Abstract
Existing theories of a particular subject matter are typically revised and adjusted to reflect changing environments. At some point, due to continual revisions, the theories becomes so dichotomous and complex that it is no longer reflective of the subject it is intended to define and explain. At this point, the existing theories need to be replaced with models that more accurately reflect the link between theory and reality. The domain of learning is significantly hampered by progressive revisions of what it means to learn, to know, and to understand. A subset of connectivism, network forming, is presented as an accurate model for addressing how people learn. The test of any theory is the degree to which it solves problems and incongruities within a domain. The shortcomings of behaviourist, cognitivist, and constructivist ideologies of learning are answered in light of learning as a connection-forming (network-creation) process.
Introduction
"Knowledge is of two kinds. We know a subject ourselves, or we know where we can find information on it." (Samuel Johnson).
Our metaphors of learning have become tired and worn. Skinner presented the “black box” of behaviourism (we don’t know what happens inside, so we just focus on the behaviour). Ausubel and others presented a computer-processing model (inputs, processing, coding for retrieval and outputs). More recently, constructivism has been presented as a free-floating theory of learning as an individually constructed experience
Underlying each theory is a deeper ideology and worldview. Philosophers, psychologists, theorists, and linguists have long debated the nature of learning and knowing. Is learning the process of aligning with objective, external knowledge and truth (objectivism)? Are learning and knowledge an interpretive process (i.e. we learn as we experience, and truth is revealed through our action and cognition) (pragmatism)? Or is learning a process where we create our own truth through our own perspective of the world (interpretivism)?
Something is missing in this debate. Knowledge and truth can exist in a variety of ways. Different perceptions of what it means learn (or possess knowledge) do not need to be seen as exclusive. To some degree, objectivism, pragmatism, and interpretivism provide partial insight into a specific aspect of the learning and knowledge process. The nature of the subject matter and the learners themselves impact which approach to knowing will be most beneficial to learners. In contrast to these established views of learning, connectivism presents learning as a connection/network-forming process.
A caution to readers: most papers are intended to allow the author to express what he knows or has come to understand. This paper is largely intended to be a public grabbling of personal dissatisfaction with the learning process as conceptualized over the last century. It is my intent that this paper will provide opportunities for dialogue and exploration with others on how a network-forming model of learning provides insight into our knowledge and information needs in today’s world. The discussion will be held on the blog, discussion forum, and email list at: http://www.connectivism.ca
What is a network?
The beauty of networks is their inherent simplicity. A network requires at minimum two elements: nodes and connections. Nodes carry different names in other disciplines (vertices, elements, or entities). Regardless of name, a node is any element that can be connected to any other element. A connection is any type of link between nodes.
Various factors influence the capacity of nodes to form connections. Once a network has been established, the flow of information can move from one domain to another with relative ease. The stronger the connection between nodes, the more rapidly information will flow.
The information system underlying network creation includes:
- Data – a raw element or small meaning neutral element
- Information – data with intelligence applied
- Knowledge – information in context and internalized
- Meaning – comprehension of the nuances, value, and implications of knowledge
This information system is a continuum, and learning is the process that occurs when knowledge is transformed into something of meaning (and will then generally result in something that can be acted upon). During this process, l learning is the act of encoding and organizing nodes to facilitate data, information, and knowledge flow.
Types of Nodes
Virtually any element that we can scrutinize or experience can become node. Thoughts, feelings, interactions with others, and new data and information can be seen as nodes. The aggregation of these nodes results in a network. Networks can combine to form still larger networks (each node in a larger network can be a network of nodes itself). A community, for example is a rich learning network of individuals who in themselves are completed learning networks.
Nodes are characterized by a general sense of autonomy. A node may exist within a network, even if it is not strongly connected. Each node has the capacity to function in its own manner. The network itself is the aggregation of nodes, but can only exert limited influence on the nature of each node in the network.
