2 The Educational Potential of CyberParks
Cyberpark is smart physical learning environments exploiting the affordances of digital, context-aware and adaptive devices that promote better and faster learning through ubiquitous digital connectivity (Isaksson et al.
2017; Klichowski
2017). This enables learners to connect in context-aware scenarios to a wider network of knowledge, experts and learning communities via their adaptive devices. For Hwang (
2014) a smart learning environment, besides enabling learners to access digital resources and interact with learning systems in any place and at any time, actively provides the necessary learning guidance, supportive tools or learning suggestions. Buchem and Perez-Sanagustin (
2013) propose four modes of learning (seamless learning, crowd learning, geo-learning and citizen inquiry) that emerge in such contexts manifesting users’ interaction with the natural, historical, cultural, architectural and digital dimensions of the space. For Sharples et al. (
2012) seamless learning is evident when a person experiences a continuity of learning across a combination of locations, times, technologies and social settings. Gros (
2016) characterises learning in technology-enhanced environments (like in a cyberpark) as fundamentally personal, social, distributed, ubiquitous, flexible, dynamic and complex in nature. She states that one of the most important features of smart learning is that the data used serves as feedback for the learner to support personalised learning.
Hybrid environments like a cyberpark may trigger deep learning that change an individual’s competence profile and epistemological conceptions. Interactivity extends the zone of possibilities providing new focussed learning instances (Cook et al.
2015). Buchem and Perez-Sanagustin (
2013) contend that, when mediated through mobile technologies and locative media, the surrounding physical and the digital environment can be dynamically merged into augmented, ad-hoc personal learning environments. By interacting with these hybrid environments learners develop 21
st century skills including efficient and effective access of information and knowledge, inquiry/problem-solving, creative, collaborative and communicative competences, and the ability to be innovative in using the surrounding habitat in culturally sensitive, globally aware and ethically responsible ways. Through networked technologies citizen-learners develop new interactional patterns with the various aspects of hybridity.
Cyberparks can challenge people to extend their learning boundaries through acquisition of new knowledge and skills, by sharing their understanding and by contributing to the distributed knowledge and networked experience (Klichowski
2017). The more citizens learn about technology and learn through technology the more empowered they become to interact with the surrounding environment. The situation is complex as it merges different epistemologies within one learning instance or calls on relevant epistemologies for different instances of learning. Consequently, different theories of learning serve as conceptual lenses through which interactions cyberpark can be analysed to identify the underlying learning principles and conditions.
4 Toward a Pedagogical Model for CyberParks
Bonanno (
2011;
2014) uses a process-oriented model based on dimensions and levels of interactions for designing ubiquitous learning and learning within social networks. The dimensions of interactions are subject-content, technology, data networks and community. For learning in Cyberparks, this model can be extended by including the physical environment as another dimension.
Considering knowledge as distributed across networks of connections, Wang et al. (
2014) developed a pedagogical model based on the characteristics and principles of interaction in complex connectivist learning contexts identifying three categories of connectivist learning activities: (1) personal knowledge acquisition from networked distributed knowledge, (2) social networked learning by building communities that form a network for knowledge sharing and connection, and (3) complex connectivist learning where learners prompt connection building and network formulation by contributing to distributed knowledge, to decision-making related to complex problems, and to the development of technological and pedagogical innovations.
Researchers about online learning identified dimensions of interactions according to technological affordances including student-teacher, student-student, student-content, student-interface, teacher-teacher, teacher-content and content-content interactions (Moore
1989; Hillman et al.
1994; Anderson and Garrison
1998). Through their social constructivist orientation, Web 2.0 and social technologies promote various forms of interpersonal interaction comprising group-content, group-group, learner-group, and teacher-group (Dron
2007), as well learner-content, learner-technology and learner-community (Bonanno
2011;
2014). Connectivist pedagogy, with its emphasis on the development and nurturing of networks as a major component of learning, extended the interaction possibilities to include groups, sets and networks (Dron and Anderson
2014. Besides dimensions researchers considered also levels of interaction such as learner-self, learner-resource (human and nonhuman) and a meta level learner-instruction interaction which guides the previous two types (Hirumi
2002; operation interaction, information interaction, and concept interaction, from simple to complex and concrete to abstract (Chen
2004; learner-content, learner-interface, learner-support, learner-learner, and learner-context (Ally
2004); pedagogical levels of acquisition, participation and contribution in relation to novice, experienced and expert competence levels in the domain, technology and community dimensions (Bonanno
2011;
2014).
