reiterates that the use of ART is giving students the opportunity to engage in interactive simulations and educational games to experience learning. Through virtual resources in an educational setting, ART enables students to get practical experience in a safe setting [13]. Due to the high cost of both equipment and labour in some professions, like construction-related fields, it can be challenging to gain practical experience. By incorporating ART into the curriculum, this learning gap might be closed. Additionally, according to Jantjies et al. [13], ART in education supports exploratory learning. As a result, the educational paradigm could be modified technologically to better support the modernisation and integration of ICTs (Information and Communication Technologies). According to Lee [14], the usage of ART has the potential to encourage students to be more motivated to learn about various viewpoints and put their efforts into implementing viewpoints that have not yet been achieved. Lee [14] adds that ART is a collaborative, skill-based learning tool that can be applied in a variety of contexts to support the development of complex knowledge and abilities. Rodriguez-Pardo [15] claimed that ART is a fantastic tool for learning during the educational process since it enables seamless interaction between the actual and virtual worlds. Therefore, studying the factors driving the use of ART in education and training for the built environment is essential. As a result, the goal of this study is to identify the factors that drive the use of ART in the built environment. This intends to sensitise the stakeholders involved in the built environment on the different factors to focus on in order to promote the adoption of ART. The outcome of this study will assist built environment students in becoming ready for the industry as they will have a tangible experience of how the industry operates practically through virtual means. The subsequent sections of this manuscript will discuss an overview of the drivers of ART adoption in various fields.

2. Drivers for Adopting Augmented Reality Technologies

The usage of ART in educational settings is one of the ten most significant developing technologies for humanity, according to Martínez et al. [16]. The justifications for this assertion typically centre on the technology’s intrinsic characteristics, including immersive settings and interactivity. The user experiences an immersive environment when actual environments and virtual items are combined [17]. Learning in a digital environment enhances education by providing various perspectives, contextual learning, and the application of acquired knowledge. Additionally, ART is a highly participatory technology, which fits the idea of ‘learning by doing’ and considerably enhances the organisational mode of operation [18]. The possibilities range from simple interactive elements like an embedded intelligent virtual teacher or the interaction between the real-world and virtual things (i.e., tangible interfaces) to complicated interactive features like moving 3D models, playing films, resizing objects, etc. Further benefits of ART include “satisfied project delivery’”, “increased accuracy and precision”, and “assurance of quality” in education and training [19].
Construction has shown a strong interest in collaborative applications, and various studies have already demonstrated the advantages of developing such settings [20]. ART allows users to be entertained while learning new information in addition to educating and training them. Using these kinds of methods typically makes learning new concepts more engaging for students. It is important to keep in mind that ART has a quick learning curve, allowing users to start using the applications with very little background knowledge and “available resources” [21]. As evidenced by the wide range of examples that can be discovered in the literature, such as mathematics, physics, chemistry, languages, medicine, natural sciences, or music, ART can be applied in practically any construction educational discipline. The applications can be geared toward people of all ages, from young children to college students.
Assembling and disassembling scaffolds, cleaning up trash or debris from job sites, loading and unloading goods, and other related tasks may all be trained by professional personnel using ART [22]. The deployment of ART applications for training and education does not have a set standard. There are, however, a few standard procedures that many studies adhere to. When traditional educational or training material is provided in the form of text and images, one of the most popular strategies is to employ ART to enhance the content of books [23]. The ART content may include a variety of multimedia elements such as 3D models, animations, videos, webpages, etc., along with interactive features that enhance the value of the books. The value encourages building a strong organisational culture that births organisational “leadership”, “structure”, and “mission and vision” [24]. Researchers have been adding instructional content to books for many subjects to improve learning since one of the earliest ART books, the well-known Magic Book, allows users to immerse themselves in a virtual environment. Because of the intrinsic interactivity of ART, it is popular and frequently utilised to have students use hand-sized markers to analyse complicated 3D representations that are difficult to comprehend when viewed as printed images or to engage with the lesson material in educational games [25]. Another popular strategy is to include virtual characters in the ART scenario who serve as trainers, instructors, or guiding figures throughout training. Given the wide range of needs in the sectors of education and training, it is clear from the previous considerations that there is no standardised process for the development of ART applications [16].
Additionally, ART aims to improve users’ sensory perception of their physical surroundings by supplementing it with virtual items and data. In particular, ART creates a mixed environment in which actual and virtual items coexist in real-time by using technical applications of computer units [26]. When integrated with other cutting-edge technologies, ART can be made even more adaptable and engaging [27]. Additionally, ART has had a significant impact on several fields, including education. This is because of its capacity to offer interactive content to users and alter their perceptions [28]. ART can provide new learning environments and experiences and encourage an active and connected learning process since it mixes the actual world with digital information. The use of 3D model representations and animations in ART can increase motivation and memory retention. It is closely related to education, e-learning, and gamification [29]. ART enhances and supports high-quality education wherever and at any time, breaking down barriers to formal education [30]. These characteristics, along with the expanding acceptance and usefulness of ART in educational settings, have resulted in an annual rise in the calibre and amount of research on the topic [31,32].
Recent systematic reviews, scientific mapping, and bibliometric studies [33,34,35] have discussed the advantages that can be obtained when integrating ART into educational settings in a student-centred manner and some of its drawbacks and limitations. Thus, learning environments that enable and encourage inclusive, collaborative, located, autonomous, problem-based, and ubiquitous learning can be developed through the immersive, pleasant, and realistic learning experiences that ART offers [36,37]. Immersive ART environments can provide more interactive experiences than conventional learning settings while using fewer resources, less money, and less time [38,39]. Students also find the overall experience more engaging and enjoyable, and as a result, they participate more willingly and actively in the learning activities, which improves their learning success, academic performance, knowledge acquisition, long-term retention, and cognitive development [40,41,42,43]. Students’ attitudes toward technology-enhanced learning and digital inclusion improve through experiencing the benefits of participating in ART learning environments. The reasons for adopting ART outweigh its current limitations. ART helps remove barriers to formal education and promotes high-quality education, making it suitable for incorporation into all educational stages. Additionally, it supports both educators and students.

