Proyecto y prácticas de impacto en la sociedad

Upon the introduction of technology in everyday life, Connectivism redefined the concepts of learning and knowledge. Pioneered by Siemens (2005), Connectivism

23

integrates principles from Chaos, Network, Complexity and Self-organisation theories while arguing that more traditional theories of education (i.e. Behaviourism,

Cognitivism and Constructivism) are insufficient to explain new learning phenomenon (Siemens, 2006a).

Connectivism is a learning theory that centres in three main assumptions and believes: knowledge and learning is a network of connections; knowledge and learning reside inside and outside the individual; and knowledge is distributed (Siemens, 2005). In other words, Connectivism’s states that knowledge and learning are ever-evolving networks of interactions between and within individuals at the internal (i.e. neural and conceptual level) and external levels (e.g. networks formed in interactions with others and objects such as the Internet) (AlDahdouh et al., 2015). For this, Siemens (2005) proposes a new definition of learning:

Learning is a process that occurs within nebulous environments of shifting core elements – not entirely under the control of the individual. Learning (defined as actionable knowledge) can reside outside of ourselves (within an organization or a database), is focused on connecting specialized information sets, and the connections that enable us to learn more are more important than our current state of knowing (p. 5).

Then, knowledge is a distributed network and learning is the navigation and connections formed within networks (Downes, 2012). In this sense, Connectivism shares features of Constructivism and even Cognitivism in the fact that learning implies a construction of knowledge. However, Connectivism argues that knowledge and learning are not only bound to the constrains of language, but knowledge is distributed and even constructed outside language. Connectivism denies that knowledge can be created, represented or constructed in the individual since individuals can only access a portion of this

knowledge, then, knowledge can reside inside and outside the individual. Downs (2012) further argues that “…knowledge can be produced by networks” (p. 32), and therefore, knowledge and learning are network bound. In this sense, Connectivism provides eight basic principles of learning (Siemens, 2005):

• “Learning and knowledge rest in diversity of opinions.

• Learning is a process of connecting specialized nodes or information sources.

• Learning may reside in non-human appliances.

• Capacity to know more is more critical than what is currently known. • Nurturing and maintaining connections is needed to facilitate continual

24

• Ability to see connections between fields, ideas, and concepts is a core skill. • Currency (accurate, up-to-date knowledge) is the intent of all Connectivist

learning activities.

• Decision-making is itself a learning process. Choosing what to learn and the meaning of incoming information is seen through the lens of a shifting reality. While there is a right answer now, it may be wrong tomorrow due to alterations in the information climate affecting the decision.” (p. 5)

Furthermore, Downes (2006) enlists five major implications aligned with the new understanding of knowledge:

• Knowledge is sub-symbolic (i.e. knowing words does not mean that there is knowledge)

• Knowledge is distributed • Knowledge is interconnected • Knowledge is personal

• Knowledge is an emergent phenomenon

This new view of learning and knowledge clearly decentralizes the individual as the learner and it transforms it into a complex system that only forms in the interaction of the subject with the internal and external holders of information (Downes, 2012). Then, the focus of learning in school switches from content of information to how the network of learners navigates these vast networks of information.

These important networks of interaction are not only possible but needed because of the current abundance flow of information, impossible to contain in one single individual (Siemens, 2006b). Networks are defined as “…connections between entities.” (Siemens, 2005, p. 4), where entities can be at the neural, ideas and institutional levels.

AlDahdouh et al. (2015) created a network typology to explain this distributed networks of knowledge with its focus in the nodes and their relationships formed in interaction. The node in this typology “refers to any objects that can be connected” (AlDahdouh et al., 2015, p. 4). Nodes come in three forms: neural (i.e. neurons and dendrites),

conceptual (i.e. connected thoughts, ideas and concepts), and external (i.e. people, books, websites, programs and databases connected through the Internet or face-to-face relationships). Relationships refer to the links between the nodes of interaction and they have four characteristics: graded or interpreted, direction, self-joined or connected to itself, and patterned if the relationship is composed of two or more inseparable nodes.

25

Finally, AlDahdouh et al.(2015) agrees with Siemens (2005) that knowledge is a moving time-bound process. The speed of knowledge discovery in the world after the implementation of the Internet has created a learning reality that is dependent on time of accessed knowledge. To this extend, Siemens (2005) states that, “While there is a right answer now, it may be wrong tomorrow due to alterations in the information climate affecting the decision" (p. 4). Then knowledge depends on emergence, volatility and autonomy: nodes of knowledge weaken or strengthen according to these characteristics.

In brief, Connectivism explains a new way of learning while describing knowledge from a network perspective. It decentralises learning and knowledge from the individual to communities and appliances and knowledge as a distributed phenomenon. Learning and knowledge are network bound, hence the need to understand how networks form at the node levels in space and time (i.e. context).