Building on this literature about dimensions and levels of interaction researchers developed pedagogical models to facilitate the design, assessment and evaluation of learning in technology-enhanced contexts. Chen (
2004) developed the hierarchical model for instructional interaction (HMII) in a distance-learning context, based on Laurillard’s conversation framework. According to this model learners that shift from concrete to abstract and from low to high levels manifest three levels of interactions. The most concrete level, on which the other levels depend, is operation interaction, in which the learner operates different media and is interacting with the media interface.
The second level is information interaction, which includes learner-teacher, learner-learner, and learner-content interactions. In connectivist, networked environments characterised by fluid, complex and emerging knowledge, learners have to orientate themselves to filter, integrate, and extract information so as to make it coherent and understandable. Siemens (
2011) proposed two means of information interaction and orientation in such complex online learning environments: wayfinding (orienting oneself spatially through the use of symbols, landmarks and environmental cues) and sensemaking (responding to uncertainty, complex topics or in changing settings).
The third level of HMII concept interaction is the most abstract and includes intra-individual cognitive and affective interactions that form neural networks. It stimulates the deepest cognitive engagement characterised by knowledge creation and growth (Downes
2006;
2007). It includes creation of new learning artefacts individually or collaboratively and it is combined with learner-content interaction, but in collaborative learning environments (Wang et al.
2014).
These three levels of interaction can occur simultaneously and recursively, and are hierarchical with the operation interaction serving as the foundation of information interaction, while information interaction is the foundation of concept interaction. For (Chen
2004) the higher the level, the more critical it is to the achievement of learning objectives so that only concept interaction leads to meaningful learning. Merging Downes (
2006;
2007) concept of innovation interaction with Bloom’s revised taxonomy (Anderson et al.
2000) that proceeds from remembering to understanding, applying, analysing, evaluating, and creating as cognitive processes, Wang et al. (
2014) superimpose four interaction levels onto the HMII. These are operation, wayfinding, sensemaking and innovation interaction. In operation interaction learners merely practice and remember how to operate various media to build their own learning spaces. In wayfinding interaction, learners have to master the ways to navigate in a complex information environment and connect with different human and nonhuman resources, thus reaching higher levels of understanding, applying and evaluating information and connection formed in this process. Sensemaking is a pattern recognition process, mainly involving applying, analysing and evaluating information. Innovation interaction focuses on the expression of ideas, models or theory by artefact creation and innovation to enhance and build new social, technological and informational connections. This engages learners at the deepest, creation level of Bloom’s taxonomy.
Another process-oriented pedagogical model proposed by Bonanno (
2011;
2014) integrates interactions along three dimensions (domain, technology and community) within three pedagogical levels of interaction (acquisition, participation and contribution). Table
1 show how this can be represented.
Table 1.
Process-oriented pedagogical model proposed by Bonanno (
2011;
2014)
Acquisition Participation Contribution | | | |
The acquisition level is similar to Wang’s et al. (
2014) operation interaction dealing with basic interactional skills in the domain (information categorisation), surface structure of digital tools and interpersonal interactional skills. The participation level is linked to the information interaction level comprising wayfinding and sensemaking within the domain and the learning community. The contribution level is identical to concept interaction and innovation interaction as it deals with learners’ creations within the three domains.
Developed to consider interactions in on-line learning environments these two models do not capture all the dimensions of interactions evident in Cyberparks. Interactions of the different agents (persons, technology or data) with the physical environment are not considered. This shortcoming is addressed by including a fourth dimension – the physical environment. Another dimension (data) is being added considering Cyberparks as smart learning environments characterised by the utilisation and generation of data. To achieve more comprehensive coverage of the possible interactions in a CyberPark, the two models are merged into one, which is depicted in Table
2.
Table 2.
