This research is guided by the following theoretical frameworks: constructivism and experiential learning. Constructivism posits learning as the active construction of knowledge based on the contextual experiences of the learner (Rannikmäe et al., 2020; Bada & Olusegun, 2015; Cooper, 1993). Since learning exists in the human mind, learning experiences do not necessarily need to correspond with real-world realities (Bada & Olusegun, 2015); therefore, invented realities can strongly influence the situated perception of learning (Aiello et al., 2012). Learners attempt to update and reflect on their mental models through the perception of each new experience, contemplating on new stimuli and information to make contextual mental constructions of reality (Bada & Olusegun, 2015). As such, educators practicing a constructivist paradigm play an active role as facilitators, mentors, or coaches (Rannikmäe et al., 2020; Cooper, 1993) in the application of knowledge to enhance learning objectives through realistic activities that mimic real-world counterparts (Huang & Liaw, 2018). Therefore, a constructivist perspective defines the educator’s role and how learning could occur within a virtual environment.
Experiential learning views learning as “the process whereby knowledge is created through the transformation of experience” (Kolb, 2015, p. 49). Being derived from constructivism, experiential learning emphasizes learning within the construction and reflection of knowledge through environmental interactions and perceived experiences (Mughal & Zafar, 2011; Fenwick, 2001). Nevertheless, whereas constructivism tends to focus on social-cultural interactions (Rannikmäe et al., 2020; Mattar, 2018), experiential learning focuses on individualistic constructions of knowledge through reflections (Mughal & Zafar, 2011). Kolb (2015) furthers this argument by viewing learners as autonomous; therefore, social relationships and interactions through language and cultural practices are not part of knowledge construction. The model of experiential learning theory describes learning through a circular model of four opposing modes:
- concrete experiences focus on undergoing and participating in an experience;
- reflective observations conclude and learn from an experience through reflection;
- abstract conceptualizations assimilate conceptual concepts based on reflections; and
- active experimentations test new abstractions, which guide additional experiences (Lehane, 2020; Kolb, 2015; Kolb & Kolb, 2009).
For this reason, when considering virtual environments, experiential learning clarifies the function of the environment as a place of concrete and active experimentation, authentically leading to reflective observations for new conceptualizations.
The concept of a head-mounted display (HMD) for use in virtual reality (VR) first came under consideration in the 1960s (Sutherland, 1965), while the first commercial products appeared in the twilight years of the 1980s (Cipresso et al., 2018). Initial studies of VR focused on the computer science field (Mazuryk & Gervautz, 1996; Sutherland, 1965), though later studies branched out into psychology (Slater, 2018; Slater, 2003; Riva, 1999), education (Fowler, 2015; Dalgarno & Lee, 2010; Hedberg & Alexander, 1994; Winn, 1993), and now VR stems from a rich interdisciplinary community (Cipresso et al., 2018). In order to understand the scope of virtual learning environments (VLEs) in secondary classrooms, I present an overview of key concepts that contribute to a richer understanding of a virtual learning experience. Multiple disciplines will be examined, coalescing into modern models of learning in virtual environments.
A Brief History
The first contemporary immersive VR-based technology was formulated within Sutherland’s (1965) paper and realized through an HMD called the Sword of Damocles (Sutherland, 1968). Sutherland’s Sword of Damocles signified a surge of innovation in VR-related technologies that continued to the early 1990s. During this time, many innovative VR-based technologies were birthed from “the degree of excitement, creativity, speculation, visions of a positive future, [and] belief in the near-term mass availability of VR” (Slater & Sanchez-Vives, 2016, p. 3). One of these technologies was developed by the US Airforce in 1982; it was called the Visually Coupled Airborne Systems Simulator (VCASS) and acted as an advanced flight simulator where fighter pilots wore an HMD to view targeting and flight path information (Mandal, 2013). Likewise, in 1992 Cruz-Neira et al. develop the Cave Automatic Virtual Environment or CAVE system, an alternative immersive VR system that projected images onto multiple walls of an enclosed cube to surround the user in a 3D world much like HMDs (Cipresso et al., 2018; Dalgarno & Lee, 2010) and was referred to as projection-based displays (Feng et al., 2018).
