Abstract
The use of artificial intelligence (AI) within a learning environment has been shown to enhance the learning environment, improve its effectiveness, and enrich the entire educational experience. The next generation of intelligent learning environments incorporates the immersion of learners within virtual worlds while still offering the educational affordances and benefits of the online environment as a teaching medium. In this chapter, the current implementation of the virtual learning world (VLW) is presented bringing together a number of previous initiatives that integrated AI within a virtual learning environment (VLE) as well as the employment of a virtual world (VW) as learning environments. The realisation of the first VLW prototype provided numerous insights that provide valuable recommendations and significant conclusions to assist in taking the virtual learning environment to the next level.
TopIntroduction
Universities and other higher educational institutions have been employing the evolving ICT technology in a number of ways before the turn of the century. The Virtual Learning Environment (VLE) is one of numerous endeavors to provide educational content to learners while providing a medium for educators to correspond in some way with their same learners. Basic VLEs allow assessment functionalities as well as course management interfaces that are directly sourced from the institution’s information system. Even though these features seem to satisfy the educational needs that the institution seeks to provide (Beastall & Walker, 2007; Oliver, 2005), a number of educational researchers questioned such a medium and expected a deeper and much more effective learning environment that truly enriched the process that learners deserve (Stiles, 2007; Craig, 2007; Alhogail & Mirza, 2011). The issues documented are not only those related to inadequate implementation (Dublin, 2004), unemployed features (Sharpe, Benfield, & Francis, 2006), cultural issues (Alhogail & Mirza, 2011), and acceptance or adoption concerns by learners (Govindasamy, 2002), but additional ones that are far more deeper rooted in the academic significance of employing such learning environments. The question here is whether or not we are taking full advantage of the digital medium and the affordances it provides to create an intuitive and andragogical-sensitive environment in-line with the ever-evolving digital backdrop that learners instinctively associate themselves with. Such a learning environment not only provides the necessary academic functionality expected from the traditional VLE, but additionally supports and affords features that add value to the educational experience.
In this chapter we will be presenting an intelligent learning environment that is not only virtual and digital, but also social-network-like within a virtual world educational space. Our extensive experience in social-network like learning environments (Montebello, et al., 2018), together with the use and purposing of 3-dimensional virtual worlds within an educational context (Camilleri, de Freitas, Dunwell, & Montebello, 2017), as well as both in combination (Camilleri, Dingli, Mifsud, Montebello, & Seychell, 2012), has led us to harness and apply both technologies in tandem in an effort to add value and optimize the learning environment beyond any VLE expectation. The rest of the chapter is organized as follows. The next section gives a short background on VLEs, followed by a similar background on Virtual Worlds (VWs). Our highly-published social-network like learning environment, called Scholar, is thoroughly covered in the fourth section highlighting the seven e-learning affordances that are excelled within this rich VLE. Finally, we present the merging of both worlds by describing into detail how the VLE meets the VW in our attempt to investigate and develop the next generation of VLEs thereby adding value and assisting in enhancing e-learning effectiveness. We close the chapter with numerous recommendations and conclusions we draw from our experiences in developing this innovative and ground-breaking VLE.
Key Terms in this Chapter
Personal Learning Environment (PLE): A combination of personal academic tools, services and communities that a learner makes use of. Electronic personal learning spaces are traditionally made up of two components, namely, a personal learning network and a personal learning portfolio.
Artificial Intelligence: The use of computer science techniques to develop computer programs in an attempt to simulate human behaviour. These programs perform tasks that usually require a human to do and thereby convey a sense of added value when compared to simple computer tasks.
Avatar: A graphical representation of a human user or the user's own character or persona. Such representation is usually either in the form of a 2-dimensional image as an icon over WWW chats, forums, bulletins, as well as over VLEs, or otherwise in the form of a 3-dimensional figure that wither resembles the real user in some way or even fantasy characters as in games or virtual worlds.
