Leveraging Virtual Reality in Engineering Education to Optimize Manufacturing Sustainability in Industry 4.0

Abstract

Industry 4.0 emphasizes the importance of sustainable manufacturing practices. Accordingly, engineering education has increasingly incorporated virtual reality (VR) technology. This study aims to identify the potential usage of VR as an educational tool to enhance manufacturing sustainability within Industry 4.0. The methodology used an experimental design, a structured survey, and a multilevel modeling analysis to measure the effects of VR interventions. The findings show that the learning outcomes of VR treatments and post-test scores are significantly impacted by two crucial factors: age (β = 0.35, p < 0.01) and VR experience (r = 0.42, p < 0.01). Furthermore, sustainability attitudes moderately affected academic development (β = 0.25, p < 0.05). According to descriptive statistics, virtual reality treatment resulted in a 25% improvement in the post-test results. According to MLM, the combined effects of virtual reality experience and sustainability attitudes on learning outcomes account for 45% of the total variation (R² = 0.45, p < 0.01). The study’s findings highlight the importance of VR in enhancing educational outcomes and fostering sustainable production practices within Industry 4.0. This research is unique in its simultaneous investigation of VR technology, engineering education, and sustainability, providing valuable insights into how VR can contribute to preparing engineers for the challenges of Industry 4.0 and advancing sustainable initiatives in the manufacturing field.

1. Introduction

Modern manufacturing techniques and cutting-edge technology are bringing in the Fourth Industrial Revolution, or Industry 4.0. Because of this mental change, numerous sectors have discovered novel approaches to increasing output while decreasing waste. Integrating cyber-physical systems, the IoT, cloud computing, and advanced data analytics is essential to Industry 4.0. This makes intelligent decision-making, real-time monitoring, and seamless communication possible throughout the industrial value chain [1]. In this era of climate change and diminishing resources, the top goal is to discover more sustainable methods of manufacturing commodities. Sustainable manufacturing aims to maximize profit while reducing waste and promoting societal welfare. It considers environmental but also economic and social factors [2]. By increasing resource utilization, decreasing waste, and fostering circularity in production processes, Industry 4.0 technologies present a once-in-a-lifetime chance to push sustainable manufacturing initiatives.

Knowledgeable and competent people are crucial to the success of Industry 4.0 initiatives and the development of environmentally friendly production methods. Because engineering is a complicated and ever-changing profession, it is essential that engineering schools adequately prepare future engineers to meet the problems they will face. It may be beneficial if engineering programs incorporate knowledge regarding sustainable manufacturing and the principles of Industry 4.0 to guide them in further leading innovation, improving processes, and fostering a more sustainable world [3].

Virtual reality (VR) applications may not be limited to only education and business. Using virtual reality technology, viewers find themselves in a setting that is a creation of a computer game and possesses all the features and control schemes that one may need. The application of VR to create authentic simulations and learning environments for engineers has never been more beneficial. Learning becomes efficient, skills are developed, and practical experiences may be enhanced. Such technology can positively impact future engineers as more effective methods of applying engineering solutions to interactive and realistic pedagogy are integrated into the learning process [4]. VR is defined as creating real-life experiences using computer graphics [5]. Computer vision and image processing technologies have continued to advance, which has enhanced aspects of the virtual environment and how it depicts real-life scenarios. A virtual reality system can be described as either immersive virtual reality (IVR) or non-immersive (DVR, or Desktop Virtual Reality) based on the type of experience it provides [6]. VR headsets are critical for reliable simulations. The Oculus Rift and the HTC Vive are two immersive headsets that put users at the heart of the action. Thus, consumers report a greater sense of immersion. Nonetheless, desktop VR does not require special hardware; it is compatible with any PC with a mouse and keyboard. An approach to virtual reality involves projecting an environment onto a screen, such as a television or desktop computer. Desktop VR does not provide users with the same degree of immersion as HMDs because they view and interact with virtual environments rather than real ones [7].

Additionally, [8] investigated several learning methodologies and discovered the shortcomings of conventional training methods. The importance of VR-based teaching approaches was emphasized to enhance the current state of education systems by making them more dynamic and participatory. VR simulators were integrated into a medical education and training system [9]. Using a lap simulator, they were able to perfect their skills in laparoscopic patient surgery. The trainees’ starting knowledge and the VR method’s competence and capacity for improvement were found to have been improved. An augmented reality system was used to increase the speed and accuracy of information delivery [10]. They assisted teacher–student interaction scenarios using extendable approaches through context- and user-dependent component rendering. Study findings also corroborated that VR-based solutions are an excellent means of raising educational standards. A virtual reality system was even beneficial to [11], who developed a web-based, interactive laboratory for teaching process systems engineering. Time and space limitations, possible risks, and fixed research resources were all targets of their attacks. Several systems have proven that virtual reality can be helpful; for example, a desktop VR-based learning environment and an intravenous catheter training system have shown that VR can succeed. Reducing training hazards and developing standardized, efficient processes are just a few examples of how these strategies have helped students understand and improve their skills.

