Chapter 1
Meta-connective learning
Todayâs new learning environments should be characterised by âconnectivityâ rather than âdigitalâ, or the latter enhances the former. Emerging teaching and learning practices go beyond traditional classroom boundaries because connectivity reshapes the educational landscape. In essence, the digitally networked learning environment, through diverse communication channels, has generated various learning modes and models: blended learning, online learning, flipped learning, and enhanced various learning models including multimodal learning, problem-based learning, project-based learning, anchored instruction, inquiry-based learning, and case-based learning. In these modes and models, connectivity, or the quality of being interconnected with proper people, tools, content, and places or communities, is essential for ensuring that technological impacts on education become meaningful and inclusive. In this context, the awareness of connectivity, or meta-connectivity, becomes significant to facilitate authentic learning. In other words, connectivity needs to be conceptualised and structured so that it reflects the authentic nature of the digitally networked world. Its mis-conceptualisations often take place based on a metaphysical assumption that learners are fundamentally or ontologically disconnected from the network. This assumption further restricts epistemic activities to discover or gain truths by individualsâ curiosity and willingness alone and regards axiological or ethical engagement as an âadd-onâ to digital literacy. As a result, it blinds us to the ecological, transformative nature of the network. In this chapter, I will discuss how we understand the concept of connectivity, how the nature of connectivity (re)defines knowledge and learning of knowledge, and what pedagogical values emerge from connectivity. Based on a critical review of connectivism, I will argue for meta-connective learning and its characteristics such as meta-connectivity, interconnectivity, and trans-connectivity, which reflect the ecological, transformative nature of the network. Based on this critically conceptualised meta-connective learning, I will position learners back in the network and articulate its pedagogical aims in terms of learner awareness, learn-ability, and learner engagement.
Connected knowledge and connective learning
Learning may be not fully controlled by learners and teachers because rapidly changing environments and innovations of learning technology often go beyond their perceptions and expectations. In this context, learning means not what they must learn but how to learn and evaluate the new environment and information. In this view, Siemens (2005) and Downes (2005) proposed connectivism as a new learning theory for the digital age. They argued that learning resides outside us in that knowledge is distributed across the networked environment. In essence, the traditional school education, which sustains knowledge transmission by one teacher to many students in a classroom setting, has been broken in the digitally networked world. If knowledge exists distributed among multiple agents, the hierarchical relation between teacher and student becomes invalid and undesirable. From a perspective of connectivism, learning occurs when learners actuate knowledge through their connection with learning communities in the network (Kop & Hill, 2008). That is, knowledge is âa process that occur[s] within nebulous environments of shifting core elements, [whereas] learning⌠is connecting specialized information setsâ (Siemens, 2006, p. 30). Conceptually, connecting between nodes on a network or being connected enables us to learn, and thus understanding and managing connections between nodes is critical for both teachers and students to cope with this 21st-century learning and teaching.
The central metaphors of a network are nodes and connections. In a network, a node is a connection point that can be connected to another node. It is either a redistribution point or a communication endpoint, as organisations, information, data, feelings, activities, and images are considered nodes. This means that a node is both form and formless, such as a representation of a substance, a digital form of any information, and a place where social activities occur. For Siemens (2004), a node is a learning community that is always a part of its network. In a connected status, he argued that learning is a process of creating connections between communities and elaborating the network. Connectivism as a theory of learning focuses on the connectivity between nodes (communities) and requires learning processes to reflect the nature of connectedness where knowledge is distributed across nodes. Thus, it creates âa notion of know-whereâ (Siemens, 2004, para. 4) that learning is to teach learners to know where they can find knowledge when needed in a networked learning environment. The notion of âknow-whereâ supports the following beliefs: learning resides in non-human appliances, and thus it is a process of connecting nodes (Siemens, 2004).
However, critics point out that connectivism does not reflect or de-emphasise human learning (i.e., teacher and student relations); it has a lack of validity on how learning takes place (Kop & Hill, 2008; Verhagen, 2006). In connectivism, in essence, learners are required to have the ability to look for information cross nodes; to evaluate reliability and validity of information; to connect, maintain, and nurture their participating community for continuous learning; and to make decisions on the basis of connectedness (Gerard & Goldie, 2016; Siemens, 2004). In this understanding, the critics may perceive that connectivism assumes that nodes are fixed entities, although connectivists define a node as an organic entity to uphold connectivism. The perception may be derived from a lack of clarification on learner engagement with nodes in connectivism. In their extension of Siemensâ (2005) three node types, neural, conceptual, and external nodes, AlDahdouh, OsĂłrio, and Caires (2015) defined knowledge as a network and articulated the four characteristics of network relationship: (a) the relationship between nodes is interpreted or graded; (b) the direction of the relationship flows from larger to smaller or reversed; (c) a node connecting to itself; and (d) a set of connections creating a single whole or a pattern (pp. 4â6). This means that AlDahdouh et al. viewed a node as an organic entity, and thus, a node itself contains the representation of a network, which is identical to Downesâ (2007) argument.