After the initial surge of iVR technologies, the field shifted to other extended forms, such as mixed reality (Cipresso et al., 2018) and desktop-based virtual environments (Ai-Lim Lee et al., 2010; Robertson et al., 1997). As first expressed by Pausch et al. (1996) and still true today, VR is in a paradigm shift as researchers need to develop a new standardized syntax and discover ways of presenting content that takes advantage of the medium’s unique characteristics (Slater & Sanchez-Vives, 2016). VR’s current transitive state is further cemented by Weller’s observation that when “new technolog[ies] arrive, [they] tend to be used in old ways” (2020, p. 64). A more recent second wave of interest in VR technologies has been fueled by the sudden mass availability of lower-cost commercial HMDs and substantial investments from companies like Facebook, Sony, Samsung, HTC, and Google (Cipresso et al., 2018). However, due to this recent abundance, there has been “limited research on [the] effects [that VR has] on children and its application to learning in actual classrooms” (Southgate et al., 2019, p. 20).
Mixed and Virtual Reality
Much like its multidisciplinary roots, the term VR has been associated with a variety of technologies. Pioneering endeavours first concentrated on defining clear boundaries focused on the levels of technological immersion the equipment offered, differentiating the VR system into three categories: (1) non-immersive or desktop VR systems, sometimes referred to as the window on world systems, was a lower-level system that had the user view the virtual world through one or more computer screens; (2) semi-immersive systems or fish tank VR supported head tracking creating a more immersive experience but often lacks sensory inputs; (3) immersive systems made use of an HMD to position and orientate the user as well as making use of other sensory inputs, specifically, sensory-based interfaces and was considered the apex of virtual reality (Cipresso et al., 2018; Mandal, 2013; Mazuryk & Gervautz, 1996; Metzger, 1993).
Milgram and Kishino (1994) argued that the VR label is often used with a variety of environments in which total technological immersion may not take place, creating “parallel problems of inexact terminologies and unclear conceptual boundaries” (p. 2). To help establish these conceptual boundaries, Milgram and Kishino (1994) suggested adopting a new taxonomy called mixed reality. As the name suggests, the term mixed reality involved merging virtual and real worlds and gave rise to the virtuality continuum (see Figure 1). The virtuality continuum helps describe the degree of virtual envelopment a technology affords, with the left of the continuum solely consisting of real objects or objects that have a genuine material existence (Milgram & Kishino, 1994). Progression from the left side to the right of the continuum involves more objects being simulated until all objects are simulated within a virtual environment (Azuma et al., 2001; Milgram & Kishino, 1994).
The Virtuality Continuum
Note. The virtuality continuum describes the degree of virtual envelopment derived from the use of virtual reality-related technologies. From “Recent advances in augmented reality,” by Azuma, R., Baillot, Y., Behringer, R., Feiner, S., Julier, S., & MacIntyre, B., 2001, IEEE Computer Graphics and Applications, 21(6), 34-47. (https://doi.org/10.1109/38.963459).
Contemporary movements have given rise to an extensive array of VR-related technologies. Mixed reality, while encompassing various technologies with differing degrees of virtual envelopment, can be simplified into two taxonomies: augmented reality and augmented virtuality (Fast-Berglund et al., 2018; Azuma et al., 2001; Milgram & Kishino, 1994). Augmented reality combines real-world and virtual objects, running in real-time to seamlessly integrate interactions with the real and virtual spaces, often being used for remote guidance and visualized instructions through phones or other handheld devices (Fast-Berglund et al., 2018). Augmented virtuality builds upon augmented reality by utilizing a higher degree of virtual entanglement as more elements are synthetic (Fast-Berglund et al., 2018). The Microsoft Hololens (Cipresso et al., 2018; Microsoft, n.d.) exemplifies augmented virtuality devices and is commonly confused as the sole mixed reality (Fast-Berglund et al., 2018).
Consequently, virtual reality would be situated outside the mixed reality taxonomy because as users immerse themselves in the environment, their perception of the physical world is replaced with an artificially simulated one (Feng et al., 2018). This notion is supported by Fast-Berglund et al.’s (2018) adapted model (see Figure 2), which builds upon Milgram & Kishino’s (1994) and Doil et al.’s (2003) work to express the relationship between the diverging technologies as they transform from real to virtual environments. The term extended reality (XR) was adopted to refer “to all real-and-virtual combined environments and human-machine interactions generated by computer technolog[ies] and wearables” (Fast-Berglund et al., 2018, p. 32).
Extended Reality Systems
Note. This figure represents the extended reality technologies based on the degree of virtualization, with physical or real systems on the left and virtual systems on the right; likewise, VR is placed outside mixed reality. From “Testing and validating extended reality (xR) technologies in manufacturing,” by Fast-Berglund, Å., Gong, L., & Li, D., 2018, Procedia Manufacturing, 25, 31–38. (http://dx.doi.org/10.1016/j.promfg.2018.06.054).