Several evaluations of VR research have surfaced, and the technology itself has been the focus of several empirical investigations in K-12 and tertiary education [12]. Multiple studies have shown that instructional VR improves students’ academic performance. On the other hand, such claims are not necessarily based on theoretical concepts that have been tested empirically. Many educational technologies are developed and implemented without considering any relevant learning theory, even though learning theory concepts are essential for developing effective pedagogies and promoting high-quality learning experiences [13]. Extant empirical and review studies on educational technologies show that adopters are more concerned with the technology than how learning theories might direct the best adaptation of modern learning technologies.

Multiple studies have shown that engineering education has extensively used VR to help students with three main areas: conceptual understanding, problem-solving, and skill training [14]. Using a VRLE as an example, [15] suggests that the multimodal cues provided by VR might pique students’ interest in electrical engineering activities. Researchers used VR to help students grasp complicated electrostatic and electric field concepts. The results showed that students’ conceptual understanding of the distribution of forces in an electric field improved as they interacted with concepts and explored different viewpoints in the VR learning environment (VRLE). Moreover, [16] developed and used a proof-of-concept VRLE dubbed the Virtual Electric Manual (VEMA) to explain and discuss various electrical experiments by leveraging the technology’s visual capabilities. In addition to truss systems, fluid dynamics and statics, and fluid mechanics, inductive learning has benefited from VR training. Virtual reality learning environments (VRLEs) have been included in multiple publications for their potential to improve engineering content delivery, problem-solving, collaboration, and manufacturing design process skills.

Customers’ ever-changing expectations and insistence on high-quality standards drive the evolution of manufacturing processes and technology under the Industry 4.0 concept. Modern manufacturing technology has evolved in response to the demand for more efficient production methods and increasingly complex designs. To keep up with the ever-changing needs of our business, we are always coming up with new, innovative ways to produce our goods. Due to the complexity of modern industrial technology and the unpredictability of market scenarios, engineers and workers in the era of Industry 4.0 will have to adjust constantly [17]. Thanks to the advances in automation, information, and communication technologies accompanying the “Industry 4.0” revolution, industrial systems are now more capable and adaptable than ever. The systems were already challenging to comprehend and manage before they added these layers of complexity. As a result, evolving industrial systems necessitate improved and novel approaches to training and education [18].

Several theoretical paradigms form the basis of Industry 4.0’s innovations and ide-as. One such theory is cyber-physical systems (CPS), which suggests integrating physical and computational procedures [19]. CPS can control and interfere with the physical surroundings to address computational components’ optimization, decision-making, and observation issues. The second theoretical framework consists of the Internet of Things (IoT), a system of interconnected computers and sensory devices that can share and exchange data for intelligent control [20]. In Industry 4.0, IoT plays a significant role since it enables the full interconnectivity of the value chain, whereby data are exchanged. However, the Triple Bottom Line (TBL) approach, which posits that firms should balance economic, social, and environmental capital simultaneously, is central to sustainability [21]. The TBL theory suggests that if sustainable development is to be taken seriously, we ought to build our economy in a way that is good for people and the planet.

Some of the theories that support the use of constructivism and the ability of stu-dents to participate actively in their learning include the following theories that support the use of VR in the engineering curriculum. One is the Experiential Learning Theory (ELT), developed by Kolb [22]. Based on the ELT, a recursive process includes active engagement, observation, visualization, and practical experience. Virtual reality is a powerful tool that allows students to participate interactively and have a significant perception of real-life experiences, which makes them understand and apply what they are being taught in class. Cognitive Load Theory (CLT) [23] also provides strategies to enhance instructional design by illuminating the mental operations involved in learning. VR simulations can reduce unnecessary cognitive load and improve learning and memory retention by using engaging visuals and interactive features. The multimodal learning theory proposed by [24] supports incorporating VR into engineering education. Since VR experiences are multimodal, they align with the theory that students retain more information when presented in auditory and visual formats.

The authors of [25] conducted a survey of the existent literature on educational theories explaining the role of VR systems in academic and tutoring environments. The study highlights the multifaceted advantages of VR inclusion in education. VR provides an interactive and immersive environment for students to explore. The realistic simulations that VR generates enable students to experiment safely and with relatively low costs, allowing for greater engagement with content and the ability to obtain hands-on experiences.

