Metaverse Platforms in Enhancing Student Interest in Teaching and Learning: A Pilot Study on Cryptography Using Ciphersphere

Metaverse Platforms in Enhancing Student Interest in Teaching and Learning: A Pilot Study on Cryptography Using Ciphersphere

F. Alifah M. Jaaffar*, Nor Hafizah Adnan

Faculty of Education, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.9020344

Received: 16 February 2025; Accepted: 20 February 2025; Published: 24 March 2025

ABSTRACT

This study examines the use of the Metaverse platform to increase student interest in education. The focus of the study is the development of the CipherSphere learning platform, developed through the Spatial.io platform, as well as its functionality and implementation. This study investigates how elements of Technology Acceptance Model (TAM), including Perceived Ease of Use (PEU), Perceived Usefulness (PU), and Perceived Enjoyment (PE), affect the goals of students using the Metaverse platform in the learning process. The study involves fifty Form Three students from a secondary school in Southern Malaysia. This study uses a design development research approach. The Metaverse platform uses TAM factors to teach the topic of Columnar Transposition in Fundamental Computer Science subjects. The findings show that Perceived Enjoyment does not have a significant impact on students’ willingness to use the Metaverse platform. In contrast, Perceived Ease of Use and Perceived Usefulness have a significant impact. This study provides great benefits to educators and educational technology developers as they think about and implement digital learning methods. To ensure students accept and engage, ease of use must be considered. The results of this study also suggest that further research is needed to understand the elements that influence the acceptance of the Metaverse platform in various educational contexts.

Keywords: Metaverse, Cryptography, TAM model, Computer Education

INTRODUCTION

Cryptography in Education

Cryptography is an important issue for computer science education, focusing on topics like cybersecurity awareness and applied theoretical concepts. Students learn principles to secure digital era sensitive knowledge by combining cryptographic concepts as encryption techniques and secure communication protocols. For example, gamified approaches, such as the “Crypto Go” card game, have enhanced engagement and understanding of cryptographic tools (González-Tablas et al., 2020). Introducing basic cryptography at an early stage of education will pay off later in better information literacy and enable competencies in cybersecurity (Zufarova AS et al., 2023). In addition, experiential learning approaches, including the application of the principles of blockchain, also contribute to the deepening of cryptology courses and comprehension of concepts (B. Wang & Li, 2021). Modern studies also focus on teaching classical algorithms like the Caesar Cipher to demonstrate practical applications of cryptographic systems (Vebby et al., 2023). To prepare students for advanced problem-solving in secure communication, such as addressing cryptographic challenges posed by quantum computing; the curriculum must evolve (Yadav, 2023).

Columnar transposition is another classic key-based cryptographical technique for rearranging a plaintext message into columns. It helps the student to understand the basic concept and logic in cryptography. Introducing metaverse in teaching cryptography improves the learning potential by immersion and interaction. Students can create a conceptual three-dimensional virtual space for the analysis and manipulation of a process such as columnar transposition.

According to Gan et al. (2023), the metaverse helps students to learn and become more engaged as it is personalised, experiential, contextual, meaningful for them. In this scenario, immersive VR used to capture student interest and levels of presence while actually performing the cryptography tasks (Lee & Kim, 2021). Flipped Classroom in the Metaverse — Through the use of flipped classroom techniques associated with the metaverse, students can work collaboratively, and apply their learning to context-realistic problems (Chamorro-Atalaya et al., 2023). Furthermore, applying the metaverse in the lessons on columnar transposition also increases students’ interest and actively involves them, which contributes to better comprehending the content of the cryptography subject through entertaining and interactive learning methods.

Metaverse

Within education, the metaverse is characterized as an interactive digitized world in which users are completely immersed. Such a space encourages learners to perform learning tasks and interact with their counterparts in real-time. This makes learning more dynamic (Chen et al., 2023). Rather than enhancing traditional educational materials with e-learning environments that rely on 2D images and text, learning in the metaverse is conducted in a continuous three-dimensional cyberspace where students can move around, work together, and interact with information through their avatars in virtual reality. A cinematic universe where one-plus-one is more than the man (Pentangelo et al., 2024; Suh & Ahn, 2022). Meta, Roblox, Zepeto, and many other platforms are trying to lead in how this new technology (metaverse) is incorporated into learning processes, offering learners enthralling immersive experiences and practical buttresses that combine fun and learning. In turn, this approach constitutes a remarkable deviation from conventional e-learning approaches where through static and ‘boring’ contents, students are passive recipients with very limited active interactions (Wang & Shin, 2022; Rahman et al., 2023) .

In addition, the exchange of real and virtual environments allows for more interactive experiences and activities such as role-playing, simulations and group works, which will enhance student’s experience (Buragohain et al., 2023; De La Asuncion Pari-Bedoya et al., 2023). Besides the interactive simulations, there are game elements, for example, rewards and leaderboards, that make lessons even more enjoyable for the students (Khair Naim & Kim Hua, 2024). The metaverse platform is positioned as an effective method for future education, offering possibilities like digital interaction among classmates. Additionally, it provides a more interactive and immersive learning experience, enabling students to participate in virtual simulations, role-playing exercises, and collaborative projects with peers worldwide (De La Asuncion Pari-Bedoya et al., 2023). As the students become more active participants in these engrossing events, so do the functions of educators and institutions. The new ways of teaching need to be grown into by the teachers, and the institutions of learning have to revolutionize their strategies in teaching so as to be able to incorporate the use of the modern tools. Besides an enhanced experience on the part of the students, the integration of the metaverse has the power to revolutionize the learning experiences of educators, institutions, and policy makers. It would be helpful for teachers to know the reasons that motivates students to participate actively in the metaverse since this, along with providing opportunities, will enhance learning and direct the design of educational programs in the future (Nguyen et al., 2024).