Presence and Immersion
The concept of immersion is the strongest argument for the advocates of VR in education (Makransky & Petersen, 2021; Dalgarno & Lee, 2010; Whitelock et al., 1996; Hedberg & Alexander, 1994; Winn, 1993). However, despite its advocacy, the concept of immersion has a degree of ambiguity attached to it. Generally, two diverging opinions have formed. Slater & Wilbur (1997) argued for immersion to be viewed as a technological quality, whereas Witmer & Singer (1998) viewed immersion as the psychological phenomenon subjective to the individual’s beliefs (Radianti et al., 2020). Furthermore, some early writings used immersion and presence interchangeably (Dalgarno & Lee, 2010).
Accordingly, Slater (2018, 2009, 2003, 1999) argued for the separation of the term presence from immersion. He viewed presence as the subjective perception of being there brought on by the objective manipulation of the sensorimotor modalities through an immersive system. Strictly speaking, immersion would be a measure of the technology’s capabilities to present an immersive world; likewise, presence would be the psychological reaction to the virtual environment or the qualia associated with the illusion (Dalgarno & Lee, 2010; Slater, 2009; Slater, 2003). However, this does not mean presence is the cognitive suspension of disbelief, but an illusion where the environment looks and behaves within the expectations of the user’s perceptions (Slater, 2009). “It is a perceptual but not a cognitive illusion” (Slater, 2018, p. 432).
This illusion can be further broken down into two subsects: place and plausibility illusions (Slater, 2009). A place illusion is a type of presence enabled within the sensorimotor systems as it is fed varying degrees of stimuli (Slater, 2018; Slater, 2009), “it is the strong illusion of being in a place in spite of the sure knowledge that you are not there” (Slater, 2009, p. 3551). Likewise, plausibility illusion reinforces place illusion through the realistic and expected interactions within the environment even if the user is not the direct driver of such events (Skarbez et al., 2017; Slater, 2009); it “is the illusion that what is apparently happening is really happening (even though you know for sure that it is not)” (Slater, 2009, p. 3553).
Immersive Virtual Environments
One of the first conceptual frameworks developed to understand how virtual environments could contribute to learning outcomes was developed in the early 1990s. Winn’s (1993) work argued that VR could facilitate constructivist first-person non-symbolic experiences intentionally designed to achieve a learning objective (Mikropoulos & Natsis, 2011). First-person knowledge results from the direct interaction with the environment without conscious thought, resulting in knowledge that is “direct, personal, subjective and often tacit in the sense that we often do not know that we know something” (Winn, 1993, para. 8). Likewise, third-person experiences result from the vicarious knowledge filtered through a symbolic, explicit representation of the event; and it is often the means of knowledge acquisition used in schools today (Winn, 1993).
Furthermore, Winn (1993) postulated that immersive virtual environments would permit learning experiences that could not be obtained by traditional educational means through three characteristics: (1) size, virtual environments are not held to the restrictions of the real-world as such notions of size could be inflated or deflated to extremes to reveal new learning objectives; (2) transduction, through the use of hardware, human senses can be altered to experience stimuli outside the domain of normal function; (3) reification, a process of design and teaching in virtual environments that focuses on the participation of the learner through size and transduction rather than strictly simulating real-world means. Today, many researchers have utilized these characteristics to explore abstract concepts in first-person scenarios with varying degrees of success (Zinchenko et al., 2020; Marks et al., 2017; Bertram et al., 2015; Youngblut, 1998).
The notion of immersive virtual environments infers a relationship between the virtual environment, technology, and user. To Hedberg and Alexander (1994), much like Winn’s (1993) rationale of first-person experiences, the fundamental feature of immersive virtual environments was an intuitive and naturally transparent interface with which users could directly control virtual objects within the context of the virtual environment. This view was foundationally held through situated learning. Situated learning is the perception that learning and knowledge are context-specific and for learning activities to be authentic, they must be situated and framed in the culture, environment, and activity (Chang et al., 2013; Dunleavy et al., 2009; Hedberg & Alexander, 1994). Such a perspective tends to agree with more modern views of embodied learning. Embodied learning, often referred to as embodied cognition, suggests meaning is not solely contrived from a dualistic representation of the physical and mental characteristics of an action; alternatively, it proposes that meaning is made through the embodiment of the relationship between physical and mental perceptions, which are instantiated and shaped by the environment (Shapiro & Stolz, 2019; Stolz, 2015).