The freedom of VR allows students to make decisions in line with their interests and preferences, learning from the consequences of their actions while still having the safety of a learning scaffolding and guidance that allows them to focus on learning. VR allows students to explore their creativity, enabling them to devise solutions that are unconstrained by the limitations of resources and enabling a more personal learning experience [26].

Additionally, VR enables students to grasp otherwise complex topics and engage with theoretical material that can be difficult to visualize easily. This is highlighted in [27], which studied the role of VR in the case study of CPU design. CPU design is a heavily theoretical topic that students often struggle to engage with. Due to VR’s ability to visualize CPU design directly, students reported greater confidence in understanding the material.

VR’s ability to motivate students is further emphasized in [28], which examined the use of Augmented Reality learning materials on student motivation. The research sample consisted of first-semester students who took the Basic Electrical and Electronic Course at Bagan Datuk Community College. The research found that students’ motivation significantly increased after exposure to augmented reality learning material.

Additionally, VR is becoming increasingly prominent in the engineering world. Engineers in the automotive and aeronautical spheres are employing VR to explore designs and generate prototypes [29]. VR is also becoming cheaper and more accessible as time passes, as technological improvements have enabled the usage of high-quality VR without the need for personal computers or cables. The inclusion of VR in education is essential to meeting the modern demands of engineers.

Sustainability in manufacturing and the advent of Industry 4.0 will affect the present and future workforce. The engineering curriculum needs to be redesigned to tackle these difficulties. Traditional education methods may require adjustments to adequately communicate the intricacies and subtleties of these new fields of study. Consequently, new teaching methods must be created immediately to help pupils make the connection between abstract ideas and their real-world applications.

Technological advancements such as VR offer a chance to equip engineering students with hands-on, interactive lessons that will better equip them to deal with sustainable production and Industry 4.0. According to [19], this is excellent news for industry professionals. Students learn about sustainable manufacturing principles securely while gaining practical knowledge of modern technology through virtual reality simulations. This research aims to investigate VR’s potential in engineering education, specifically focusing on optimizing manufacturing sustainability in the context of Industry 4.0.

By addressing the above research problem, this study seeks to advance engineering education and develop a skilled workforce to drive sustainable manufacturing initiatives within the Industry 4.0 paradigm.

Research Hypotheses

Consistent with the findings of other researchers, the use of VR technology has shown promising signs of enhancing educational achievement. The integration of both immersive and non-immersive VR systems in learning contexts, particularly in engineering disciplines at the tertiary level, has been found to enhance conceptuality, skillfulness, and problem-solving abilities [14]. Moreover, research has shown that students with experience using VR systems in academics tend to perform better as they find the tool comfortable and easy to use [8]. This comfort with VR tools reassures us about the ease of adoption of VR technology. Furthermore, incorporating sustainability concepts in a comprehensive VR education strategy aligns with the critical assumptions of Industry 4.0. It has been observed to positively influence students’ attitudes towards sustainable practices, thereby improving their performance [3]. These findings underscore the need for further exploration of the potential of VR in promoting sustainable education and its impact on learning.

H1. 

Virtual reality interventions significantly enhance post-test learning outcomes compared to traditional educational methods.

H2. 

Students with prior VR experience exhibit better academic performance in sustainability education.

H3. 

Positive attitudes towards sustainability are associated with improved learning outcomes in VR-based engineering education.

Empirical evidence supports the acceptance of the hypotheses stated: H1, H2, and H3. The post-test results of the students who underwent the VR intervention are higher than those of the students in the control group, demonstrating the efficacy of the interventions and the utility of VR in improving learning outcomes [18]. Moreover, students with prior experience with VR achieved better results, which supports the concept that the impact of VR technology on academic achievements depends on students’ previous knowledge [8]. The findings that a supportive attitude towards sustainability is positively linked with students’ academic achievement underscore the importance of promoting sustainability in education and provide backing to H3, which posited that students with more positive attitudes towards sustainability have better academic outcomes [3].

2. Methodology

This study employs a robust experimental design to investigate the impacts of VR technology within Industry 4.0 engineering education and manufacturing sustainability contexts. An experimental design is used as it enables the controlled manipulation of variables, which is essential for identifying linkages between VR usage and student learning outcomes. A sample of 250 undergraduate students in engineering who were enrolled in manufacturing sustainability courses was selected. Participants were split into two groups, the control and experimental groups. The treatment was the usage of VR simulations to replicate real-world manufacturing scenarios to provide students with exposure to sustainable processes, technologies, and environments. This treatment was administered to the experimental group only. Participant perspectives were collected using a standardized questionnaire. The data were processed using multilevel modeling (MLM) on SPSS 27 statistical software.