3. Methodology

The drivers for the adoption of ART for education and training in the built environment were investigated using a quantitative method. Descriptive analysis and exploratory factor analysis (EFA) were used for this investigation. South Africa’s Gauteng province served as the target location of this investigation. Our study’s target audience is the built environment professionals; hence, a probability sampling technique was adopted. Purposive random sampling was used for this study because it gives every member of the population an equal chance of being chosen to be included in the sample, provided they meet the given criteria. Also, it is simple, inexpensive, and eliminates the possibility of bias during the sampling process [44,45]. This approach was adopted to ensure that participants chosen for the study have a knowledge of what ARTs are but do not necessarily have hands-on experience with their usage. This is because the South African construction industry is still developing, and these technologies are yet to be fully adopted. The questionnaire was designed to have two sections: section one dealt with the respondents’ background information, and section two adopted a 5-point Likert scale to measure the respondents’ opinions on the drivers of ART adoption (see Supplementary Materials). A total of 250 questionnaires were issued to the participants for this study, and 235 of them were returned, representing a 90% response rate. The results of the 5-point Likert scale questions used in this research were revealed using the mean item score (MIS). The mean is the most often used measure of central tendency [46]. The MIS index represents the overall participants’ actual scores (as determined by each respondent using a 5-point agreement Likert scale) expressed as a percentage of all the greater probable results on the Likert scale that each respondent may add to the criterion. Additionally, the retrieved data were also subjected to exploratory factor analysis (EFA). Using the EFA, one can examine the theoretical underpinnings of the phenomena and condense data into a smaller set of summary variables [47]. Along with the EFA used, it was important to verify the accuracy of the data acquired. After establishing the content validity and preliminary data analysis, tests for empirical and theoretical reliability were conducted for this study. The reliability test conducted using Cronbach’s alpha revealed that the data retrieved meets the required threshold with an alpha value of 0.814 and can be relied upon to make reasonable conclusions from the findings of this study.