A pedagogical model for CyberParks
Operation interaction | Determining interactional potential of different areas of the CyberPark | Defining a domain-related PLE | Promoting digital and info competencies; developing effective HCI strategies | Identify data sources relevant to PLE | Nurturing interpersonal interactional skills within groups and networks |
Wayfinding | Connecting specialized nodes or information sources related to CyberPark | Connecting key domain info and knowledge nodes to the different aspects of the CyberPark | Using digital tools that mediate learner connection with info, knowledge, resources and relevant people | Connecting to relevant data sources | Connecting with key people and identifying key features of mature identity |
Sensemaking | Negotiation and argumentation to understand the different aspects of CyberPark | Negotiation and argumentation of domain related knowledge; developing an interdisciplinary knowledge structure | Linking technological affordances to learning modes | Developing an organizational network of data sources, types and capturing devices | Identity development; dialogic space analysis and expansion |
Innovation interaction | Re-design of Cyberparks to address citizen evolving needs | Renovation of domain knowledge relevant to Cyberparks | Customize tools to interact in new ways with the hybrid environment | Generating data through creation of digital artefacts | Renovate and extend users’ social networks and digital footprint |
This final model captures most of the interactional possibilities that can take place in Cyberparks and can be used to design and evaluate smart learning activities. At the basic level operational interactions are possible in all five dimensions to build interaction spaces or PLE that merge knowledge and skill competence in different aspects of Cyberparks. Changing the physical environment into a PLE implies getting to know the interactional potential of each section of the place and linking these to ad hoc learning strategies. A smart learning journey, indicating relevant buildings, areas and any associated points of interests facilitates operational interactions in the physical environment. A PLE can be created in a particular domain (history, architecture, engineering, science or humanities) relevant to any aspect of the CyberPark, by identifying resources, support structures involving peer learners, experienced persons or experts, together with learning strategies that can be adopted.
Operational interaction involves connecting learners with different technologies through learner-interface interaction to support their further learning, by connecting with different knowledge and opportunities and by bridging learning across multiple learning and living contexts. Typical actions showing operational interactions with technology include play, download, search, read, view, listen and buy. Also, learners attempt to integrate other social and network-based media into their PLEs and connect with different groups of people and information nodes, to develop a collective distributed technological network. In data rich environments operational interactions enable smartphone users to connect with different data sources after rationalising relevant mobile app interfaces to obtain (and possibly contribute) data related to their learning endeavours. Along the community dimension interpersonal interactional skills have to be nurtured both with contiguous and on-line groups or networks. This develops operational competence with tools used for communication and social networking.
Wayfinding interaction involves finding and connecting the right information and people. Information about different sections of the physical environment are identified and made available for access. People and special interest groups related to the different areas are also identified, organising their means of contact. Learner-content interaction and learner-group interaction are also carried out within any field of knowledge related to the CyberPark, or any part of it, thus elaborating the relevant knowledge web, the learning community and the social networks. This linking and organisational approach is applied to any available or generated data. Typical wayfinding interactions include communicate (chat, rate, comment, message) and share (send, upload, publish).
Sensemaking interaction is a collaborative process that includes information sharing and discussion (Wang et al.
2014). Learners bring together concepts from different domains in a novel way to achieve a coherent comprehension of information and make decisions quickly. Thus, a detailed spatial plan and a global knowledge network serve to integrate the different sections of cyberparks. Knowledge organisation is also carried out in any field consulted, which in turn is linked to the other fields thus creating a final interdisciplinary knowledge structure. With regards to technology, sense making involves linking different digital tools used in various locations in cyberparks, such as QR code systems, augmented reality, geo-tagging and gaming, into a coherent functional system for promoting various modes of learning (Klichowski
2017). Similar patterns are established with regards to data, by creating a bird’s eye view of data sources, data types and data capturing devices. Along the community dimension sensemaking interaction manifest itself in the development and sharing of learners’ knowledge networks, network identities and social presence. Typical sensemaking interactions involve different modes of facilitation such as recommend, channel, tag, subscribe, filter and mentor. The outcomes of sensemaking interaction are organisational-networked patterns connecting tightly together nodes in geophysical, technological, data, social and conceptual (neural) networks which will eventually form the basis for personal contributions in innovation interaction.
Innovation interaction is the deepest form of learner interaction and cognitive engagement. Experienced learners show their knowledge and competence status through contribution, engaging in evaluative and creative activities (Bonanno
2011;
2014). They create (digital) artefacts or elaborate existing ones and share this innovation with others bringing more networking opportunities on the open network where they are both accessible and persistent (Wang et al.
2014). Cyberparks’ users can propose new designs or re-designs of the existing space or parts of it, add or modify new knowledge about (aspects of) the cyberpark, or create/modify open educational resources relevant to some particular aspect or theme. New digital technologies or applications can be customised to interact in innovative or more elaborate ways with the physical, virtual and social environments. Cyberparks visitors use available data and generate new data as (multimedia) artefacts to communicate and share their ad hoc experience. New tools or elaboration of existing ones can be used to innovate and extend users’ social networks and digital footprint. Thus, key innovation-interaction actions include customises, design, produce, contribute, program, model and evaluate.
This pedagogical model provides the necessary framework to design and assess formal or informal learning in cyberparks. It captures patterns of interactions characterising different learning instances or extension of one’s knowledge and social networks. Each square of the grid represents a specific category of interactions that may be used to design focussed learning activities.