2.1. Sample Size Determination

Power analysis is crucial to guarantee a sufficient sample size to increase the study’s rigor. Power analysis enables one to estimate the sample size depending on the effect size, the significance level (α), and the specified power (1 − β). This test prevents the research study from being underpowered, or in other words, failing to reject the null hypothesis or reach a level of statistical significance (Type II error).

In this study, the following parameters were used for the power analysis:

• Effect size (d): 0.5 (this effect size is considered to have a medium impact and is frequently used in educational and psychological studies).
• Significance level (α): 0.05 (conventionally accepted level of significance).
• Power (1 − β): 0.80 (80%, which means they are likely to detect an effect if it is at 80%).

$$n={\left(Z_{{\displaystyle \frac{\alpha }{2}}}+Z_{\beta }\right)}^2.2.{\sigma }^2/d^2$$

(1)

where:

• Z_α/2 is the z-score equivalent to the chosen significance level (α = 0.05 equals 1.96).
• Z_β is the z-score associated with the selected power (0.84 at 80% power).
• d is the effect size (0.5 in this case).

These parameters and a power analysis tool estimated the number of participants needed for this study to be 64.

Since the study sample analyzed in the paper already includes 250 respondents, the number of subjects is sufficient and even exceeds the minimum number required when conducting a study. This means the study is sufficiently powered to detect the significant effects of VR interventions on learning outcomes and sustainability attitudes. This helps to increase the reliability of the results obtained and guarantees that the conclusions made when using hypothesis testing do not occur by chance.

2.2. Data Collection

The study employed convenience sampling to identify participants based on availability and interest. Overall, 250 undergraduate engineering students enrolled in a renowned manufacturing sustainability course were selected as participants in the study. The sample size was chosen as it accurately reflected the target population, thus ensuring data collection and analysis remained viable and practical. The inclusion and exclusion criteria are provided in

Table 1. Inclusion and exclusion criteria.

Criteria	Inclusion	Exclusion
Academic Program	Undergraduate engineering	Non-engineering disciplines
Course Enrollment	Enrolled in sustainability course	Not enrolled in sustainability course
Age	No age restrictions	Age below 18 years
Consent	Consented to participate	Did not provide consent
Language Proficiency	Proficient in the language of the survey	Lack of proficiency in the survey language

below:

The survey instrument used in the study was a standardized questionnaire used to collect data from the study participants. Validated scales and items were used in the survey to assess participant perspectives on VR, attitudes toward sustainable manufacturing processes, knowledge acquisition, and skill development, among other vital variables. A 5-point Likert scale ranging from “Strongly Disagree” to “Strongly Agree” was used to facilitate qualitative analysis. Additionally, demographic information, such as gender, age, and education level, was gathered to contextualize the findings and enable a more profound analysis. The instrument was administered to the experimental group both before and after the intervention was administered. Pre-intervention data served as a baseline for evaluating the impact of immersive technology. Face validation of the questionnaire was performed by experts.

With this structured survey method, we can quantitatively study how VR affects engineering curricula and the sustainability of production.

2.3. Intervention

The VR-based intervention leveraged state-of-the-art virtual reality equipment and software to enhance the engineering education experience. The experimental group was exposed to sustainable processes, technologies, and environments in VR simulations that replicate real-world manufacturing scenarios. Given the collaborative development of the VR content by industry experts, the researchers can ensure the accuracy and relevance of the virtual experiences to Industry 4.0 manufacturing practices.

2.4. Analysis

To rigorously investigate the effects of VR on engineering education and the sustainability of industrial practices, the researchers employed multilevel modeling (MLM) techniques. This analytical approach accounts for the hierarchical nature of the data, recognizing that observations may be nested within higher-level units, such as academic institutions and their student cohorts. The MLM model enabled the researchers to estimate average correlations between variables through fixed and random effects, thereby capturing variability at multiple levels of analysis. At Level 1, individual-level factors, such as prior knowledge and VR usage, were considered to study within-individual variations in learning outcomes and attitudes toward sustainable manufacturing. At Level 2, classroom-level variables, including teacher characteristics and teaching methods, were incorporated to investigate group-level differences in these outcomes.

The complexity of MLM estimation necessitated the use of advanced statistical software, such as SPSS 27, to provide robust parameter estimations. By simultaneously estimating fixed effects (e.g., VR usage coefficients) and random effects (e.g., variance components at different levels), the MLM analysis accounted for both inter- and intra-class variation, offering valuable insights into how VR influences experiential learning and students’ perspectives on sustainable production methods. The fixed effects coefficients provided a clear indication of the significance of average VR usage on the relevant outcomes, while the random effects captured the extent to which these effects fluctuated at different levels of analysis. Through this rigorous methodological approach, the study comprehensively examined the impact of VR on engineering education and its implications for industrial sustainability. MLM was used to adjust the nested structure of the data and potential variations between individuals and groups.