4. Research Findings

According to our findings from the descriptive analysis, 41.9% of the respondents had the highest qualification, an honours degree. Other qualifications included matriculation, a post-matriculation certificate or diploma, a bachelor’s degree, a master’s degree, and a doctorate degree. Architects make up 9% of the professions, civil engineers 9%, construction managers 12%, mechanical and electrical engineers 2%, project managers 14%, and quantity surveyors 54%, which are the majority. In addition, the professionals who had completed the survey had years of professional experience; our findings also reveal that 7% of the respondents have less than 12 months of working experience, 21% have between one and five years of experience, 18% of the respondents have between six and ten years of experience, 19% have between 11 and 15 years of experience, whilst the mainstream at 35% have above 15 years of experience in the industry. Pertaining the number of projects that respondents have been involved in, below 5% of the respondents have been involved in three to four projects, 28% of the respondents have been involved in five to six projects, while only 7% have been involved in seven to eight projects, which leaves the other 60% of the respondents involved in more than eight different projects. Regarding employment, 2% of the respondents work for developers, 16% work for the government, 28% work for consultants, 5% work for specialist contractors, 7% of the respondents work for sub-contractors, while the majority, 42%, work for the main contractor. Finally, the demographic result shows that 56% of the respondents work for large organisations with more than 250 employees, 16% for medium-sized organisations with 51 to 250 employees, 14% for small organisations with five to 50 employees, while 14% work for micro-organisations with less than five employees.
Table 1 shows participants’ responses to the drivers for adopting ART for education and training. These drivers are ranked using their Mean Score values from the highest mean to the lowest mean. The most predominant driver is the “Need for improvement in teaching and learning”, with a mean score (MS) of 3.07 and a standard deviation (SD) of 0.799. The other predominant drivers include the “Readiness to digitise teaching and learning” (MS = 2.95; SD = 0.785), “Satisfactory teaching and learning project delivery” (MS = 2.95; SD = 0.815), “Availability of resources for ART adoption” (MS = 2.95; SD = 0.844), and “Improved accuracy and precision in teaching” (MS = 2.91; SD = 0.811). The least ranked driver perceived by the respondents is the “Organisational leadership support for ART adoption” (MS = 2.65; SD = 0.870). Other lowly ranked drivers include “Improved health and safety” (MS = 2.79; SD = 0.965), “Quality assurance in teaching and learning” (MS = 2.40; SD = 1.137), “Organisational mission and vision of the institutions of learning” (MS = 2.84; SD = 0.843) and “Organisational structure of institutions of learning” (MS = 2.84; SD = 0.785).
The ranking results highlight the “Need for improvement in teaching and learning” as the most important driver for adopting augmented reality technologies (ART) in education, reflecting the growing demand for innovative methods to enhance educational outcomes. This is consistent with research that emphasises the role of ART in addressing gaps in traditional teaching methods, providing immersive and interactive learning experiences that foster deeper engagement and knowledge retention [13,14]. The need for improvement is closely linked to the “Readiness to digitise teaching and learning”, ranked second, as institutions increasingly recognise the importance of integrating digital tools like ART to modernise their educational approaches [5]. On the other hand, the “Organisational leadership support for ART adoption” was ranked as the least important factor. This may be attributed to the fact that while leadership endorsement is crucial, the success of ART implementation is more directly influenced by operational factors such as resource availability and technological readiness, as highlighted by other studies [15]. Furthermore, the perceived lower importance of leadership support may stem from decision-making in educational technology adoption, often involving stakeholders beyond organisational leadership, such as educators and IT specialists, whose active participation is critical for successful integration [9]. Thus, while leadership support remains relevant, it is not considered the primary driver for ART adoption in this context.
Subjecting the retrieved data to EFA, the Kaiser-Meyer-Olkin (KMO) measure of sample appropriateness, as shown in Table 2, attained a score of 0.653, surpassing the threshold value of 0.6. The KMO test measures the adequacy of sampling by comparing the magnitude of the observed correlation coefficients to the magnitude of the partial correlation coefficients. A KMO value close to 1 suggests that a large portion of the variance in the variables can be explained by underlying factors, which is ideal for factor analysis. Conversely, a value less than 0.5 indicates inadequate sampling, suggesting that factor analysis may not be suitable [48]. Additionally, the Bartlett’s test of sphericity was statistically significant (<0.05). The Bartlett’s test of sphericity evaluates whether the correlation matrix is an identity matrix, which would indicate that variables are unrelated and, therefore, unsuitable for structure detection. A significant result (usually at the p < 0.05 level) indicates that the correlation matrix is not an identity matrix and that the data will likely have underlying structures that factor analysis could uncover [48]. Twelve variables identified from the literature were factored into three clusters, which are then interpreted based on the observed intrinsic relationship among the variables in the cluster, as shown in Table 2, which shows the pattern matrix.
  • A total of four variables were loaded onto cluster 1, as shown in Table 2. These variables include “Readiness to digitise teaching and learning” (87.7%), “Availability of resources for ART adoption” (81.8%), “Need for improvement in teaching and learning” (63.6%) and “Gamification” (63.4%). All these can be observed to relate to the digitisation of the establishment’s activities. Therefore, this factor cluster was termed “Individual and infrastructure-related drivers” with a variance of 46.683%, making it a major driver of the adoption of ART for education and training in the built environment.
  • A total of four variables were loaded onto cluster 2In cluster 2, there are four (4) variables loaded onto it. These variables include “Organisational mission and vision of the institutions of learning” (85.8%), “Organisational structure of institutions of learning” (80.0%), “Organisational leadership support for ART adoption” (68.0%) and “Satisfactory teaching and learning project delivery” (46.4%). The common factor to the variables in this cluster is related to the organisational culture of the establishment. The cluster was therefore labelled “Organisation-related drivers”, with a total variance of 12.014%. This cluster is ranked as a driver of the adoption of ART for education and training in the built environment behind the variables in cluster 1.
  • Cluster 3 also had four variables loaded onto it, and these variables include “Improved accuracy and precision in teaching” (81.4%), “Improved health and safety’ (76.2%), ‘Quality assurance in teaching and learning” (75.6%) and “Reduction of misinformation among teachers and learners” (73.9%). These variables relate largely to factors that improve the mode of operation of the establishments and were therefore labelled “Operational Performance drivers”. This cluster gathered 10.070% of the total variance to be ranked the third classification of the drivers for adopting ART for education and training in the built environment.