The use of technology in education, particularly interactive learning applications, plays a crucial role in enhancing student motivation and understanding of complex concepts. Research has shown that interactive digital tools significantly improve engagement and learning outcomes in science education (Ajang & Mohammad Yasin, 2024). In line with Malaysia’s 12th Development Plan and the Malaysia Education Blueprint 2013–2025, the government has made efforts to provide infrastructure such as electricity and internet access to all schools. These initiatives aim to support digital transformation in education, ensuring that students have access to emerging technologies that foster immersive and interactive learning experiences. After COVID-19, education systems are incorporating new-age innovations into age-old teaching practices. The shift to virtual environments has transformed education, creating new learning demands. Immersive technologies now bring new dimensions to the learning experience (Buragohain et al., 2023).

Educational policy makers seeking to use metaverse technology for pedagogical applications might significantly benefit from the managerial insights provided by the study (Nguyen et al., 2024). The curriculum also needs to include courses that enhance learning and the integration of emerging technologies such as the metaverse. For that matter, clear learning goals are very important; students with high self-efficacy understand better what they want to achieve. In the metaverse, instructors are able to set clear goals for each module that will help students stay focused (Al-Adwan et al., 2023). The development of emerging technologies and the rapid growth of digital information have resulted in significant paradigm changes within educational institutions (Krishnan et al., 2021).

The metaverse is a virtual environment that provides an engaging experience, supported by technologies like virtual reality (VR), augmented reality (AR), and artificial intelligence (AI). Further research should be conducted to fully understand the potential uses of metaverse technology in education (De La Asuncion Pari-Bedoya et al., 2023). Although the metaverse has the potential to enhance effectiveness in online learning experiences, the educational uses of the metaverse remain in their beginning stages (Al-Adwan et al., 2023). Users are bound to utilize the metaverse education platform if they find it easy to use and if there is social influence. When users think that the platform is user-friendly and friends with similar interests or public opinions about this platform influence it, they will be interested in seeing how helpful it can be in learning (G. Wang & Shin, 2022). The learning platform based on the metaverse should be very user-friendly and instinctive; otherwise, users will not perceive it as valuable if its use or access presents difficulties. Therefore, the platform should be designed by keeping user experience in mind, providing clear guidelines and tutorials, and ensuring that all features are easily accessible (Al-Adwan et al., 2023).

Metaverse is a place where students today can learn together and with others. They will gain information through the exploration process and hands-on practice, so they will be part of the learning process (De La Asuncion Pari-Bedoya et al., 2023).  Students may experience realistic simulations and creative alternatives thanks to the unified environment provided by Metaverse, which connect the gap between the actual and digital worlds  (Nguyen et al., 2024). These features not only reshape student participation but also significantly impact educators and institutions, requiring them to cope with this changing educational reality. Constructivist education should be embraced by educators and researchers, who should aim for student engagement and continuous participation in the learning process (Suh & Ahn, 2022). Utilizing virtual tools allows educators to create lessons that capture students’ attention, actively engage them, and support their development as proficient communicators (Khair Naim & Kim Hua, 2024).

Makrakis (2024) conducted a study that involved 1,815 in-service teachers from Indonesia, Malaysia, and Vietnam. The study highlighted the need for educators to improve their self-efficacy and transformative teaching skills in order to effectively integrate ICT and metaverse learning technologies into their teaching practices, particularly in addressing sustainability issues. The outcome is significant in Malaysia, as the growth of digital education is inseparable from global and national advances in digital technology. This is a new set of challenges the sector is facing, which demands adaptation for better competitiveness and growth (Ministry of Education Malaysia, 2023).

Lam and Norman (2024) found that metaverse-based learning significantly enhances primary school students’ achievement and interest. Their findings showed that students’ test scores after using metaverse technology were higher than their scores prior to the intervention, which employed conventional teaching methods. Furthermore, the Digital Education Policy (DEP) Malaysia, launched on November 28, 2023, aims to cultivate a digitally proficient generation within the education sector. This policy underscores the Ministry of Education Malaysia’s commitment to transforming the digital education landscape and fostering a digitally literate and competitive society.

Currently, the objective of the integration of digital technologies in education is to actively engage students and broaden teaching and learning technique, including the Internet of Things (IoT), artificial intelligence (AI), augmented reality (AR), and big data (Ministry of Education Malaysia, 2023). Nasir et al. (2023) suggest that learning from countries like South Korea could help Malaysia reach SDG 4 by making sure all students, no matter where they are or their background, have access to quality education through digital technology. As the metaverse evolves, its role in education will grow, transforming learning into an interactive and inclusive experience that boosts student success, and reshaping the approach of educators, institutions, and policymakers in this digital era.