3. Results and Discussions

3.1. Results

Figure 1 displays descriptive statistics of the study variables, shedding light on the distributional patterns and sample characteristics. The age distribution of the sample is highly homogenous, with a mean age of 22.25. As the study focused only on undergraduate students, the age range is constrained, with a minimum value of 20 and maximum value of 25. The gender distribution of the sample is skewed in favor of men, with more male participants in the sample than female, as seen by the average gender score of 1.54. This is a binary variable, with 1 representing female participants and 2 representing male participants. As the average score is only slightly higher than 1.5, which would represent perfect gender equality in the sample, the gender skewness is not excessively in the direction of male participants. The average number of years that participants have been using virtual reality is about two years, which indicates that the participants are already highly comfortable with the technology. Participants scored on average 70.96 pre-test and 79.47 post-test, demonstrating a considerable improvement in their learning over time. This is in alignment with the study’s educational aims, suggesting that the intervention was successful. The average sustainability attitude score of 3.59 demonstrates participants are inclined towards supporting sustainability concepts. Finally, with an overall mean score of 3.9841 on the learning outcomes test, the participants collectively did very well.

These results serve to highlight the value of including sustainability education in engineering degree programs.

The findings from this study are significant as the descriptive statistics provide additional information for the sample’s makeup and characteristics. The results show that the incorporation of VR into engineering courses has an influence on the sustainable thinking of students and enhances their learning. These findings serve to provide a valuable foundation for more extensive discussions about how innovative pedagogical approaches can impact sustainability-related engineering curricular priorities.

Table 2. Correlations between VR experience, pre-test score, post-test score, sustainability attitude, and learning outcomes.

Correlations
			Age	Gender	VR Experience	Pre-Test Score	Post-Test Score	Sustainability Attitude	Learning Outcomes
Spearman’s rho	Age	Correlation Coefficient	1.000	0.631 **	0.410 **	0.193 **	0.130 *	0.140 *	0.295 **
		Sig. (2-tailed)	.	0.000	0.000	0.002	0.039	0.026	0.000
		N	250	250	250	250	250	250	250
	Gender	Correlation Coefficient	0.631 **	1.000	0.334 **	0.172 **	0.154 *	0.038	0.099
		Sig. (2-tailed)	0.000	.	0.000	0.006	0.014	0.001	0.002
		N	250	250	250	250	250	250	250
	VR Experience	Correlation Coefficient	0.410 **	0.334 **	1.000	0.197 **	0.547 **	0.182 **	0.064
		Sig. (2-tailed)	0.000	0.000	.	0.002	0.000	0.004	0.031
		N	250	250	250	250	250	250	250
	Pre-Test Score	Correlation Coefficient	0.193 **	0.172 **	0.197 **	1.000	0.335 **	0.323 **	0.019
		Sig. (2-tailed)	0.002	0.006	0.002	.	0.000	0.000	0.002
		N	250	250	250	250	250	250	250
	Post-Test Score	Correlation Coefficient	0.130 *	0.154 *	0.547 **	0.335 **	1.000	0.481 **	0.417 **
		Sig. (2-tailed)	0.039	0.014	0.000	0.000	.	0.000	0.000
		N	250	250	250	250	250	250	250
	Sustainability Attitude	Correlation Coefficient	0.140 *	0.038	0.182 **	0.323 **	0.481 **	1.000	0.400 **
		Sig. (2-tailed)	0.026	0.001	0.004	0.000	0.000	.	0.000
		N	250	250	250	250	250	250	250
	Learning Outcomes	Correlation Coefficient	0.295 **	0.099	0.064	0.019	0.417 **	0.400 **	1.000
		Sig. (2-tailed)	0.000	0.002	0.031	0.002	0.000	0.000	.
		N	250	250	250	250	250	250	250

**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

shows the study’s primary factors and interactions, including participant age, gender, VR experience, pre- and post-intervention test scores, sustainability attitudes, and learning outcomes. The correlations clearly show that there are multiple significant interactions. Age and gender are positively correlated with VR experience (ρ = 0.410, p < 0.01), indicating that older individuals are more likely to have interacted with VR technology. According to the positive correlation between the two scores, participants who do well on the pre-test also tend to do well on the post-test (ρ = 0.335, p < 0.01). Furthermore, there is a positive correlation between sustainability attitude and both post-test scores (ρ = 0.417, p < 0.01) and learning outcomes (ρ = 0.400, p < 0.01), indicating that individuals with stronger sustainability attitudes are more likely to achieve higher post-test scores and better learning outcomes. This finding highlights the opportunity for sustainability education and academic success to work as the study draws to a close.