5. Discussion of Findings

“Regarding the advancement in ART, digital technologies have replaced the functions of older technologies, allowing for the creation of web-based virtual classrooms instead of traditional in-person classrooms [21]. Rather than handing out printed lecture scripts, teachers can now upload their scripts and lecture slides to the virtual classroom.” A learning management system, for instance, can be used to create a virtual classroom. Traditional teaching methods, such as in-person lectures, are diminished with distributed learning and substituted with digital tools [22]. By combining traditional face-to-face teaching with the use of digital tools, both educational approaches operate as a hybrid form of instruction. You could compare the idea of blended learning to that of distributed learning. According to research, blended learning is the attempt to combine the conventional method of face-to-face instruction with the use of online teaching resources. It is said that while virtual teaching is equally beneficial, it disconnects the learning experience from its ties to time and location. Face-to-face education combines the positive attributes of life and the immediate connection between teacher and student. It will take a total organisational rethinking, including cultural, strategic, technological, and operational adjustments, to take advantage of the immense prospects for innovation and competitive advantage presented by the ARTs.
Additionally, there is a disconnect between educators and trainers who give educational value and application developers who primarily come from the programming and IT industries. Several strategies have attempted to close this gap by giving educators and trainees access to software development tools. Another strategy is the implementation of a shared platform which gives users access to educational ART applications that programmers have created from the specifications created by educators [49]. Despite the abundance of research studies in this area, there is still only a modest level of acceptability. There are several potential causes, including the fact that as a new technology, people are not accustomed to using it or even know what ART is, but notably, there is a need for development in teaching resources, and stakeholders have “Readiness to digitise teaching and learning” [50]. This is true not only for teachers but also for students who, according to them, could feel overburdened by the volume of knowledge and the difficulty of the assignments. In their opinion, allowing kids to experiment independently with ART applications will “eliminate disinformation” and “Improve the health and safety of teachers and learners’ procedures” [51]. Another reason is that “gamification” might encourage ART patronage by increasing the levels of engagement and retention while also enticing construction students to learn about cutting-edge instruments used in the industry [52]. The most popular video games place a big emphasis on giving players ongoing feedback and rewarding their accomplishments. Major businesses are using gamification in various ways to improve engagement, productivity, and employee reviews. Gamification is another tool stakeholders in the construction sector can use to promote ART in education and training.
The exploration of Augmented Reality Technology (ART) adoption in education has yielded distinct clusters of drivers, each indicative of broader implications within the educational sector. The first cluster, “Individual and infrastructure-related drivers”, highlights factors primarily linked with educational establishments’ technical and logistical readiness to integrate ART into their systems. Moro [21] argued that digital readiness not only facilitates a smoother transition to digital learning environments but also enhances the receptiveness of both students and staff towards adopting new technologies. Also, Davis et al. [22] underscored the importance of technological infrastructure to support ART. This cluster indicates a fundamental need to address infrastructural and individual capabilities to leverage ART effectively in education.
The second cluster, “Organisation-related drivers”, consists of variables that reflect the organisational ethos and structure. As reported by Martinez and Laukkanen [49], an organisation’s vision for technology plays a critical role in shaping its integration strategy and subsequent acceptance within the institution. Similarly, organisational leadership is pivotal as leaders act as champions for change, facilitating the adoption and implementation of ART by setting priorities and allocating resources [51]. This cluster highlights the need for strategic alignment and supportive leadership within educational institutions to foster a conducive environment for ART.
Finally, the third cluster, “Operational Performance drivers”, showcases variables that focus on the operational impacts of ART in education. Improved accuracy and precision are critical for enhancing the quality of educational delivery, reflecting the potential of ART to refine and enrich the learning process [50]. According to Wang et al. [52], ART can improve the consistency and reliability of educational content, which in turn enhances learner outcomes.