Yet the characteristics need more elaboration to clarify learner engagement with nodes in which their assumptions remain focused on individual learnersâ ability to access nodes. This elaboration is provided by Siemensâ (2004) understanding of learnability that refers to the ability to identify, access, and examine information on nodes and connect to nodes when necessary for learning. AlDahdouh et al. also endorsed this position by indicating that âaggregating, exploring and discovering the known knowledge is more important than inventing new knowledgeâ in connectivism (p. 11). Such understandings describe learnability as a compensatory individualistic approach to nodes, which undermines the fact that the mutual interactions between learners and nodes can affect the topography of the network. In other words, learnability needs to be expanded in a way to ensure that learner engagement reflects the ecological nature of the networked world. This suggestion is based on the assumptions that (a) the environmental conditions constantly change and (b) learners never get out of the environment in an ontological sense. It is obvious that learner engagement can change nodes, yet the connections also determine learner engagement, unless we deny the ecological nature. In other words, the connectivity between nodes should refer to ârelationshipâ or being connected, and this view accepts the emergence of diverse networked relationships where learning activities can be assessed and designed.
To examine a node as an organic entity, thus, the question should be shunted to, To what extent do the nodes and their connections determine learner engagement before what learners can or should do? Both engagement and disengagement are engagement with nodes, as we never get out of it: discontinuing engagement, withdrawing from sites, and removing contributed information are also evidence of learnersâ engagement with nodes, whereas increasing engagement (e.g., higher ranked users), creating, sharing and disseminating information, and adding its connection to another community, which indicates that the learners are active. This idea of learner engagement means that learner engagement or disengagement with nodes can directly affect the quality of connections and more importantly, in turn, the reshaped/renewed connections; as well, nodes by learner inputs can affect learning quality. Such engaged and disengaged engagement aspects are pedagogically significant in that both aspects indicate that we are always being connected to the networked environment. This connection implies that an ontological assumption has to precede epistemological justification (which is also ontological) because the learning environment has changed, and thus pedagogical development should reflect the ontology, otherwise pre-existing epistemology brings us back to the old learning environment. In this sense, the ability to connect to nodes (epistemology) has to be built based on how (form and formless) things exist in the digitally networked world (ontology), and therefore, epistemology is used to make meaningful contributions to the network (axiology). In this process, a holistic, relational pedagogical approach is needed over an atomistic, instrumentalist pedagogical approach because the ontological assumption of each approach is different, and the former is closer to the ontology of the networked world. This methodological shift raises an ontological-pedagogical question, How does knowledge exist in the networked learning environment?
Distributed knowledge and learnability
Siemens (2008) claimed that the epistemological framework for learning in connectivism is distributed knowledge that goes beyond objectivism, pragmatism, and interpretivism. First, objectivism holds the belief that knowledge is external to the mind, and it can be acquired through senses and experience. Second, pragmatism suggests that knowledge is a negotiation between inquiry/reflection and action/experience, so that learning is to justify the existence of the mind in terms of what is good for learners to believe. In other words, pragmatists are concerned with practical consequences of learnersâ conception of objects. Last, interpretivism claims that knowledge is an internal construct, and it is formed through socialisation and cultural clues. These three learning theories can be summarised with the following pedagogical terms: teacher-centred, learner-centred, and learner-driven approaches. On the contrary, the theory of distributed knowledge is claimed as a learner-driven approach (Marcum, 2006; Bell, 2010). The theory of distributed knowledge proposed by Downes (2006) and supported by Siemens (2008) relies on the concept of emergent, connected, and adaptive knowledge. Knowledge and learning of knowledge are distributive and are not located in any given place but âconsist of the network of connections formed from experience and interactions with a knowing communityâ (Downes, 2006, para. 13). In essence, connectivism views learning not as a separate activity but as being embedded in meaningful activities, and they believe that it occurs through communication, interaction, participation, sharing, and creation in a network (Downes, 2006). In this sense, the theory of distributed knowledge is close to an action-driven interpretivist learning theory.
Based on emergentist theories of knowledge, which is in contrast with reductionism, Downes (2006) argued that the concept of supervenience is not reducible to physical phenomena, although it is dependent on them, and the non-physical state could be a belief or a mental state (para. 47). This argument means that knowledge is distributed throughout areas, objects, or groups, and thus it cannot be defined as a discrete entity but as instances of that knowledge. This should be a logical conclusion to view learning as making connections between nodes (e.g., ideas, facts, people, and communities): distributed knowledge across an information network can be stored in a variety of digital formats (e.g., images, audio, and video clips). For Siemens (2008), both knowledge and learning are to ârest in diversity of opinionsâ (as they are distributed). The nature of distributed knowledge is âconstant changeâ, so that its accuracy and validity also change over time according to learnersâ discovery of new networks (precisely, a cluster of nodes in the digitally networked world) (Kop & Hill, 2008).
Contradictorily, however, the concept of learner discovery implies that connectivism may not be completely free from objectivism, pragmatism, and inter-pretivism. The hidden ontological assumptions would be as follows: (a) knowledge exists on a network, which is outside the human mind (objectivism); (b) learners construct their own mental models through negotiation (pragmatism) and interpretation (interpretivism). As Kerr (2007) argued, in addition, connectivism holds the lessons of constructivism in which learning in the network requires individual learners to build their own cognitive frame...