While networks are simple in nature, numerous elements impact the flow and dynamics of connection creation. Elements and characteristics of a network include:
- Content (data or information)
- Interaction (tentative connection forming)
- Static nodes (stable knowledge structure)
- Dynamic nodes (continually changing based on new information and data)
- Self-updating nodes (nodes which are tightly linked to their original information source, resulting in a high level of currency (i.e. up to date)
- Emotive elements (emotions that influence the prospect of connection and hub formations).
Data and information are database elements (i.e. they need to be stored and processed in a manner which permits them to be dynamically updated within existing networks). As these elements update, the entire network structure similarly gains and benefits. In a sense, the network grows in intelligence. Knowledge and meaning, on the other hand, receive their worth from the underlying data/information elements.
Forming Connections
Connections are the key to network learning. Yet not every connection has equal weight and influence in the entire structure. Connections can be strengthened based on a number of factors:
- Motivation – motivation is a difficult concept to fully detail. The difficulty arises in that motivation is influenced through our emotions and logic. An individual with a clear goal may have significantly greater motivation to learn a new subject. Keller’s (1987) ARCS model communicates part of the challenge evident in fostering motivation. Numerous factors of attention, relevance of information, our sense of competence, and satisfaction impact the likelihood of connection forming. As discussed in the section Cognitions, Emotions, and Learning section of this paper, learning is a process of encoding nodes and forming connections. Motivation determines if we are receptive to particular concepts as well as our desire to foster deeper network connections through the items listed below (reflection, logic/reasoning, etc.).
- Emotions and how we feel play a large role in how we value nodes and permit the presence of contradictory perspectives. Consider as an example, the phenomenon of global warming. General consensus exists that we are at least partially to blame. For many people, however, only limited lifestyle changes have occurred. How can this concept be seen in light of connectivism (or network learning)? Quite simply – the emotional and motivating nodes (the network itself is simply a node in a larger network. The process scales and reduces based on the size of the network we are considering) evaluate perspectives of global warming in relation to daily lifestyle (commuting, recycling). Until the node of “global warming” begins to impact the quality of life for the individual, many may continue to be comfortable with their global warming node, and route around it. Others may be more sensitive, and place greater emphasis on the concept of global warming because it more seamlessly integrates with the existing network. Reworking and recreating a network at its core takes time. Emotions are the influencing factors that enact other nodes and apply weighting scales to the network elements.
- Exposure – repetition is an excellent way of strengthening connections. A node grows in popularity (relevance) as more nodes link to it. Ideas that link strongly to other ideas are quickly integrated into the network. This is a big factor for the difficulty of personal change. The first glimmer of thoughts of change (quit smoking, exercise more, eat healthy) result in a rogue node. The node exists but has limited traction within the entire network. As the node begins to form its own connections with other nodes (sense of self worth, happier, greater productivity at work, feel better), it gains traction and begins to link and connect to a greater degree with other nodes. The tipping point occurs when the node itself has created a strong enough network to begin to influence the entire thought process (neural network). Once it is no longer a rogue node, it continues to embed itself as node that is used by the rest of the network. Innovation within corporations follows a similar path. New ideas and processes are initially seen as threats by the rest of the organization (organism). As a result, the node is treated as a fringe element and left largely unconnected. However, if the idea (node) has true merit, it will continue to form connections within the network until it creates a sub-network of connections within the larger structure. At this point, it has the capacity to influence the larger network that originally resisted it.
- Patterning is one of the most significant elements of learning. Patterning is the process of recognizing the nature and organization of various types of information and knowledge. The shapes created by these structures will determine how readily new connections can be made. For example, a learner in the field of medicine (who is aware of the nature of their own learning network) may recognize pattern similarity of elements between her field and the field of philosophy. The recognition of these patterns can result in exponential knowledge growth through connecting similar network elements. Duplication can also be minimized. This concept is quite interesting in light of how network theory was popularized in recent literature. Sociologists have spent decades exploring and detailing network phenomenon in a social sense. Several years ago, physicists began exploring networks in greater detail. The significant pattern-similarity between these two fields of research was quickly noticeable (though some would argue that physicists like Barsabi (2002) have popularized network concepts somewhat exclusionary to the work of sociologists). Syncretically fusing experiences of various domains provides substantial benefit in the process of learning formation. Much new knowledge will be generated in the future as specialized fields become more aware of each other.