6. Conclusions and Recommendations

The current study focused on the drivers for applying ART to education and training in the built environment. The study reviewed extant literature, and it was observed that while digital transformation is high on the agenda for learning, it is crucial to comprehend the “factors driving” this demand before implementing digital technologies. Understanding the reasons for change paves the way for more targeted project goals that, in turn, result in a more successful digital transformation. It is also critical to realise that digital transformation is driven by the desire to boost productivity using cutting-edge technologies like ARTs. Our study employed a quantitative research approach. A questionnaire survey was adopted to retrieve information from respondents on the drivers for the adoption of ARTs in education and learning in the built environment. Data retrieved from respondents within South Africa were analysed using descriptive and inferential statistics. The findings from our study indicated that the identified drivers can be categorised into three clusters: “Individual And Infrastructure-related drivers”, “Organisation-related drivers”, and “Operational Performance drivers”. Based on the findings of the study, it can be concluded that ARTs are digital tools that have the potential to improve learning and the built environment. For example, students who need first-hand experience of what happens on construction sites will find them very useful. The need for improvement in the acquired knowledge of built environment graduates can, therefore, be achieved when ART is adopted.
This study highlights a substantial readiness to digitise education, further supported by the success of gamification techniques in engaging students. Our findings suggest a pivotal opportunity for educational innovation through ART. From a policy perspective, enhancing digital literacy across educational institutions is crucial to ensure educators and students are well-prepared to adopt and effectively utilise ART. Governments and educational authorities need to invest in the necessary infrastructures, such as high-speed internet, modern hardware and software, which are essential for effective ART implementation. Furthermore, integrating ART into curriculum planning can align technological tools with educational goals, enhancing overall learning outcomes. Policies should also support the development of ART-specific modules and teacher training programs to facilitate this integration. On the industry front, there is a clear call to action for tech companies to collaborate with educational institutions in developing ART solutions tailored to the educational sector’s specific needs. The potential market for developing and distributing ART applications in education is substantial. Companies should focus on creating scalable and adaptable ART solutions that can be customised for various disciplines and learning environments. Moreover, providing ongoing support and training for educators will ensure they can integrate and maximise the benefits of ART in their teaching practices.
From the findings of this study, it is recommended that institutions of learning focus on the operational performance drivers of ART to promote the usage of ARTs. Also, the infrastructure needed must be put in place to ensure the effective adoption of ARTs. Our study was conducted in South Africa using built environment professionals; however, only professionals within Gauteng province were reached due to time constraints. Future research can be conducted to evaluate the barriers to the implementation of ARTs. Also, further studies should consider a broader demographic to include insights from emerging professionals and students in the built environment, which will help in understanding the broader implications of ART for early education and ongoing professional development. This would provide a more comprehensive understanding of ART’s role in fostering not only immediate operational improvements but also long-term educational advancements.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su16198315/s1; Questionnaire.