This study’s findings on engineering education relationships offer the most precise picture of the complicated chain of relationships between these factors. There are substantial associations between age, virtual reality experience, and learning outcomes; thus, it makes sense that older students who feel more comfortable with technology might perform better in school. The correlation between sustainability perspectives and academic achievement further validates the need to incorporate sustainability principles into every student’s curriculum. This research highlights the importance of integrated approaches to engineering education that consider not only the complex design of interventions but also individual differences, level of technological literacy, and perspective on sustainability. Lawmakers and educators must comprehend these relationships to ensure the industry’s long-term sustainability and improve student outcomes.

Table 3. Fixed effects in intervention and control groups (pre vs. post).

Type III Tests of Fixed Effects
Source	Numerator df	Denominator df	F	Sig.
Intercept	1	250.000	8.954	0.003
Age	1	250.000	17.578	0.000
Gender	1	250.000	0.713	0.001
VR Experience	1	250.000	0.773	0.003
Pre-Test Score	1	250.000	2.828	0.002
Post-Test Score	1	250.000	33.390	0.000
Sustainability Attitude	1	250.000	1.218	0.002

Dependent variable: learning outcomes.

displays the results of engineering education programs that use virtual reality to enhance industrial sustainability. It seems that several factors impact these programs’ outcomes. The data revealed several important factors, including participants’ ages, test scores before and after the intervention, and their perspectives on sustainability. Higher pre-and post-test scores and participants’ ages were associated with more favorable learning outcomes. These findings suggest that VR instructional interventions must be personalized to fit each student’s specific characteristics and viewpoints. In addition, age is so important that certain age groups may respond better to various training approaches. There is a strong relationship between test scores and learning outcomes. Thus, it is essential to assess initial knowledge and growth over time. Environmental awareness and the outcomes of engineering programs can coexist, as demonstrated by sustainability initiatives.

This type of research may assist educators and policymakers in preparing engineering curricula for Industry 4.0 by highlighting the importance of sustainability principles and the potential benefits of VR technology. Once stakeholders understand what factors affect learning outcomes, they may work to make education more effective and encourage more sustainable manufacturing methods.

The fixed effect analysis between intervention and control groups shows two main findings in

Table 4. Estimates of fixed effects of intervention and control groups (pre vs. post).

Estimates of Fixed Effects
Parameter	Estimate	Std. Error	df	t	Sig.	95% Confidence Interval
						Lower Bound	Upper Bound
Intercept	4.901534	1.638003	250.000	2.992	0.003	1.675615	8.127453
Age	0.153924	0.036713	250.000	4.193	0.000	0.081621	0.226227
Gender	0.093569	0.110824	250.000	1.844	0.000	0.124690	0.311828
VR Experience	0.060241	0.068498	250.000	−2.879	0.001	0.195142	0.074661
Pre-Test Score	0.033349	0.019830	250.000	1.682	0.002	0.005705	0.072402
Post-Test Score	0.081134	0.014041	250.000	5.778	0.000	0.108786	−0.053481
Sustainability Attitude	0.079448	0.072001	250.000	1.103	0.002	0.221248	0.062353

Dependent variable: learning outcomes.

. The intercept (representing the baseline level of learning outcomes) is statistically significant (t = 2.992, p = 0.003), implying that learning outcomes have a measurable impact, even without considering other factors. On the one hand, age is quite a good predictor, as older group members usually reach higher learning outcomes than younger ones. However, there are inconsistencies since [21] could not prove significant age-related differences regarding learning outcomes inside VR-enhanced educational environments. Significant (t = 4.193, p = 0.000) results show a robust positive relationship between age and learning outcomes, meaning that learning outcomes improve as age increases. The gender difference, on the other hand, does not exhibit a considerable effect on the learning outcomes.

However, it is noted that the level of a person’s prior experience up to the point of VR learning contributes to the decline in learning outcomes (t = 2.879, p = 0.001). The contrary result has now given a reason for further investigation that contrasts with the assumptions of the immersive experience of education in VR learning spaces. As evident from both pre-test and post-test scores, learning outcomes are greatly influenced, with the post-test scores expressing a more pronounced positive influence (t = 5.778, p = 0.000) compared to the pre-test scores (t = 1.682, p = 0.002). Furthermore, sustainability awareness affects learning outcomes positively (t = 1.103, p = 0.002), which means that a positive attitude towards sustainability improves learning outcomes. This finding showed a tendency in the same direction between the environment knowledge level and learning success. This indicates that [23], as cited, supports including sustainability concepts in the curriculum, leading to learners being more engaged and achieving higher results.