- Logic is a cornerstone in the learning process. Much of what we know (and have learned) is the by-product of thinking and reflection (reflection is much like logic but permits greater interplay of emotions). The process of thinking involves organizing and structuring our learning networks. As a reflective activity, logic can provide time for nodes to form connections. Connections can be formed without conscious thought, but the process is substantially improved when directed by focused reasoning. Logic is an ingrained connection-forming task, evaluating and recognizing patterns between different concepts and network elements. Cognitive neurology is rapidly developing our understanding of logic and cognition.
- Experience is also a significant aspect of network creation. A great deal of our learning comes through informal means. Experience is a catalyst for both acquiring new nodes and forming connections between existing nodes. Learners who graduate from university or college often have the information and knowledge nodes, but connections themselves do not form fully until the learner is active within his/her field. Experience in this sense is largely a facilitator of connection forming.
Can learning be both an influence and be influenced in the network forming process? If learning is the activity that occurs between knowledge and meaning, it is obviously an object that is being acted upon, that is it is an influenced element in network forming. Learning itself is also an influencing element in that the actual process is one of network creation and forming. The strong reflexive and iterative aspects of learning contribute to its frequent misclassification as largely a content consumption process. Learning cannot be viewed only as a passive (acted upon) or active (acting upon other elements) process.
How does a network view of learning relate to other established theories of the instructional process (instructional in the sense of formal education, though it is no longer an exclusively lecture format)?
Chickering’s work on good practices in undergraduate education provides a strong link to networked learning theory:
Good practice in undergraduate education:
1. encourages contact between students and faculty,
2. develops reciprocity and cooperation among students,
3. encourages active learning,
4. gives prompt feedback,
5. emphasizes time on task,
6. communicates high expectations, and
7. respects diverse talents and ways of learning.
Similarly, Gagne’s Nine Events of instruction integrate with a network-forming theory of learning:
1. gaining attention (reception)
2. informing learners of the objective (expectancy)
3. stimulating recall of prior learning (retrieval)
4. presenting the stimulus (selective perception)
5. providing learning guidance (semantic encoding)
6. eliciting performance (responding)
7. providing feedback (reinforcement)
8. assessing performance (retrieval)
9. enhancing retention and transfer (generalization)
References
1. Barabási, A. L., (2002) Linked: The New Science of Networks, Cambridge, MA, Perseus Publishing.
2. Chickering A. , Gamson Z., (undated) Seven Principles for Good Practice Retrieved on August 10, 2005 from: http://honolulu.hawaii.edu/intranet/committees/FacDevCom/guidebk/teachtip/7princip.htm
3. Driscoll, M. (2000). Psychology of Learning for Instruction. Needham Heights, MA, Allyn & Bacon
4. Keller, J., M., (1987). The Systematic Process of Motivational Design. National Society of Performance and Instruction, 26(9) (p. 2).
5. Ravid R., Raefali S., (2005). Asynchronous Discussion Groups as Small World and Scale Free Networks Retrieved August 10, 2004 from http://www.firstmonday.org/issues/issue9_9/ravid/
6. Rocha, L. M. (1998). Selected Self-Organization and the Semiotics of Evolutionary Systems. Retrieved August 10, 2004 from http://informatics.indiana.edu/rocha/ises.html.
7. Theory into Practice (undated) Conditions of Learning, (R. Gagne) . Retrieved on August 10, 2005 from http://tip.psychology.org/gagne.html
8. Visual Analytics (2005). Centrality. Retrieved August 10, 2004 from http://www.visualanalytics.com/Products/Placements/Centrality.cfm