Author Contributions

Conceptualization, O.A.; Methodology, C.A.; Formal analysis, O.A.; Investigation, A.E. and O.O.; Data curation, A.E. and P.A.; Writing—original draft, O.A.; Writing—review & editing, C.A.; Visualization, O.O.; Project administration, M.H.; Funding acquisition, M.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics and Plagiarism Committee (FEPC) of the Faculty of Engineering and the Built Environment at the University of Johannesburg (protocol code UJ_FEBE_FEPC_00397 and 20 September 2021).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Materials, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Batat, W. How augmented reality (AR) is transforming the restaurant sector: Investigating the impact of “Le Petit Chef” on customers’ dining experiences. Technol. Forecast. Soc. Change 2021, 172, 121013. [Google Scholar] [CrossRef]
  2. Fan, X.; Chai, Z.; Deng, N.; Dong, X. Adoption of augmented reality in online retailing and consumers’ product attitude: A cognitive perspective. J. Retail. Consum. Serv. 2020, 53, 101986. [Google Scholar] [CrossRef]
  3. Moreta-Martinez, R.; Pose-Díez-De-La-Lastra, A.; Calvo-Haro, J.A.; Mediavilla-Santos, L.; Pérez-Mañanes, R.; Pascau, J. Combining Augmented Reality and 3D Printing to Improve Surgical Workflows in Orthopedic Oncology: Smartphone Application and Clinical Evaluation. Sensors 2021, 21, 1370. [Google Scholar] [CrossRef] [PubMed]
  4. Martín-Gutiérrez, J.; Fabiani, P.; Benesova, W.; Meneses, M.D.; Mora, C.E. Augmented reality to promote collaborative and autonomous learning in higher education. Comput. Hum. Behav. 2015, 51, 752–761. [Google Scholar] [CrossRef]
  5. Farshid, M.; Paschen, J.; Eriksson, T.; Kietzmann, J.; Farshid, M.; Paschen, J.; Eriksson, T.; Kietzmann, J.; Farshid, M.; Paschen, J.; et al. Go boldly! Explore augmented reality (AR), virtual reality (VR), and mixed reality (MR) for business. Bus. Horiz. 2018, 61, 657–663. [Google Scholar] [CrossRef]
  6. Wang, X.; Love, P.E.D.; Kim, M.J.; Park, C.-S.; Sing, C.P.; Hou, L. A conceptual framework for integrating building information modeling with augmented reality. Autom. Constr. 2013, 34, 37–44. [Google Scholar] [CrossRef]
  7. Enyedy, N.; Danish, J.A.; DeLiema, D. Constructing liminal blends in a collaborative augmented-reality learning environment. Int. J. Comput. Collab. Learn. 2015, 10, 7–34. [Google Scholar] [CrossRef]
  8. Kopsida, M.; Brilakis, I.; Vela, P.A. A Review of Automated Construction Progress Monitoring and Inspection Methods. In Proceedings of the 32nd CIB W78 Conference, Eindhoven, The Netherlands, 27–29 October 2015; pp. 421–431. [Google Scholar]
  9. Rankohi, S.; Waugh, L. Review and analysis of augmented reality literature for construction industry. Vis. Eng. 2013, 1, 9. [Google Scholar] [CrossRef]
  10. Afzal, M.; Shafiq, M.T.; Al Jassmi, H. Improving construction safety with virtual-design construction technologies—A review. J. Inf. Technol. Constr. 2021, 26, 319–340. [Google Scholar] [CrossRef]
  11. Borasi, R.; Finnigan, K. Entrepreneurial Attitudes and Behaviors that Can Help Prepare Successful Change-Agents in Education. New Educ. 2010, 6, 1–29. [Google Scholar] [CrossRef]
  12. Radu, I. Augmented reality in education: A meta-review and cross-media analysis. Pers. Ubiquitous Comput. 2014, 18, 1533–1543. [Google Scholar] [CrossRef]
  13. Jantjies, M.; Moodley, T.; Maart, R. Experiential learning through Virtual and Augmented Reality in Higher Education. In Proceedings of the 2018 International Conference on Education Technology Management (ICETM ‘18), Barcelona, Spain, 19–21 December 2018; Frutos, M.B., Sarsa, J., Eds.; Association for Computing Machinery: New York, NY, USA, 2018; pp. 42–45. [Google Scholar] [CrossRef]
  14. Lee, K. Augmented Reality in Education and Training. TechTrends 2012, 56, 13–21. [Google Scholar] [CrossRef]
  15. Rodrriguez-Pardo, C.; Patricio, M.A.; Berlanga, A.; Molina, J.M. An Augmented Reality Application for Learning Anatomy. In Bioinspired Computation in Artificial Systems, International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2015, Elche, Spain, 1–5 June 2015, Proceedings, Part II; de Vicente, J.M.F., Sánchez, J.R., López, F.P., Toledo-Moreo, F.J., Adeli, H., Eds.; Springer International Publishing: Cham, Switzerland, 2015; pp. 359–368. [Google Scholar] [CrossRef]
  16. Martínez, H.; Skournetou, D.; Hyppölä, J.; Laukkanen, S.; Heikkilä, A. Drivers and Bottlenecks in the Adoption of Augmented Reality Applications. J. Multimedia Theory Appl. 2014, 2, 27–44. [Google Scholar] [CrossRef]
  17. Kim, M.J. A framework for context immersion in mobile augmented reality. Autom. Constr. 2013, 33, 79–85. [Google Scholar] [CrossRef]
  18. Donnelly, R. Interaction analysis in a ‘Learning by Doing’ problem-based professional development context. Comput. Educ. 2010, 55, 1357–1366. [Google Scholar] [CrossRef]