Malaysia has initiated a policy known as the Digital Education Policy with the aim of joining the rest of the world in improving digital literacy. This policy focuses on creating a more inclusive and globally competitive education system by incorporating advanced technologies like the metaverse and fostering digital literacy (Ministry of Education Malaysia, 2023). Additionally, Nasir et al. (2023) propose that adopting best practices from leading nations like South Korea could expedite Malaysia’s progress toward achieving SDG 4 by ensuring that all students, regardless of their location or socio-economic status, have access to quality education enhanced by digital innovations. This way, as the metaverse continues to evolve, educators, institutions, and policymakers will leverage it as an emerging platform for education to make learning interactive, inclusive, and student-success-based, in addition to reimagining their practice in the digital era.

Problem Statement

Cryptography is an essential element of computer science education; nonetheless, students sometimes find its abstract principles challenging to understand. Conventional teaching techniques, including textbooks and static presentations, often insufficiently engage students and promote deep understanding. Ozer and Cetinkaya, (2024) identified that students have difficulties in mastering cryptographic techniques, underscoring the need for more participatory and stimulating teaching methods. The metaverse represents a significant potential of changing the educational experience using immersive and interactive virtual environments. Zhang et al. (2022) suggest that the metaverse, a three-dimensional digital environment that merges physical and virtual worlds, has great potential for the future of education. However, the beneficial effects of metaverse platforms in improving student engagement, comprehension, and acceptance in the subject of cryptography education remains majorly unexplored.

This project seeks to fill this gap by developing and evaluating CipherSphere, a metaverse-oriented cryptography educational platform. This method aligns with Camilleri (2024), who suggests that metaverse applications in education might improve students’ knowledge and abilities through immersive experiences. This research employs the Technology Acceptance Model (TAM) to examine the impact of Perceived Ease of Use (PEU), Perceived Usefulness (PU), and Perceived Enjoyment (PE) on students’ intentions to accept and employ metaverse-based learning platforms. The results are expected to offer valuable views for educators, policymakers, and developers in developing immersive and effective digital learning platforms.

Research Objectives and Research Questions

This study aims to build and test CipherSphere, an educational space built on the Spatial.io metaverse platform. The study goals are based on the three stages of DDR (Dependent-Independent-Relationship) and how they fit with the TAM (Technology Acceptance Model). The specific objectives are as follows:

1. To identify the key requirements for developing CipherSphere as a metaverse-based learning platform by assessing the relevance of PEU, PU, and PE factors.
2. To design and develop the CipherSphere learning environment within Spatial.io based on the Columnar Transposition concept.
3. To evaluate the usability and acceptance of CipherSphere among students based on PEU, PU, PE, and their relationship with Metaverse Behavioral Intention (MBI).

The study was conducted to reach the mentioned objectives. Therefore, it has significance in the field of computer science education practices that encompass stakeholders such as computer teachers, schools, the education technology department, policy makers at Ministry of Education (MOE), and other researchers. Essentially, efforts should be directed at improving students’ attitudes and interests in the use of metaverse platforms for learning.

In this study, we aim to answer the following research questions:

Research Questions 1: What are the key requirements for developing CipherSphere as a metaverse-based learning platform?

Research Questions 2: How does the design and development of CipherSphere contribute to the effectiveness of learning Columnar Transposition?

Research Questions 3: How do PEU, PU, and PE factors influence students’ intention to use CipherSphere?

LITERATURE REVIEW

Theoretical Basis

Technology Acceptance Model (TAM) and Theory of Reasoned Action (TRA) are two models commonly applied to explain the acceptance and usage of information technology. TRA, a psychological theory developed by Fishbein and Ajzen in 1975, is widely used to explain human behavior and is considered one of the most influential theories in this field (Nasri Wadie & Charfeddine Lanouar, 2012). It is the foundation for the TAM theory. According to TAM, a person’s intention to use technology is a key factor in whether they adopt it. This intention is influenced by several important factors, including Perceived Ease of Use (PEU), Perceived Usefulness (PU), and Perceived Enjoyment (PE) (Al-Adwan et al., 2023).

Scholars in several disciplines have become very fond of the Technology Acceptance Model. The TAM is a conceptual framework that helps in understanding users’ behavioral intentions in reference to adopting and utilizing technology (Feng et al., 2021). It is worth noting that knowing the behavioral intention of users of the metaverse technology, which integrates various information technologies marking the future of the Internet, is particularly valuable (Al-Adwan et al., 2023). TAM is theoretically sound and enjoys considerable empirical support across many contexts, including those involving the adoption of educational technologies (Feng et al., 2021). It is also quite useful to study metaverse behavioral intentions, as it deals precisely with perceived usefulness and perceived ease of use aspects, which are important in determining users’ behavioral intentions within the adoption of new technology.

In conclusion, the Technology Acceptance Model is a suitable framework for assessing metaverse behavioral intentions because it identifies factors that influence users’ perceptions and attitudes towards metaverse technology (Al-Adwan et al., 2023). The main objective of this study is to develop and assess the usability of the ChiperSpheres Learning Platform: Exploring the Metaverse in relation to perceived enjoyment (PE), perceived ease of use (PEU), perceived usefulness (PU), and metaverse behavioural intention concerning the Spatial.io platform and the cipher topic. The primary learning theories, namely the Theory of Reasoned Action model (TRA) and the extended TAM Model, underpin the development of the ChiperSpheres Learning Platform: Exploring the Metaverse to test the usability of the platform and enhance students’ interest in learning as illustrated in the figure below.