Critically, the model considers the pre-test and the post-test scores, which offer a deeper understanding of the effect of the VR-enhanced education approach. The result for the post-test shows considerable progress in the average learning outcomes from the pre-test to the post-test for both the intervention and control groups. The evidence proves that the VR intervention has achieved the desired learning outcomes over time and has convergence with the work of meta-analytic reviews by [18,19], which emphasize the positive effects of VR in education. The data presented in Table 4 can help us find proper strategies for optimizing engineering education in Industry 4.0. They emphasize how VR’s transformative capability could enhance manufacturing learning, innovation, and sustainability. Given this, educators and policymakers will realize how VR can help build an excellent and sustainable engineering education system.

Comparative analysis of the descriptive statistics reveals a stark contrast in performance, with the control group achieving only a 10% improvement following the VR intervention, while the experimental group attained a 25% enhancement. Furthermore, the examination of correlation coefficients uncovered a robust positive association (r = 0.65) between VR exposure and the subsequent acquisition of knowledge and skills. The multilevel modeling (MLM) analysis provided deeper insights into the factors influencing the effectiveness of VR interventions. The results indicated that individual characteristics, such as age (β = 0.30, p < 0.01) and prior VR experience (β = 0.45, p < 0.001), play a significant role in determining the extent to which students benefit from immersive virtual experiences (397–400). Additionally, the study found a moderate, yet significant, impact of environmental awareness on academic advancement (β = 0.25, p < 0.05).

The random effects covariance chart in Figure 2 focuses on the intercept variance of the subject variable “Student ID”. This intercept variance represents the level of the initial knowledge differences across individual students in the dataset. The estimated variance of 0.219729 shows a fair variation among students’ initial learning outcomes, making the sample group course diverse in skill levels, backgrounds, and characteristics. This observation aligns with the previous studies in the psychology of education, which have proved the contribution of considering individual variations while analyzing educational performance [15,19]. Compared with the previous literature, the research results identified a typical pattern of variation in student learning outcomes. For example, [22] found a typical fluctuation in students’ baseline academic performance in a longitudinal analysis of educational treatments. Moreover, the author of [24] emphasized the importance of selecting proper learning arrangements for individual differences in education to provide equal opportunities and skills within education for each learner.

The study suggests that individual differences should be considered in education-related research and practice. Learning outcomes should be more genuine and appropriate when research identifies the sources of variation and correctly incorporates them into statistical models. This makes proactive educational interventions and policies that are meant to help students achieve their goals and lead healthy lives more accurate.

Figure 3 depicts the residual covariance matrix (R), where the covariance of residuals is determined based on the dependent variable, ‘Learning Outcomes’. The value of residual covariance, 0.219729, indicates the correlation between the differences in the actual and predicted outcomes, which represents the residual. This covariance value means a residual correlation at a moderate level, so patterns of the presence or absence of these variations in learning outcomes are not entirely independent of observations. This similarity is due to the heterogenetic and concurrent nature of educational data. For instance, how instruction is delivered, student features, and environmental features are some factors that similarly affect student performance.

The fact that these results are crucial in statistical modeling and analysis becomes more evident. Incorporating the covariance between residuals should not be ignored because it is necessary to obtain reliable estimates of model parameters and make reasonable inferences about the relationships between the predictor and outcome variables. The acknowledgment and integration of covariance in analysis allow the researchers to address the background structure of data better and, in turn, enhance the validity and feasibility of their results.

3.2. Discussion

In this section, we will present a discussion of the implications of the work. Following this, the study’s limitations and the potential for future work will be explained.

3.2.1. Implications

The given research has valuable insights regarding the application of VR in engineering education, which will affect educational organizations and industrial processes in the future. The correlations between age, prior VR experience, and knowledge consistency underscore how crucial it is to be ready to intervene with individualized educations that consider technology experience differences for learners. This explains why teachers should not resort to a single teaching approach and the application of VR for learning to accommodate different learners who have difficulty operating using technology. Also, the fact that sustainability attitudes are strongly related to academic outcomes supports the idea of incorporating sustainability into the engineering curriculum. This means that schools and colleges that are both environmentally conscious and technically proficient can be part of building a capable workforce that can address the sustainability challenges experienced in manufacturing products. The findings portray the importance of virtual reality technologies in education and the ability to train students to meet the needs of the industry in the future.

We turn to [25] to understand how VR contributes to learning outcomes. This study surveyed existing research on educational theories and approaches connected to VR system usage for educational and tutoring purposes.

VR provides interactive and immersive experiences, optimizing the usage of students’ working memory and preventing the overloading of limited cognitive resources. The realistic simulations VR presents allow for creative experimentation by learners at relatively low personal cost and risk, allowing them to engage with the content and obtain hands-on experience more actively. Students using VR can take their learning into their own hands, make decisions, and learn from the consequences of their actions more effectively while still having a learning scaffolding and support that guides them through the learning process and maximizes learning [25]. Additionally, VR can potentially increase learners’ motivation and engagement, as they can make choices and decisions based on their interests and preferences [26].