Virtual Learning Environment (VLE): This term broadly encompasses virtual spaces that are used for learning. Such environments can include Learning Management Systems (LMS), Multiuser Virtual Environments (MUVEs), Virtual Worlds (VWs), and Serious Games.
Crowdsourcing: The use of online users to collectively contribute and aggregate information towards a common goal. Initially coined by Jeff Howe and Mark Robinson to describe the way commercial entities outsourced tasks to the crowd over the World Wide Web.
World Wide Web (WWW): The massive knowledge base of information spread over the global network of servers known as the Internet. Different generations of WWW represent the evolution of how this technology has radically changed over a short period of time from a read-only, to a read-write and share.
Learning Technologies: Different media, technology-based applications and tools that can be used to facilitate and support learning. Learning technologies also include the 21st century digital practices that would require a specific set of skills and attitudes.
Customization: The process of tailoring or changing the content, environment, or the surroundings according to the specific needs and preferences of a unique user. Customization of services or products are notoriously of an elevated value as the user is given the feeling of being given special or preferential treatment thereby being more effective and useful (Montebello, 2018a).
Face-2-Face (F2F): A mode of delivery within the education/training arena when the educator and learners meet and interact directly in a physical location, as opposed to alternate virtual interaction that can substitute F2F where educator and learners participate in the educational process over the WWW.
Andragogy: The art and science of teaching adult learners. Usually taken for granted however effective teaching requires specific skills and experience. Educators can employ a plethora of teaching strategies to optimize the use of the learning medium selected.
Virtual Learning World (VLW): A proposed learning space made available online through the integration of an advanced VLE called Scholar within a VW called Second Life in an attempt to enhance the learners’ education process.
Machine Learning (ML): Software algorithms that enable the application of AI techniques as the employ the processing of data to add value and incrementally improve automatically as they learn from the extracted information. The learning process is through the analysis of the masses of data available and identifying patterns while performing decisions based on the algorithm programmed by the AI developer.
Virtual World (VW): Sometimes referred to as a 3-dimensional virtual space, this computer-simulated that is inhabited by avatars that impersonalize real human users. The avatars can interact with each other as well as with objects within the same VW.
Learning Analytics: All the data generated by learners when interacting with a learning environment is saved and processed in a way to be employed as further information is extracted that is specific to a unique learner. Such analytics provide customized information as well as making use of all the data points collected to assess the learner’s performance (Montebello, 2018b).
Social Networks: This term refers to the connections between individuals in a community. Christakis and Fowler (2011) define this as “an organized set of people that consists of two kinds of elements: human beings and the connections between them. Real, everyday social networks evolve organically from the natural tendency of each person to seek out and make many or few friends, to have large or small families, to work in personable or anonymous workplaces” (p. 13).
Web 2.0: O’Reilly (2005) coined this term to demarcate a phase within the evolution of the WWW whereby websites allow user-generated content thus encouraging web user to author, contribute, share, and distribute their own and others material. Social media were a direct result of this particular phase that also has dynamic characteristics in contrast to previous static read-only counterparts.
E-Learning: Is learning on Internet Time, the convergence of learning and networks. e-Learning is a vision of what corporate training can become. E-Learning is to traditional training as eBusiness is to business as usual. Different versions and generation of e-learning exist as technologies evolved over the years.
Intelligent Adaptive Learning (IAL): Intelligent adaptive learning is defined as digital learning that immerses students in modular learning environments where every decision a student makes is captured, considered in the context of sound learning theory, and then used to guide the student’s learning experiences, to adjust the student’s path and pace within and between lessons, and to provide formative and summative data to the student’s teacher. This type of learning tailors instruction to each student’s unique needs, current understandings, and interests, while ensuring that all responses subscribe to sound pedagogy (Dreambox, 2014).
E-Learning Affordances: The additional functionality and capability that the digital has made possible providing a rich learning experience that previously was not possible through traditional teaching. Some of these affordances include ubiquitous learning, active knowledge making, multimodal meaning, recursive feedback, Collaborative Intelligence, metacognition, and differentiated learning (Cope & Kalantzis, 2013).