The conceptual framework suggests that efforts to increase students’ interest in cryptography can be improved by using the Chiper Spheres Learning Platform: Exploring the Metaverse, combined with the Discovery Learning approach to help students learn better.

Fig 1. Conceptual Framework

Perceived Ease of Use (PEU)

The Metaverse is designed to enhance the learning process and must be both advantageous and appealing to users; otherwise, its development is futile. Two constructs within the Technology Acceptance Model (TAM) are employed to assess students’ acceptance of any introduced technology. These constructs are Perceived Usefulness (PU) and Perceived Ease of Use (PEU) (Zulherman et al., 2023).

Perceived Ease of Use (PEU) refers to an individual’s perception of how easy it will be to use a certain system. An application that is easier to use than similar applications has a greater chance of getting accepted by users (Davis, 1989). Numerous studies have examined the role of perceived ease of use in the TAM model. PEU was measured as an assessment of students’ beliefs concerning how easy and simple the use of the metaverse for educational purposes can be (Al-Adwan et al., 2023). Perceived ease of use is “the degree to which a person believes that using an information system will be free of effort”. The perception of ease-of-use fosters positive attitudes towards technology, perceived usefulness and intention to use, perhaps by bolstering self-confidence in the use of the technology (Nasri Wadie & Charfeddine Lanouar, 2012).

Although very important in user perceptions, Perceived Ease of Use has less influence on Actual System Usage, as shown by its T value being less than the T-table value, indicating a significant negative impact on real-world system usage (Faisal et al., 2021). Understanding the role of Perceived Ease of Use is important for developers and educators of the Metaverse to correctly build a successful learning environment within this emerging technology.

Perceived Usefulness (PU)

Perceived usefulness (PU) is defined as the degree to which a user’s inclination to employ or refrain from technology is contingent upon their belief that it will aid in improving their job performance (Davis, 1989). PU concept is to determine whether the students will continue to engage, or to use educational technologies including the Metaverse or mobile learning consistently. We can then determine the extent to which students believe the communication tools we have identified as improving their learning based on PU.

The arguments made by the authors are summarised by Al-Adwan et al. (2023), whereby the authors explain that if the students find that the technology is useful to them in their learning then they are likely to improve the performance. Similarly, Voicu & Muntean (2023) stated that perceived usefulness is a good predictor of continued mobile learning, specifically, the higher a students perceived usefulness score the more likely he or she continue using mobile learning, while the opposite is likely for the low perceived usefulness score. An assessment of the gap in the understanding of PU can enhance the focus on enhancing technological applications to learning for the student.

Perceived Enjoyment (PE)

A study has revealed the following effects of perceived enjoyment in the metaverse environment. Perceived enjoyment looks at how interested and fun a person believes using an introduced technology will be. Several studies acknowledge that it refers to the person’s behaviour since the technology concerns their reception of the specific podium (Nasri Wadie & Charfeddine Lanouar, 2012).  In addition, the results of the quantitative analysis revealed that perceived enjoyment has a significant and positive effect on perceived usefulness (Toraman & Gecit, 2023). Perceived enjoyment is also identified as a key enabler of students’ behavioral intentions to adopt metaverse technology for education  (Al-Adwan et al., 2023). In addition, PE has a strong impact on the adoption of new Metaverse platforms (Pan et al., 2023).

In conclusion, research consistently shows that perceived enjoyment is a key factor in shaping individuals’ attitudes, intentions, and willingness to adopt metaverse technology, underscoring its significance in both educational settings and emerging Metaverse platforms.

METHODS AND DATA COLLECTION

Participants

This study involved 50 Form 3 students from a public secondary school in the southern state of Malaysia, enrolled in the Fundamentals of Computer Science subject. Consequently, the findings of this investigation are applicable solely to the involved educational institution. The sample was selected through purposive sampling, a commonly used strategy in educational research where particular information or knowledge from participants is necessary (Etikan et al., 2016). Form 3 students enrolled in the Fundamental Computer Science curriculum were selected based on their direct exposure to cryptography topics, rendering them the most suitable respondents for evaluating the usability and educational efficacy of the CipherSphere metaverse platform.

Purposive sampling was considered more suitable than random sample due to the need for participants having previous experience in cryptography, hence guaranteeing relevant and informative replies (Palinkas et al., 2015). Random sampling, although often used for research aimed at generalizability, does not ensure that participants have the requisite prior knowledge to provide substantive input. This technique corresponds with the study’s aim of assessing students’ evaluations of usability and efficacy derived from their direct interaction with the platform. This study utilizes purposive sampling to ensure that the data collected is relevant as well as aligned with the research aims, since participants had personal expertise in cryptography learning, delivering their input invaluable for assessing CipherSphere’s usability and effect.