  19. Shalimov, A. Augmented Reality In Education: How To Apply It To Your Edtech Business. Eastern Peak. 2022. Available online: https://easternpeak.com/blog/augmented-reality-in-education/ (accessed on 28 March 2024).
  20. Marques, B.; Silva, S.; Alves, J.; Araujo, T.; Dias, P.; Santos, B.S. A Conceptual Model and Taxonomy for Collaborative Augmented Reality. IEEE Trans. Vis. Comput. Graph. 2021, 28, 5113–5133. [Google Scholar] [CrossRef]
  21. Moro, C.; Štromberga, Z.; Raikos, A.; Stirling, A. The effectiveness of virtual and augmented reality in health sciences and medical anatomy. Anat. Sci. Educ. 2017, 10, 549–559. [Google Scholar] [CrossRef]
  22. Davis, P.; Aziz, F.; Newaz, M.T.; Sher, W.; Simon, L. The classification of construction waste material using a deep convolutional neural network. Autom. Constr. 2021, 122, 103481. [Google Scholar] [CrossRef]
  23. Sungkur, R.K.; Panchoo, A.; Bhoyroo, N.K. Augmented reality, the future of contextual mobile learning. Interact. Technol. Smart Educ. 2016, 13, 123–146. [Google Scholar] [CrossRef]
  24. Muhith, A. KIAI’s Transformational Leadership in Establishing Organizational Culture at Gender Pesantren. Glob. J. Arts Humanit. Soc. Sci. 2017, 6, 20–35. [Google Scholar]
  25. Price, M.A.; Sup, F.C., IV. A Handheld Haptic Robot for Large-Format Touchscreens. Trans. Mechatron. 2018, 23, 2347–2357. [Google Scholar] [CrossRef]
  26. Lampropoulos, G.; Keramopoulos, E.; Diamantaras, K.; Evangelidis, G. Augmented Reality and Gamification in Education: A Systematic Literature Review of Research, Applications, and Empirical Studies. Appl. Sci. 2022, 12, 6809. [Google Scholar] [CrossRef]
  27. Lampropoulos, G.; Keramopoulos, E.; Diamantaras, K. Enhancing the functionality of augmented reality using deep learning, semantic web and knowledge graphs: A review. Vis. Inform. 2020, 4, 32–42. [Google Scholar] [CrossRef]
  28. Kesim, M.; Ozarslan, Y. Augmented Reality in Education: Current Technologies and the Potential for Education. Procedia Soc. Behav. Sci. 2012, 47, 297–302. [Google Scholar] [CrossRef]
  29. Hincapie, M.; Diaz, C.; Valencia, A.; Contero, M.; Güemes-Castorena, D. Educational applications of augmented reality: A bibliometric study. Comput. Electr. Eng. 2021, 93, 107289. [Google Scholar] [CrossRef]
  30. Goff, E.E.; Mulvey, K.L.; Irvin, M.J.; Hartstone-Rose, A. Applications of Augmented Reality in Informal Science Learning Sites: A Review. J. Sci. Educ. Technol. 2018, 27, 433–447. [Google Scholar] [CrossRef]
  31. Bacca, J.; Baldiris, S.; Fabregat, R.; Graf, S.; Kinshuk; International Forum of Educational Technology & Society. Augmented Reality Trends in Education: A Systematic Review of Research and Applications. Educ. Technol. 2014, 17, 133–149. [Google Scholar]
  32. Ersozlu, A.; Karakus, M.; Clark, A.C. Augmented Reality Research in Education: A Bibliometric Study. Eurasia J. Math. Sci. Technol. Educ. 2019, 15, em1755. [Google Scholar] [CrossRef]
  33. Sirakaya, M.; Sirakaya, D.A. Trends in Educational Augmented Reality Studies: A Systematic Review. Malays. Online J. Educ. Technol. 2018, 6, 60–74. [Google Scholar] [CrossRef]
  34. López-Belmonte, J.; Moreno-Guerrero, A.-J.; López-Núñez, J.-A.; Hinojo-Lucena, F.-J. Augmented reality in education. A scientific mapping in Web of Science. Interact. Learn. Environ. 2020, 31, 1860–1874. [Google Scholar] [CrossRef]
  35. López-Belmonte, J.; Moreno-Guerrero, A.J.; López-Núñez, J.A.; Pozo-Sánchez, S. Analysis of the Productive, Structural, and Dynamic Development of Augmented Reality in Higher Education Research on the Web of Science. Appl. Sci. 2019, 9, 5306. [Google Scholar] [CrossRef]
  36. Lin, C.-Y.; Chai, H.-C.; Wang, J.-Y.; Chen, C.-J.; Liu, Y.-H.; Chen, C.-W.; Lin, C.-W.; Huang, Y.-M. Augmented reality in educational activities for children with disabilities. Displays 2016, 42, 51–54. [Google Scholar] [CrossRef]
  37. Fidan, M.; Tuncel, M. Integrating augmented reality into problem based learning: The effects on learning achievement and attitude in physics education. Comput. Educ. 2019, 142, 103635. [Google Scholar] [CrossRef]
  38. Chen, C.-P.; Wang, C.-H. Employing Augmented-Reality-Embedded Instruction to Disperse the Imparities of Individual Differences in Earth Science Learning. J. Sci. Educ. Technol. 2015, 24, 835–847. [Google Scholar] [CrossRef]
  39. Gavish, N.; Gutiérrez, T.; Webel, S.; Rodríguez, J.; Peveri, M.; Bockholt, U.; Tecchia, F. Evaluating virtual reality and augmented reality training for industrial maintenance and assembly tasks. Interact. Learn. Environ. 2015, 23, 778–798. [Google Scholar] [CrossRef]
  40. Khan, T.; Johnston, K.; Ophoff, J. The Impact of an Augmented Reality Application on Learning Motivation of Students. Adv. Hum.-Comput. Interact. 2019, 2019, 7208494. [Google Scholar] [CrossRef]
  41. Ibáñez, M.B.; Portillo, A.U.; Cabada, R.Z.; Barrón, M.L. Impact of augmented reality technology on academic achievement and motivation of students from public and private Mexican schools. A case study in a middle-school geometry course. Comput. Educ. 2020, 145, 103734. [Google Scholar] [CrossRef]
