Connectivism: How to Better Understand Learning in the Digital Age

The three broad learning theories most often utilized in the creation of instructional environments are Behaviorism, Cognitivism, and Constructivism. These theories, however, were developed in a time when learning was not impacted through technology. In other words, they do not address learning that occurs outside of people, that is stored and manipulated by technology. Nowadays the Digital revolution is changing the way we act, teach and think. The main trends in education are the need for interdisciplinarity and, more and more often, informal learning. Also, learning is becoming a continual process that never ends. Indeed, consider the half-life of knowledge: half of what is known today was not known 10 years ago. More or less. Thus, there is no doubt that today there is an increasing need for knowledge management skills. For instance, know-how and know-what is being supplemented with know-where. So, to what extent do existing learning theories meet the needs of today’s learners, and anticipate the needs of learners of the future? A group of scholars recently built a new fascinating theory of learning called Connectivism. The two papers Siemens’ Connectivism: Learning as Network Creation (2005) and Downes’ An Introduction to Connective Knowledge (2005) triggered the debate.

Connectivism is a theoretical framework for understanding learning in the digital age. It integrates principles explored by chaos, network, and complexity and self-organization theories. In particular, it acknowledges that learning is a process that occurs within nebulous environments of shifting core elements – not entirely under the control of the individual. Learning can indeed reside outside of ourselves (within an organization or a database): knowledge is distributed. As Siemens (2005) puts it:

“Connectivism is driven by the understanding that decisions are based on rapidly altering foundations. New information is continually being acquired. The ability to draw distinctions between important and unimportant information is vital. The ability to recognize when new information alters the landscape based on decisions made yesterday is also critical.”

Siemens believes that a new learning theory, in fact, is required, due to the exponential growth and complexity of information available on the Internet, new possibilities for people to communicate on global networks, and for the ability to aggregate different information streams. He argues that “knowledge does not only reside in the mind of an individual, knowledge resides in a distributed manner across a network . . . learning is the act of recognizing patterns shaped by complex networks.’ These networks are internal, as neural networks, and external, as networks in which we adapt to the world around us.

The main principles of connectivism are the following:

  • Learning may reside in non-human appliances.
  • Learning and knowledge rests in diversity of opinions.
  • Learning is a process of connecting specialized nodes or information sources. In fact, in the connectivist model, a learning community is described as a node, which is always part of a larger network. In this respect, a fundamental role is performed by weak ties, which are links or bridges that allow short connections between information. Our small world networks are generally populated with people whose interests and knowledge are similar to ours. Finding a new job, as an example, often occurs through weak ties. This principle has great merit in the notion of serendipity, innovation, and creativity. Connections between disparate ideas and fields can create new innovations.
  • Nurturing and maintaining connections is needed to facilitate continual learning.
  • Ability to see connections between fields, ideas, and concepts is a core skill.
  • Capacity to know more is more critical than what is currently known. As Siemens puts it: “the pipe is more important than the content within the pipe. Our ability to learn what we need for tomorrow is more important than what we know today.”
  • 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.

Proponents of connectivism are thus “exploring a model of learning that reflects the network-like structure evident in online interactions,” (p. 12) Nonetheless, Downes and Siemens do not suggest that connectivism is limited to the online environment. The online environment is one application that has been important for the development of connectivism, but the theory applies to a larger learning environment, and helps to inform how we understand our relatedness to the world, and consequently how we learn and understand from it.

Siemens sees the alignment between epistemologies and learning theories as detailed in figure. While the first three are universally accepted, the concept of connectivism as a learning theory has had some criticism. Critics argue that the theory remains unsubstantiated philosophizing and that existing theories satisfactorily address the needs of learning in the digital age. However, Downes (2007b) interestingly remarked that:

“Where connectivism differs from those theories, I would argue, is that connectivism denies that knowledge is propositional. That is to say, these other theories are ‘cognitivist’, in the sense that they depict knowledge and learning as being grounded in language and logic. Connectivism is, by contrast, ‘connectionist’. Knowledge is, on this theory, literally the set of connections formed by actions and experience. It may consist in part of linguistic structures, but it is not essentially based in linguistic structures, and the properties and constraints of linguistic structures are not the properties and constraints of connectivism. . . In connectivism, there is no real concept of transferring knowledge, making knowledge, or building knowledge. Rather, the activities we undertake when we conduct practices in order to learn are more like growing or developing ourselves and our society in certain (connected) ways.”

Humans, however, may be predisposed to identifying certain patterns on the basis of their neurological makeup; these patterns, in fact, may be intrinsic qualities of mind. Kerr (2007a) refers to Kay’s nonuniversals, a series of understandings (identified on the basis of research by anthropologists) that are not learned spontaneously, and which are common to all known human societies – for instance, “deductive abstract mathematics, model-based science, democracy [and] slow deep thinking.” Kerr suggests that if learning these nonuniversals is considered important, then methods ought to be identified to teach them. The suggestion is to point out the importance of identifying strategies to ensure that at least some forms of learning persist. It is in this challenge that connectivism may actually help.

To conclude, is Connectivism enough to constitute a new learning theory, and does it has anything new to offer? Is it merely a digital extension of constructivism? It is difficult to argue that. Presently, connectivism is lacking an extensive body of empirical research literature to lend it support. As Kop and Hill conclude in their paper Connectivism: Learning theory of the future or vestige of the past? (2008): “A paradigm shift, indeed, may be occurring in educational theory, and a new epistemology may be emerging, but it does not seem that connectivism’s contributions to the new paradigm warrant it being treated as a separate learning theory in and of its own right. Connectivism, however, continues to play an important role in the development and emergence of new pedagogies, where control is shifting from the tutor to an increasingly more autonomous learner.” Generally, it seems too early to judge such theory. Certainly, it stresses fundamental trends, dynamics and challenges of the emerging information society and it helps to identify new strategies to deal with them.



Downes, S. (2012). Connectivism and connective knowledge. Essays on meaning and learning networks. National Research Council Canada.

Siemens, G. (2014). Connectivism: A learning theory for the digital age.

Kop, R., & Hill, A. (2008). Connectivism: Learning theory of the future or vestige of the past?. The International Review of Research in Open and Distributed Learning9(3).



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