Instrument

This study employs the Design and Development Research (DDR) methodology, introduced by Richey et al. (2003), following the principles of Type One DDR. This approach is widely recognized in educational technology research as it integrates both the systematic development of an educational product and its usability and effectiveness evaluation (Richey & Klein, 2007). The study focuses on designing and evaluating CipherSphere, a metaverse-based learning platform for cryptography, using Spatial.io, an immersive and highly interactive virtual environment.

Unlike experimental research, which primarily examines cause-and-effect relationships, DDR is particularly well-suited for this study as it emphasizes practical implementation. It allows researchers to assess the actual usability and effectiveness of CipherSphere within a real classroom setting, rather than in controlled laboratory environments, which often lack ecological validity (McKenney & Reeves, 2018). Additionally, DDR combines both qualitative and quantitative elements, making it a more holistic approach for evaluating the development process and students’ interaction with the platform.

This study follows three key DDR phases. The first phase, Needs Analysis, involves a literature review to identify gaps in cryptography education and justify the necessity of using a metaverse-based learning approach. The second phase, Design and Development, focuses on constructing the CipherSphere learning environment within Spatial.io. This includes creating a flowchart, designing storyboards, integrating multimedia elements (text, images, animations, video, and audio), and structuring lesson plans. All materials are thoroughly reviewed by subject matter experts to ensure both pedagogical and technical effectiveness.

The final phase, Evaluation and Usability, assesses the effectiveness and user experience of CipherSphere. A survey instrument, adapted from Al-Adwan et al. (2023), is utilized to measure students’ perceptions based on the Technology Acceptance Model (TAM), specifically evaluating Perceived Ease of Use (PEU), Perceived Usefulness (PU), and Perceived Enjoyment (PE). Findings from this phase provide valuable insights into the platform’s impact on student learning engagement, enabling further refinements to improve the usability and educational effectiveness of CipherSphere.

By adopting DDR, this study ensures that the design and development process is iterative, evidence-based, and continuously refined to enhance student engagement and learning outcomes. The structured methodology also provides practical insights for integrating metaverse-based learning into the formal education system.

Need Analysis using Literature Review

According to Richey et al. (2003), this phase involved figuring out the needs and analyzing what a project aimed to achieve. The data collection was carried out in this stage through the review of existing research related to the demands of the present study. The literature review was presented based on readings of journal articles and scholarly books. Based on such a literature review, research questions were developed, and the rationale for the metaverse study was assessed in the researcher’s home country of Malaysia.

A literature review included a comprehensive search of the literature using a transparent, reproducible strategy to identify sources, appraise the quality of publications, and summarize the data, minimizing bias (Kraus et al., 2020). Researchers identified gaps in the existing knowledge by carrying out the systematic literature review. This phase involved a critical examination of the literature to pinpoint areas that required further exploration and investigation.

Design and Development

The design and development phase are executed following the DDR approach. As stated in the introduction of this chapter, this phase commences with the construction of a flowchart algorithm. An algorithm is a set of steps carried out in succession to solve a given problem, and in this study, it means designing a flowchart (Ministry of Education Malaysia, 2016). The flowchart is an important document that presents the development process for instructional materials to be developed (Krishnan et al., 2021).

Fig 2. Algorithm CipherShere

After formulating the algorithm, the second step in this phase involves the construction of a metaverse world by creating a storyboard. The storyboard serves as a primary reference that support the content development, integrating multimedia elements that are intended to be incorporated into the educational world being constructed (Krishnan et al., 2021). Therefore, a storyboard will guide how the metaverse world is shaped, and the design of this storyboard is referred to experts before the development process begins.

Fig 3. Storyboard CipherSphere

The instructional content, such as learning videos and notes, is developed by teachers with over 10 years of experience in teaching computer subjects. The development of the Metaverse World follows a structured workflow involving multiple key roles. The process begins with the Instructional Designer submitting the Metaverse World Outline to the Subject Matter Expert for approval. Once approved, the Graphic Designer starts designing the interactive components and submits a draft for evaluation. The Subject Matter Expert then reviews the courseware and provides feedback for necessary refinements. If additional modifications are required, the draft undergoes a final review before receiving formal approval. This approach ensures that instructional integrity, subject matter accuracy, and design quality are well integrated before implementation.

Implementation and Assessment

The final phase in the DDR approach is the Implementation and Assessment phase (Siraj Saedah et al., 2013). In this phase, methods such as surveys are employed. The use of surveys is aimed at testing several variables from the TAM model. This survey instrument for this study was adapted from Al-Adwan (2023). The purpose of the modification is to exclude certain constructs from the questionnaire because the sample for this study is secondary school students, as opposed to the original study which targeted high school students. The constructs measured include Perceived Usefulness (PU), Perceived Ease of Use (PEU), Perceived Enjoyment (PE), and Metaverse Behavioural Intention (BI).

A mini-impact study was performed to evaluate the accessibility of the newly developed metaverse platform. The educational process began on the metaverse platform. Students then engaged with the content in the platform through interactive videos and comprehensive notes. This research took four sessions of 30 minutes each. At the end, students responded to a Live Worksheet, an integrated quiz in the metaverse platform.

Research Model Used For Data Collection And Analysis

The study’s participants were intentionally selected based on their direct involvement in the Computer Science curriculum, which importantly includes the study of cryptography—a key element of the subject matter at this grade level. In this regard, a purposive sampling technique was applied in order to ensure that participants would be capable of providing deep and detailed reflections with regard to the use of the CipherSphere metaverse environment and its integration into their learning process.

Three hypotheses were formulated on which to base the study:

Hypothesis 1: Perceived ease of use will positively influence metaverse behavioral intention, meaning that the easier the platform is to use, the more likely students are to intend to engage with it.

Hypothesis 2: Perceived enjoyment is expected to positively influence metaverse behavioral intention, implying that higher levels of enjoyment are associated with an increased likelihood of using metaverse platforms.

Hypothesis 3: Perceived usefulness is anticipated to positively affect metaverse behavioral intention, suggesting that when students perceive the metaverse platform as more useful, their intention to use it grows stronger.

These hypotheses align with the Technology Acceptance Model (TAM) factors and are visualized in the research model diagram included with the study. The diagram illustrates the expected relationships between the constructs of Perceived Enjoyment (PE), Perceived Usefulness (PU), Perceived Ease of Use (PEU), and Metaverse Behavioural Intention (MBI), providing a clear framework for the investigation.

Fig 4. Research model

Data Analysis

In this study, we examined the relationships between individuals’ perceptions of metaverse technology and their behavioural intentions to engage with these platforms. The analysis involved three independent variables: Perceived Ease of Use, Perceived Enjoyment, and Perceived Usefulness, which we hypothesized to influence the dependent variable, Metaverse Behavioural Intention.

To assess these hypotheses, we employed linear regression techniques on data collected through a survey completed by potential metaverse users. Each respondent rated their perceptions on a scale, reflecting their views on each independent variable. The regression analysis allowed us to estimate the relative influence of each perception on the behavioural intention.

Data were gathered from a sample consisting of 50 respondents, using a validated questionnaire. Results through data analysis were computed using SmartPLS 4 software, which generated regression coefficients, p-values, and t-statistics that have been used to test the significance of the relationships among these variables. These results will help us to understand which factors most greatly influence the decision of an individual to engage in a metaverse environment, thereby helping developers and marketers of metaverse platforms.

RESULT

Demographical Findings

Out of 50 students, 42% (21 students) are male and the remaining 58% are female. Specifically, students who are 15 years old were selected. The devices used are laptops, Chromebooks, and desktop PCs. 24% of students (12 students) use Chromebooks to access the Spatial.io platform. 70% of students (35 students) use laptops, while 6% (3 students) use desktop PCs.

Measurement model

Reliability and validity of the measurement items and constructs were examined before proceeding to the proposed hypotheses (Hair et al., 2019).

Fig 5. PLS-SEM structural model analysis

Table 1. Internal consistency reliability results (construct reliability and validity)

Table 2. Convergent validity result

The table provided is from construct reliability and validity analysis in PLS-SEM (Partial Least Squares Structural Equation Modeling). This analysis is used to assess the quality of the measurement models. Each measure has a specific purpose. Cronbach’s Alpha measures internal consistency, showing how closely related a set of items are within a group. It’s a way to check the reliability of a scale. A value above 0.7 is generally seen as acceptable, meaning that the items are closely enough related to form a consistent scale. Composite Reliability (rho_a and rho_c) also measures internal consistency, but these are considered more reliable than Cronbach’s Alpha, especially in PLS-SEM. Values above 0.7 are once again seen as acceptable.

Average Variance Extracted (AVE) quantifies the proportion of variance captured by a construct in relation to the variance due to measurement error. It checks the validity of a construct, which is how well a test measures what it claims to measure. An AVE above 0.5 means that a construct explains more than half variance of its respective indicators. In the case of a structural model, it would then give the R2 value for endogenous constructs. This gives an idea about the amount of variance within a dependent variable caused by one or more independent variables.

Factor loadings provide an important role in various statistical models, like factor analysis and structural equation modeling, to uncover the relationship between the observed variables and their latent constructs. The closer to 1 the factor loading is, the stronger the variable relates to the underlying factor. The MBI construct demonstrates strong internal consistency and reliability, with Cronbach’s alpha and composite reliability values exceeding the 0.7 threshold. Additionally, the AVE value surpasses the 0.5 benchmark, indicating good construct validity—showing that the construct explains a significant portion of the variance of its indicators.

An R2 value of 0.745 in MBI shows that the independent variables of the model explain a substantial portion of the variance. Additionally, all the factor loadings for MBI items exceed the commonly accepted threshold of 0.7, confirming that each item has a strong relationship with the Metaverse Behavioral Intention construct.

PEU also displays high internal consistency and reliability, with both Cronbach’s alpha and composite reliability values above 0.7. Its AVE is well above 0.5, suggesting that the construct is valid and clearly defined by its indicators. The loadings for the Perceived Ease of Use construct are strong, all above 0.7, which shows that the construct has good convergent validity.

With a high Cronbach’s alpha and composite reliability, Perceived Enjoyment is shown to be reliable. Additionally, its AVE exceeds the recommended level, indicating that the construct accounts for the majority of the variance in its indicators. All loadings for Perceived Enjoyment are above 0.7, demonstrating strong and significant contributions to the construct.

Construct that has high internal consistency and reliability is Perceived Usefulness. Additionally, the AVE exceeds 0.5, indicating good construct validity. The Cronbach’s Alpha of 0.838 demonstrates that the construct is measured with high reliability, meaning the items making up the scale for Perceived Usefulness work effectively to capture the essence of the construct. The level of Cronbach’s Alpha confirms that the scale is a reliable measurement of the concept it aims to assess. Items for Perceived Usefulness have high factor loadings above the 0.7 cutoff and an AVE exceeding the 0.5 threshold, shows that the items are strong representatives of the Perceived Usefulness construct.

The factor loading results show that all items are strong indicators of their constructs, as all are above the acceptable threshold. This indicates that each construct is adequately measured by its indicators, ensuring the robustness of the measurement model.

In conclusion, the analysis of the constructs in the PLS-SEM model shows a solid and reliable model. The high values of Cronbach’s Alpha, Composite Reliability, AVE, and factor loadings for all constructs mean that the survey questions are dependable and accurately reflect what they’re meant to measure.

Hypotesis Testing

Bootstrapping used for hypothesis testing to determine the significance of paths in the model by generating a distribution for each parameter estimate, from which p-values and confidence intervals can be calculated. In this table, the p-values and confidence intervals generated from bootstrapping are used to test the hypotheses concerning the relationships between perceived ease of use, perceived enjoyment, perceived usefulness, and Metaverse behavioral intention.

Table 3. Hypotesis Testing

Hypothesis 1 states that Perceived Ease of Use (PEU) positively affects Metaverse Behavioral Intention (MBI), with the expectation that a more user-friendly platform will enhance students’ intention to engage with it. The path coefficient (β) is 0.388, with a p-value of 0.005, which is less than the conventional alpha level of 0.05, suggesting that the relationship is statistically significant. The confidence interval not crossing zero further supports this conclusion.

Hypothesis 2 suggests that Perceived Enjoyment is expected to have a positive influence on Metaverse Behavioral Intention, proposing that higher levels of enjoyment correlate with an increased likelihood of using Metaverse platforms. But, the β is 0.052, and the p-value is 0.319, which exceeds 0.05, indicating that the relationship is not statistically significant. Moreover, the confidence interval includes zero, further confirming the lack of a significant effect.

Meanwhile, Hypothesis 3 suggests that Perceived Usefulness is anticipated to positively impact Metaverse Behavioral Intention, indicating that when students find the Metaverse platform more useful, their intention to use it increases. The β is 0.481, with a p-value of 0.001, suggesting a very strong statistically significant relationship. The confidence interval being entirely above zero supports the hypothesis that Perceived Usefulness has a positive effect on Metaverse Behavioral Intention.

The study shows that students are more inclined to use Metaverse platforms when they feel the platforms are straightforward to navigate and genuinely helpful in meeting their needs. On the other hand, enjoyment doesn’t seem to have as much of an impact on their intention to use these platforms. This implies that while usability and utility are key factors for student engagement in the Metaverse, enjoyment alone may not be a strong enough factor to drive behavioral intentions.

DISCUSSION

This study aimed to better understand the factors influencing students’ intention to use Metaverse-based learning platforms, using TAM factors. The investigation was constructed around Perceived Enjoyment (PE), Perceived Ease of Use (PEU), and Perceived Usefulness (PU), which were hypothesized to affect students’ Metaverse Behavioral Intention (MBI). According to TAM, PU is a strong predictor of MBI, suggesting that the practical advantages of Metaverse platforms play a crucial role in influencing students’ willingness to utilize them.

The study found that PEU had a strong positive impact on MBI, indicating that a platform’s user-friendliness might greatly improve students’ intentions to engage with it. The results showed that the direct effect of PEU positively influences MBI (β = 0.388, p = 0.005), suggesting that a more user-friendly platform significantly encourages student engagement. This underscores that ease of use is a pivotal factor in influencing behavioral intentions regarding the adoption of Metaverse platforms. Our findings align with Rukhiran et al. (2022), who found that the perceived ease of using mobile learning applications positively influenced users’ intentions. However, they contrast with Al-Adwan (2023), where the direct impact of PEU on behavioral intentions was not significant, but its indirect effects through perceived enjoyment and usefulness were noteworthy. This implies that ease of use may not directly act as a motivator for students but indirectly shapes their behavioral intentions by enhancing perceived enjoyment and usefulness. The differences between these findings might be attributed to variability in context, sample, or specific technologies evaluated, demonstrating the complexity of educational technology adoption.

The analysis of the effect of Perceived Enjoyment on MBI provided surprising reports. While our study did not find a statistically significant relationship between these variables, Al-Adwan et al. (2023) reported a significant positive effect of Perceived Enjoyment on Behavioral Intentions in the context of Metaverse usage. The disparities in our findings may be due to the demographic variation between our sample of 9th-grade students, aged about 15, and the subjects of Al-Adwan’s research, who were mostly university students aged between 20 and 30. This age disparity could account for the variations in findings, particularly in relation to how these different age groups perceive and interact with educational technologies. In educational settings like the Metaverse, our findings might imply that factors other than enjoyment, such as perceived usefulness or ease of use, could be more influential in shaping students’ behavioral intentions. This contrast highlights the importance of tailoring the design and focus of educational technologies to the specific preferences and needs of their intended user base.

In educational settings like the Metaverse, our findings might imply that factors other than enjoyment, such as perceived usefulness or ease of use, could be more influential in shaping students’ behavioral intentions. This contrast highlights the importance of tailoring the design and focus of educational technologies to the specific preferences and needs of their intended user base.

Previous research has shown that asynchronous learning is a stronger construct than synchronous learning, particularly in digital environments that allow self-paced exploration (Norman et al., 2022). This aligns with the nature of Metaverse-based learning, where students can interact with educational content at their own pace without being restricted to scheduled sessions (Ozer & Cetinkaya, 2024). Furthermore, studies indicate that creativity skills are more dominant than productivity skills when utilizing educational technology, as immersive virtual learning encourages problem-solving, innovation, and higher-order thinking (Zhang et al., 2022). Given these insights, the CipherSphere Metaverse environment is designed to support self-directed learning and creative problem-solving in cryptographic education. This suggests that metaverse platforms not only provide an engaging learning space but also promote a flexible and creative learning experience, which is essential for mastering abstract and complex topics such as cryptography.

Examining the influence of PU on MBI, our results closely correspond with the research conducted by Rukhiran et al.(2022), which similarly established a strong positive correlation between PU and BI in the context of a mobile learning English web application. The path coefficient in this study was 0.481, underscoring a robust and statistically significant relationship (p = 0.001). This indicates that the utility perceived in the Metaverse platform significantly drives students’ intentions to engage with it. Our findings contrast with those of Al-Adwan et al., who discovered a less pronounced direct impact of PU on BI in their exploration of university students’ intentions to use Metaverse-based learning platforms (Al-Adwan et al., 2023). The divergence in results between these studies could be attributed to differences in user groups and educational contexts. Al-Adwan et al.’s (2023) research underscores the complex processes of technology adoption in higher education. Our study, along with Rukhiran et al.’s (2022), emphasizes the crucial influence of perceived usefulness on behavioral intentions, particularly in the realm of emerging educational technologies such as the Metaverse. These comparisons validate both the basic principles of the Technology Acceptance Model and the changing dynamics of adopting educational technology in various learning settings.

For educational technology developers and educators, these findings suggest that while focusing on making platforms user-friendly, it’s also important to consider how usability interplays with other factors like enjoyment and usefulness to influence students’ behavioral intentions.

CONCLUSION, IMPLICATION AND FUTURE DISCUSSION

The primary focus of this study, besides developing and constructing a metaverse learning platform, is to assess the utility of the metaverse environment among students. This assessment is based on the Technology Acceptance Model (TAM) factors, encompassing Perceived Ease of Use (PEU), Perceived Usefulness (PU), and Perceived Enjoyment (PE). The use of the Spatial.io platform as a learning tool appears to captivate students’ behavioral intentions to utilize it. Based on the findings and discussion, it has been observed that the direct effect of factors such as Perceived Ease of Use (PEU) and Perceived Usefulness (PU) significantly influences Metaverse Behavioral Intention (MBI), while Perceived Enjoyment (PE) shows results that are not statistically significant.

Based on the comprehensive findings and discussions, it has been observed that key factors like Perceived Ease of Use (PEU) and Perceived Usefulness (PU) have a significant impact on Metaverse Behavioral Intention (MBI) among students. The data indicates that these factors are crucial in shaping students’ intentions to engage with Metaverse platforms. In contrast, Perceived Enjoyment (PE) has been shown to have an inconsequential effect on MBI, as its influence was not statistically significant in this study.

It is essential to acknowledge some limitations of our study. Firstly, the limitation of age and educational level is a significant control. The scope of our results is limited to a specific population, which may restrict their generalizability to other age groups or educational environments. Moreover, the study’s limitation to a specific geographic area raises concerns regarding the generalizability of our findings to student groups in other areas or nations, where cultural and educational circumstances differ greatly. Furthermore, the small sample size, which includes just one school, may not provide an accurate representation of the broader student population. This research primarily used the Spatial.io platform. Hence, our study may not fully cover the numerous intricacies and encounters provided by other Metaverse platforms, each distinguished by their own features and level of user involvement.

Future study should aim to expand the scope by including a wider and more detailed demography, encompassing varied age groups, educational backgrounds, and geographical areas. By enhancing the generalizability of the data, a more knowledge of the influence of Metaverse platforms on educational achievements may be achieved. In addition, future research might investigate several Metaverse platforms to analyze and differentiate their effectiveness, user satisfaction, and capabilities. By integrating qualitative approaches, such as interviews or focus groups, the quantitative data may be enhanced, providing a more intricate insight into students’ experiences and perspectives. The implementation of pre- and post-test methods can significantly strengthen the data findings. By comparing students’ knowledge, attitudes, or skills before and after exposure to the Metaverse platforms, researchers can effectively measure the impact and efficacy of these educational tools. This approach allows for a clear assessment of learning outcomes and behavioral changes, providing concrete evidence of the platforms’ influence on student engagement and learning.

ACKNOWLEDGMENT

This study is supported by the research grant GG-2024-004 from the Faculty of Education, National University of Malaysia.

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APPENDIX

Appendix A. The survey instrument adapted from Al-Adwan (2023)

Appendix B : Lesson Plan