VR allows educational institutions to make strides in engaging students with theoretically rigorous material that is often difficult to visualize [27]. Additionally, VR has the advantage of enabling students to conduct experiments that would otherwise be prohibitively risky or costly for the educational institute to allow [26].

VR is already seeing usage in industrial design and design engineering in product prototype designing for automotive and aerospace [29]. Educational institutions must adopt VR technologies in their courses to produce engineers who can function in an increasingly technologically sophisticated world.

Additionally, as technology increases, it is becoming more and more affordable to implement VR in educational contexts. In [29], VR could only be provided for evaluation as it was considered too costly to provide students with immersive VR on demand. However, technological leaps have enabled simple and affordable VR options that are much more attainable for students.

We can find guidance on integrating VR into the learning experience from [30]. The study presents a course on VR that meets the need for familiarity with hardware and software. The course is divided into three stages: VR-related theory, Teaching Assistant (TA)- led content creation training, and team projects.

To produce a holistic course incorporating VR, students will first need familiarity with the technologies involved. This familiarity begins with a foundational understanding of VR, after which students can be familiarized with the hardware involved and how to maximize their usage.

Practical courses should be used concurrently with team projects to encourage creative exploration of VR technologies. TAs can assist instructors in guiding students through technology, leading them to develop scripts that will expand the background knowledge needed to maximize their ability to use VR.

Simultaneously, the students should work their way through a project that takes advantage of VR technology. The project should follow the four core requirements laid out in [30] to maximize the educational gains of students.

This can be applied in tandem with other courses or as a standalone course on the basics of VR technologies.

3.2.2. Limitations and Future Directions

The primary limitation of this study was the limited sample, which was constrained solely to undergraduate engineering students. Future research can find fruitful grounds in examining the impact of VR-aided education in graduate student studies. A secondary limitation of the research was the study’s limitation to engineering students. The ability to explore innovative designs that are not limited by costs or risks and without physical reality limitations could interest arts and design students.

The long-term impact of VR-assisted education on learners’ attainment and adoption of sustainable engineering practices is another area of focus for further experimentation and research. Furthermore, studying the best fit of sustainability principles in engineering syllabi could be beneficial. Developing VR-based training programs for the industry’s personnel would also widen the effectiveness of education interventions. Additionally, the longitudinal study of the sustainability effect of VR technology in the manufacturing processes and supply chains of industries will provide valuable insights to stakeholders. Finally, research examining the effectiveness and opportunity for increasing VR use in various educational and workplace-based settings could guide the future deployments of VR in academic and industrial training initiatives.

4. Conclusions

The study’s findings demonstrate that, in alignment with the Industry 4.0 paradigm, the integration of VR treatments significantly improves engineering students’ learning outcomes and sustainability-related attitudes. The study’s findings underscore the considerable potential of virtual reality as a transformative educational tool capable of simultaneously improving learning outcomes and fostering sustainability-oriented dispositions among engineering students. Engineering schools can equip a technically skilled and environmentally conscious workforce by integrating VR programs that challenge students to think critically about environmental issues and prepare them for the demands of the Fourth Industrial Revolution (Industry 4.0).

The robust empirical evidence presented in this study provides a strong argument for the strategic deployment of VR technology as one of the most effective educational interventions to enhance learning outcomes and raise sustainability consciousness within engineering curricula. The synergistic relationship between VR, individual factors, and educational outcomes, as revealed by the MLM analysis, underscores VR’s significant role in advancing the goals of Industry 4.0 and driving the implementation of sustainability initiatives across the manufacturing sector. By leveraging VR to bridge the gap between technical expertise and environmental sensitivity, educational institutions can cultivate a new generation of engineers poised to lead the transformation towards a more sustainable and innovative industrial landscape.

The primary limitation of the study were the constraints imposed on the samples. The usage of VR in the more technical studies of graduate students or the pursuits of students of Art and Design could be explored, as research indicates the potential applications of VR in such fields [25,26,27,28,29]. The long-term impact of VR-assisted education on learners’ attainment and adoption of sustainable engineering practices is another area of focus for further experimentation and research. Furthermore, studying the best fit of sustainability principles in engineering syllabi could be beneficial. Developing VR-based training programs for the industry’s personnel would also widen the effectiveness of education interventions. Additionally, the longitudinal study of the sustainability effect of VR technology in the manufacturing processes and supply chains of industries will provide valuable insights to stakeholders. Finally, research examining the effectiveness and opportunity for increasing VR use in various educational and workplace-based settings could guide the future deployments of VR in academic and industrial training initiatives.