According to Hedberg and Alexander (1994), learning could be achieved in immersive virtual environments through three attributes: active participation, the fidelity of representation, and immersion. Whitelock et al. (1996), building upon Zeltzer’s (1992) work, described three similar attributes: representational familiarity, immediacy of control, and presence. Hedberg and Alexander’s notion of immersion is closely related to Whitelock et al.’s conceptualization of presence (Dalgarno & Lee, 2010), which, in this context, not only refers to the technological state of immersion and the psychological nature of presence (Slater, 2018; Slater, 2003) but also the motivational drive enabled through the contextual representation of the task within the environment (Hedberg & Alexander, 1994). Furthermore, active participation and immediacy of control explored how the user could interact with the environment through naturalistic interfaces rather than traditional computer-based command structures (Whitelock et al., 1996; Hedberg & Alexander, 1994). Finally, Hedberg and Alexander’s (1994) fidelity of representation and Whitelock et al.’s (1996) representational familiarity both examined the ability of the technology to recreate an environment that would be faithful to the user’s expectations.
However, virtual environments are more than a series of systems enabled through hardware. A pivotal viewpoint of learning in virtual environments would be the mental exercise enabled through the interactions within the environment that transforms the learner from a mere observer to a living actor of that world (Riva, 2005; Riva, 1999). These mental exercises are deeply connected to the sensorimotor system, allowing bodily interactions in the environment that stimulate conceptual understanding and rational thought (Lindgren et al., 2016; Riva, 1999). To Riva (1999), a functional virtual environment needs the learner to recognize its limitations and expectations; likewise, the environment needs to be designed to “incorporate some information about what the [learner] ‘s goals and behaviours are likely to be” (Riva, 1999, p. 89). As such, the purpose of a virtual environment would be the immersion of the sensorimotor channels through a naturalistic interface which can be achieved through the following design principles: (1) designing a space for bodily action to stabilize the environment based on the learner’s expectations, (2) design of other intelligent beings to facilitate first-person interactions; and (3) design of the represented body to create a sense of embodiment and ground the user’s interactions within the environment (Riva, 1999).
Dalgarno and Lee (2010) expanded and coalesced the previous literature to conceptualize a virtual learning environment through the technological and psycho-social characteristics unique to learning in virtual environments. They divided the distinguishing technological characteristics into two broad categories: representational fidelity and learner interaction (see Table 1). Representational fidelity regarded how the user perceives and interacts with the technology, primarily through their sensorimotor systems; likewise, learner interaction considered how the technology enabled embodied, first-person actions that fit within the boundaries of the learner’s expectations (Dalgarno & Lee, 2010).
Distinguishing Characteristics of Immersive Virtual Learning Environments
Note. This table lists the unique technological characteristics of an immersive virtual learning environment. From “What are the learning affordances of 3-D virtual environments?,” by Dalgarno, B., & Lee, M. J. W., 2010, British Journal of Educational Technology, 41(1), 10–32. (https://doi.org/10.1111/j.1467-8535.2009.01038.x).
Furthermore, Dalgarno and Lee (2010) viewed the technological characteristics of representational fidelity and learner interaction as catalysts for psycho-social characteristics that describe a learning experience characterized as construction of identity, sense of presence, and co-presence. Construction of identity utilizes Riva’s (1999) work to describe how a virtual identity could be constructed through visual representations and the embodiment of actions (Dalgarno & Lee, 2010). Further, sense of presence, correlating with place and plausibility illusion (Slater, 2009), would be described by the psychological state of being there, and co-presence can be represented as the state of being there together (Dalgarno & Lee, 2010). In this perspective, the technological characteristics stimulate the psycho-social illusions of presence, leading to learning affordances or benefits unique to virtual reality (Dalgarno & Lee, 2010).
Models for Learning in Virtual Reality
Pedagogically speaking, “design and development efforts in this field are largely hit-and-miss, driven by intuition and ‘common-sense’ extrapolations rather than being solidly underpinned by research-informed models and frameworks” (Dalgarno & Lee, 2010, p. 25). As such, Dalgarno and Lee (2010) identified five affordances based on the aforementioned learning benefits unique to virtual environments that they hoped would lead to more informed models:
- spatial knowledge representation refers to the virtual learning environments’ ability to manipulate and position objects to facilitate learning tasks;
- experiential learning represents the unique ability of virtual environments to enable learning tasks that would be impossible or impractical in the real world;
- engagement describes the phenomenon in virtual environments that can cause higher levels of learner engagement because learning tasks are personalized through first-person experiences;
- contextual learning illustrates that realistic activities in virtual environments should transfer into improved knowledge and skills because learning would be contextualized; and
- collaborative learning represents the potential for higher levels of collaborative activities compared to 2D alternatives.