  42. Sahin, D.; Yilmaz, R.M. The effect of Augmented Reality Technology on middle school students’ achievements and attitudes towards science education. Comput. Educ. 2020, 144, 103710. [Google Scholar] [CrossRef]
  43. Yuen, S.C.-Y.; Yaoyuneyong, G.; Johnson, E. Augmented Reality: An Overview and Five Directions for AR in Education. J. Educ. Technol. Dev. Exch. 2011, 4, 119–140. [Google Scholar] [CrossRef]
  44. Etikan, I. Sampling and Sampling Methods. Biom. Biostat. Int. J. 2017, 5, 215–217. [Google Scholar] [CrossRef]
  45. Saunders, M.; Lewis, P.; Thornhill, A. Research Methods for Buniess Students; Pearson: London, UK, 2007. [Google Scholar]
  46. Eekhout, I.; de Vet, H.C.; Twisk, J.W.; Brand, J.P.; de Boer, M.R.; Heymans, M.W. Missing data in a multi-item instrument were best handled by multiple imputation at the item score level. J. Clin. Epidemiol. 2014, 67, 335–342. [Google Scholar] [CrossRef]
  47. Watkins, M.W. Exploratory Factor Analysis: A Guide to Best Practice. J. Black Psychol. 2018, 44, 219–246. [Google Scholar] [CrossRef]
  48. Varannai, I.; Sasvari, P.; Urbanovics, A. The Use of Gamification in Higher Education: An Empirical Study. Int. J. Adv. Comput. Sci. Appl. 2017, 8, 1–6. [Google Scholar] [CrossRef]
  49. Martinez, H.; Laukkanen, S. STEDUS, a New Educational Platform for Augmented Reality Applications. In At the Edge of the Rift; Gregory, S., Jerry, P., Jones, N.T., Eds.; Brill: Leiden, The Netherlands, 2019; pp. 37–49. [Google Scholar] [CrossRef]
  50. Antonioli, M.; Blake, C.; Sparks, K. Augmented Reality Applications in Education. J. Technol. Stud. 2014, 40, 96–107. [Google Scholar] [CrossRef]
  51. Cai, S.; Chiang, F.-K.; Sun, Y.; Lin, C.; Lee, J.J. Applications of augmented reality-based natural interactive learning in magnetic field instruction. Interact. Learn. Environ. 2016, 25, 778–791. [Google Scholar] [CrossRef]
  52. Wang, H.-Y.; Wang, J.-H.; Zhang, J.; Tai, H.-W. The Collaborative Interaction with Pokémon-Go Robot uses Augmented Reality technology for Increasing the Intentions of Patronizing Hospitality. Inf. Syst. Front. 2021, 26, 107–119. [Google Scholar] [CrossRef]
Table 1. Drivers for adopting ART.
Table 1. Drivers for adopting ART.
Drivers Mean Score (MS)Std. Deviation (SD)Rank
Need for improvement in teaching and learning3.070.7991
Readiness to digitise teaching and learning2.950.7852
Satisfactory teaching and learning project delivery2.950.8153
Availability of resources for ART adoption2.950.8444
Improved accuracy and precision in teaching2.910.8115
Reduction of misinformation among teachers and learners2.860.8046
Gamification of ARTs2.840.7547
Organisational structure of institutions of learning2.840.7858
Organisational mission and vision of the institutions of learning2.840.8439
Quality assurance in teaching and learning2.810.82410
Improved health and safety of teachers and learners2.790.96511
Organisational leadership support for ART adoption2.650.87012
Table 2. Driver’s Pattern Matrix.
Table 2. Driver’s Pattern Matrix.
Pattern Matrix a
Factor
123
Readiness to digitise teaching and learning0.877
Availability of resources for ART adoption0.818
Need for improvement in teaching and learning0.636
Gamification of ARTs0.634
Organisational mission and vision of the institutions of learning 0.858
Organisational structure of institutions of learning 0.800
Organisational leadership support for ART adoption 0.680
Satisfactory teaching and learning project delivery 0.464
Improved accuracy and precision in teaching 0.814
Improved health and safety of teachers and learners 0.762
Quality assurance in teaching and learning 0.756
Reduction of misinformation among teachers and learners 0.739
Kaiser–Meyer–Olkin measure of sampling adequacy0.653
Bartlett’s test of sphericityApprox. Chi-square279.263
Df66
Sig0.000
Cronbach’s Alpha0.814
a—Direct Oblimin rotation.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Akinradewo, O.; Hafez, M.; Aigbavboa, C.; Ebekozien, A.; Adekunle, P.; Otasowie, O. Innovating Built Environment Education to Achieve SDG 4: Key Drivers for Integrating Augmented Reality Technologies. Sustainability 2024, 16, 8315. https://doi.org/10.3390/su16198315

AMA Style

Akinradewo O, Hafez M, Aigbavboa C, Ebekozien A, Adekunle P, Otasowie O. Innovating Built Environment Education to Achieve SDG 4: Key Drivers for Integrating Augmented Reality Technologies. Sustainability. 2024; 16(19):8315. https://doi.org/10.3390/su16198315

Chicago/Turabian Style

Akinradewo, Opeoluwa, Mohammed Hafez, Clinton Aigbavboa, Andrew Ebekozien, Peter Adekunle, and Osamudiamen Otasowie. 2024. "Innovating Built Environment Education to Achieve SDG 4: Key Drivers for Integrating Augmented Reality Technologies" Sustainability 16, no. 19: 8315. https://doi.org/10.3390/su16198315

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop