Immersive Schooling
Welcome.
Welcome to this new imagining of Immersive Schooling. Inspired by a forthcoming renaissance in Virtual Reality (VR), this chapter seeks to unpack an investigation of Immersive VR Multi-User Virtual Environments (MUVEs) as a potential platform for schooling. Figures 1 & 2 show how VR/MUVEs look in a classroom environment.
Fig 1. Classroom use of vr. Source: https://sciencenode.org/feature/virtual-reality-goes-mainstream.php
Fig. 2 Visual from a multi-user virtual environment. Source: http://www.ercim.eu/publication/ercim_news/enw31/fahlen2.html
Context and outline of current deficiencies.
Schooling 1.0
It can be said that school has changed little in the last 100 years. Many educators are attempting to describe the 21st Century Learner (P21) in the context of existing (and therefore largely historical) educational systems and paradigms (such as represented in Figure 3). From this perspective, Mitra (2013) states that “Schools as we know them are obsolete”, whilst Brynjolfsson & McAfee suggest that “Compared to other industries such as media, retailing, finance or manufacturing, education is a tremendous laggard in the use of technology”. With this in mind, and according to an OECD report (2010) “schools [are] now beginning to focus on how best to re-structure the school to fit with the demands and needs of the technology use. The solutions for change tend to take one of two forms – either the complete replacement of the school through Web 2.0 technologies and practices, or else the reinvention of the school through the use of Web 2.0 tools and practices.”
Fig. 3. Image of a traditional classroom. Source: http://www.1900s.org.uk/images-1937-raked-classroom.htm
An imperative for change.
There may be several drivers of this change but at its core are students that, according to Konstantinidis et al (2014, p. 111), “have limited patience with the current formal, structured education system. They think, play and learn in environments that are fast-paced, multimedia, multimodal, interactive and, of course, digital with expectations of engagement and high production values at all times”. Whilst the OECD report conceives two forms of change (replacement or reinvention), others have speculated that schools may be disrupted somewhere on a spectrum, as has happened in other technologically disrupted industries. Schuck & Aubusson (2010, p. 300), suggest there will be “a continuum of potential alternatives ranging from cooperative learning networks to a competitive, consumer-driven market system”. This fragmentation and choice may well permit niche systems and environments, that include the virtual immersive school.
A vision for virtual immersive schooling.
This chapter sets out a vision of immersive schooling (represented in figure 4), that leverages a new opportunity for learning at the nexus of four platforms: 1) consumer level 3D Virtual Reality, 2) Multi-User Virtual Environments, 3) the Next Generation of Learning Management Systems (or Digital Learning Environments), and 4) Distributed & Mobile education.
Fig. 4. Model of Immersive Schooling.
Underpinning these four platforms are many issues, some which will be considered briefly in this chapter.
This vision of immersive learning is central to this chapter, and is illustrated in this video, figure 5: Learning in Second Life.
Fig. 5: Learning in Second Life. Source: https://www.youtube.com/watch?v=z8je9YZs-ew
Unpacking the four platforms.
1) Consumer level Immersive Virtual Reality.
VR can be experienced in 2D and 3D platforms, either by immersive technologies (such as a Head Mounted Display, or HMD), or through traditional displays such as a computer screen or television.
In the second half of 2016, Sony, Oculus Rift, Samsung Gear, and HTC Vive will release VR HMDs (among others, as per figure 7). This consumer grade hardware is set to underpin a VR renaissance. They consist of using a HMD to view a (computer software generated) fully immersive 3D simulated world (see example in figure 6). “These displays often include motion tracking” so “as you turn your head from side to side or up and down, the simulated world tracks your head’s movement” (Nelson & Erlandson, 2012, p. 4). The processing and pricing trajectory (as posited in Moore’s Law), has brought these, once very expensive devices, into the budgets of everyday consumers, as explained by Herold (2014, p. 11) “At roughly $350 per headset, the [Oculus] Rift – and its emerging competitors – will finally make virtual–reality devices available at a price that schools and families can afford”.
Fig. 6: Oculus rift hmd being used in school by a young student. https://www.dnainfo.com/chicago/20150714/lincoln-square/bit-space-gives-kids-place-tinker-21st-century-style
The impact of consumer level VR is being hotly anticipated, Foster-Dimino (n.d., p. 16) quotes “Mark Zuckerberg, CEO of Facebook, also thinks Virtual Reality will change the world. He has predicted that it is ‘the next major computing and communication platform’”. Already pre-mass consumer VR has been exploited in “areas including entertainment and urban planning. It has been extensively used within manufacturing industries and military bodies” Monahan et al (2008, p. 1340) citing Burdea & Coiffet (2003).
VR in education will bring benefits around access to learning objects as “it allows students to visualize abstract concepts to observe events at atomic or planetary scales, and to visit environments and interact with events that distance, time or safety factors make unavailable” as per Abdelaziz et al (2014, p. 320), as well as mitigating risks associated with learning, for example in “medical training in that it enables students to practice their clinical skills without exposing patients to any risk” as per HsiuMei & ShuSheng, (2011, p. 301). VR may also assist by leveraging its immersive properties (a particularly useful attribute in learning social sciences), as stated by Mikropoulos & Natsis (2011, p. 777) as “Immersion, another key characteristic of VR systems appears as a result of the involvement of more than one perceptual channel such as visual, auditory, haptic and olfactory” and that “immersion can enhance education in at least three ways: by allowing multiple perspectives, situating learning, and transfer”.
With such a range of HMDs coming to market, there is an inherent risk around choice for users. Until a commoditisation of HMDs occurs, buyers will need to select carefully based on compatibility with other hardware and software, and associated political and moral choices, as illustrated by Herold (2014, p. 11). “in the case of the Rift, its commercial heritage (i.e. owned by Facebook), as Markus Person, the creator and owner of Minecraft stated ‘I definitely want to be part of VR, but I will not work with Facebook’, he wrote. ‘There’s nothing about their history that makes me trust them”.
Fig 7. VR HMD’s by release timeline. Source: http://www.kzero.co.uk.
2. Multi User Virtual Environments (MUVEs).
MUVEs, described by Nelson & Erlandson (2012, p. 10), are “2D or 3D virtual worlds in which learners control avatars that represent their online persons”, in which players “explore the worlds, interact with objects, communicate with other users and complete quests”.
The global use and relevance of MUVEs is rapidly increasing, according to Wexler & Roff-Wexler (2013, p. 1). “With a billion accounts currently registered (KZero, 2011) and increasing in virtual worlds humanity may be at the verge of another evolutionary leap, with virtual world technologies providing the next environmental platform we must adapt to”. This notion of adaption is significant, as explained by Juznik (2010, p. 10) “For now, the virtual worlds can be perplexing and intimidating. It is a lot like the WorldWideWeb in 1993-94: clunky and slow, but we could all see the potential” but “dismissing the importance of web 3D technologies that millions are already using in learning strategy would be like dismissing the internet in 1994”.
MUVEs can be categorised in terms of their level of immersion. Currently, more popular desktop based MUVEs use standard computer or television screens and audio to present the environment, whereas immersive MUVEs may use VR HMD’s, multi-sensor input and haptic feedback devices.
Of the desktop categories, the most well-known is perhaps Second Life (SL) from Linden Labs. Other MUVEs that have been developed specifically with education in mind are (as identified by Merchant et al, 2014, p. 30):
- “River City is an interactive computer simulation to learn scientific inquiry and 21st century skills”
- “VfrogTM in which students dissect a virtual frog”
- “DimensionMTM, A 3D mathematics video-game.
- “Mr VetroTM is a commonly used simulation of several medical scenarios”
Further examples are from Nelson & Erlandson (2012, p. 10-13):
- Whyville: “casual games, some of which focus on science education”
- Quest Atlantis: “solve problems plaguing the virtual world.”
- SAVE Science: “testing new methods for assessing learning”.
Whilst still in niche use, their popularity belies some of the inherent educational advantages that they bring. Trahan et al (2010, p. 67) lists the typical functionality of MUVEs, that users “can share information through slides, audio, video, discussions, presentations, group projects, simulations and explorations” expanded upon by Kuznik (2010 p. 6) that “Classes in virtual worlds offer opportunities for visualization, simulation, enhanced social networks, and shared learning experiences”, and that “In virtual worlds, we can leverage a mix of content and activity to support all learners: auditory, visual and kinaesthetic”. Beyond functionality Huang et al (2010, p. 106) reports that “MUVE’s provide opportunities to use simulation in a safe environment to enhance experiential learning” and (p. 1179), citing Chittaro & Ranon (2007) that they “allow learners to acquire knowledge with less of a cognitive effort than that of traditional learning process”.
There is still some debate on the efficacy of collaboration within MUVEs. Greiner et al (2014, p. 381), found that “Social interactions in Second Life are already more cooperative than in the real world”; Trahan et al (2010, p. 66) found “an increase in the depth of discussions, as well as greater prompted and unprompted contributions” and that “higher cooperativeness in the virtual world lowers the need for additional communication between avatars to achieve efficient outcomes”. These findings are, however, contrary to Konstantinidis (2010, p. 106) who found that “more than 40% of the students agree that it was harder to collaborate through SL in comparison to the traditional method”.
Other hazards that MUVEs have found to bring to the classroom are around Student-Teacher identity (Barrett & Gelfren, 2010, p. 45), Self-Identity (Wexler & Roff-Wexler, 2013, p. 4-5) and negative pathologies (Cheng, 2014, p. 107). Besides these psychological hazards, Jones & Warren (2011, p. 6-9) also identify a raft of other practical issues that educators need to overcome in operating a MUVE such as; Access (where the platform is a shared space, where uninvited guests can join the classroom); Lack of evidence of efficacy (i.e. insufficient case studies relating to specific fields of studies); Bandwidth & Technology access (i.e. network or internet capacity, and access to computing devices); and Time (i.e. delays caused by launching the application, or updating the software).
Another notable risk, is that private MUVE providers can revoke service without notice, as was found when Linden Labs somewhat notoriously closed the SL teen grid and withdrew educational discounts. This particular issue can be mitigated by implementing an open-source platform such as OpenSim, which would also assist control of access, as identified by Konstantinidis (2010, p. 107), an advantage of OpenSim is that it can run in either grid mode (to connect with other users) or standalone mode (a helpful option for schools). Another advantaged of OpenSim is its potential to work natively with the Oculus Rift HMD, offering an out of the box immersive environment.
3. Next Generation Learning Management Systems (NGLMS)/Digital Learning Environments (NGDLE).
According to Keesey & Smith-Robbins (2010, p. 39) MUVEs such as SL are “not strong with document collaboration and large quantities of text. However, Second Life, when teamed with your LMS can be quite effective”. It may be that as MUVEs impact our teaching methods to negate the need for traditional notions of an LMS, otherwise some accommodation may be needed to combine the two, which may be quite plausible given the Next Generation LMS’s (or Next Generation Digital Learning Environment), seek to expand the functionality of our existing LMS’s (such as Moodle and Blackboard) through new levels of functionality including increased interoperability, Brown et al (2015, p. 4).
4. Distributed and Mobile Technology.
There can be no denying that today’s Mobile devices are yesterday’s super computers. As mobile computing increases in ubiquity, students have the potential to learn anywhere, and anytime, and whilst it still may be desirable, it may not be necessary to attend a physically located school. The chart below (figure 8) illustrates the evolution and increasing speed of mobile date. Figure 9 shows the mobile data traffic growth of the past 5 years. There is no reason to suspect that either of these trends will change.
Fig. 8: Growth in data use. Source: http://dazeinfo.com/2016/01/07/global-mobile-data-traffic-2016-2021-4g-lte-5g-apac-us/
Fig. 9: Growth in bandwidth (data speed). Source: https://ytd2525.wordpress.com/2012/08/03/hspa-vs-lte-which-one-is-better/
The trend in mobile computing power shows similar growth. The chart below (figure 10) shows the geekbench score of the iPhone range of the last 8 years. It shows an almost doubling of processing power every year. As the first generation of Immersive VR MUVEs are likely to require a geekbench benchmark of about 10,000, we can extrapolate the growth to show that mobile VR/MUVE use may be possible in about four years.
Fig. 10: Growth in device performance (as rated by Geekbench). Source: http://blog.codinghorror.com/the-pc-is-over/
Exploring further issues.
There are many issues that impact upon the four platforms of this chapter, in this section I will briefly explore learning theories, pedagogy and intelligent agents.
Learning Theories.
According to Konstantinidis et al (2010, p. 102) collaborative learning is mostly related to the principles of Vygotsky’s social constructivism, a process inherent in the social heritage of MUVE. However, what Vygotsky could not have anticipated was the rise of human machine interactivity in the form of Web2.0 technologies and other computing platforms such as MUVEs. This has resulted in the need for new learning theories. Konstantinidis et al (2010, p. 104) attributes Connectivism as one such promising theory. It is learning which “can reside outside of ourselves (e.g. within an organization or a database), is focussed on connecting specialized information sets, and the connections that enable us to learn are more important than our current state of knowing”.
Pedagogical approaches.
Whilst Barrett & Gelfren (2010, p. 265) note that “Studies have shown that a virtual 3D space can be more conducive to learning real-world capabilities than other more conventional teaching methods”, according to Fowler (2015, p. 412) “very few of the studies reviewed had a clear theoretical (pedagogical) model to inform the use and design”. Keskitalo (2011, p. 135) observes that learning as a social phenomenon “is especially important for simulation settings, where students interact and construct knowledge with each other and with teachers, in an environment, and interact with various technical devices”.
Intelligent Agents.
An extension of better known virtual assistants (e.g. Apple’s Siri and Microsoft’s Cortana) are in-world learning assistants, also known as intelligent or pedagogical agents. These may well become a force majeure in future learning environments. Feituri & Funghi (2010, p. 324-327) suggests that these agents can accelerate the learning process, “engage human learners in natural instructional dialogues”, “constantly evaluate learners’ level of comprehension and adapt lessons”, “encouraging students to interact by asking questions”, and “revise and update new contents and features”. They provide examples of these including: Steve, “an application to train navel staff on ships” (2010, p. 329), Adele, an agent “used to train general practitioners and dentists specializing in curing the elderly”. (2010, p. 331), Arianna, which can pose scaffolding questions (2010, p. 333), and Clara, which can provide “in-depth information, assessment, case studies, role playing and technical support” (2010, p. 335).
In a recent news article ‘Imagine Discovering that your Teaching Assistant is Really a Robot’, Korn (2016) reports on the reaction of several university students when they discovered that a teaching assistant named Jill, that had been posing questions and sending reminders through the semester, was in fact an Intelligent Agent. As natural language programming improves, we can expect to see greater automation of this function, combined with an Avatar in a MUVE, students may come to expect a permanent state of direct instruction.
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Part 1: Motivation
Emerging readings, research, environments & change factors that require or validate a move into game-based learning.
Master of Education (Knowledge Networks and Digital Innovation) developed by the
School of Information Studies, Charles Sturt University, 2016.