Using these five learning affordances and building upon the unique characteristics of virtual environments, Dalgarno and Lee created a learning model specific to virtual learning environments (see Figure 3).
Model for Learning in Virtual Environments
Note. This figure represents a learning model for virtual environments that uses virtual reality’s technological and psychological characteristics to derive possible learning benefits. From “What are the learning affordances of 3-D virtual environments?,” by Dalgarno, B., & Lee, M. J. W., 2010, British Journal of Educational Technology, 41(1), 10–32. (https://doi.org/10.1111/j.1467-8535.2009.01038.x).
Fowler (2015) argued that Dalgarno and Lee (2010) concentrated excessively on the technological perspective, specifically when deriving the learning affordances inherent in virtual learning environments. He posits that many technological characteristics are not relevant to specific learning contexts and advised that it must incorporate the pedagogical requirements for a learning model to describe a learning experience. As such, “the practitioner must design a specific learning experience [to best meet] the pedagogical needs of the learner” (Fowler, 2015, p. 415).
Like previous authors, Fowler (2015) cited immersion as a vital characteristic when teaching in virtual learning environments, stating that immersion can bridge the technological, psychological, and pedagogical states of a learning experience. Furthermore, Fowler (2015) suggested that any learning model should begin with the intended learning outcomes rather than viewing the technological characteristics in isolation. As such, he proposed the union of Dalgarno and Lee’s (2010) model of learning in virtual environments with a pedagogical framework to determine the learning requirements and task affordances filtered through a design for learning methodology (see Figure 4). Designing for learning is a pedagogical approach to instruction that involves taking the “general contextual description of the teaching and learning environment through a set of… requirements based on defining what stage the learner is at and what learning outcomes have to be achieved by undertaking a given set of learning activities” (Fowler, 2015, p. 419). The results of these endeavours would be a learning specification that can manifest in a variety of forms; however, regardless of the form, the learning must “explicitly incorporate pedagogical considerations into their specification” (Fowler, 2015, p. 420).
Enhanced model of Learning in Virtual Learning Environments
Note. This figure represents an enhanced model of learning in virtual environments that incorporates the pedagogical requirements. From “Virtual reality and learning: Where is the pedagogy?,” by Fowler, C, 2015, British Journal of Educational Technology, 46(2), 412–422. (https://doi.org/10.1111/bjet.12135).
Recently, Maskransky and Gustav (2021) suggested the cognitive affective model of immersive learning (CAMIL). The CAMIL model follows a similar theoretical methodology to Dalgarno and Lee’s (2010) model as it focuses on the technological affordances of the technology to derive possible psychological characteristics and cognitive benefits (Maskransky & Gustav, 2021). The goal of the model was to provide a better understanding of learning in virtual environments by facilitating how specific technological characteristics and affordances interact with different instructional methods (Maskransky & Gustav, 2021).
Virtual reality has a long, rich history that spans multiple disciplines. Despite this history, it is still in a state of flux, where terminology, methods, and theories are still contentious today. Nevertheless, as shown by Maskransky and Gustav (2021), there seems to be a growing consensus on the driving factors behind technological immersion in virtual environments. For this reason, this thesis prescribes to Dalgarno and Lee’s (2010) view of a virtual learning environment, in which the technological characteristics drive the psychological means of presence. Even so, there has been limited research on presence, specifically in the educational domain (Freina & Ott, 2015; Mikropoulos & Natsis, 2011), where presence seems to play a vital role in the learning experience (Maskransky & Gustav, 2021; Fowler, 2015; Dalgarno and Lee, 2010). As Dalgarno and Lee (2010) stated, “much of what has been published about educational uses of [VR] technologies is largely ‘show-and-tell,’ presenting only anecdotal evidence or personal impressions that cannot be usefully generalized beyond the local context” (p. 23). The conceptual generalization of presence and its possible applications in learning requires the suspension of technocentric views as they risk the emulation of current practices rather than innovative pedagogically composed methodologies (Fowler, 2015).
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