Most people would agree that a teacher’s job is to help students learn. But explaining exactly what happens during the process of “learning” is a more contentious subject. In fact, psychologists have proposed a number of vastly different learning theories that attempt to explain how learning works.
One of those theories, which has risen to prominence over the last 20 years, is connectivism. Developed as a framework for understanding knowledge in a digital, ultra-connected world, the connectivism learning theory understands learning as a process of pattern recognition across a series of knowledge-filled networks.
Here we look at the relevance of connectivism in the digital age and how educators can use the theory to improve digital teaching strategies.
What is the Connectivism Learning Theory?
Connectivism was first put forward as a learning theory in the mid-2000s by psychologist George Siemens and philosopher Stephen Downes. It is distinct from other learning theories in that it considers knowledge and learning as non-propositional, i.e., outside the sphere of language and logic.
“In connectivism, there is no real concept of transferring knowledge, making knowledge, or building knowledge,” Downes explains. “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.”
According to connectivism, learning takes place across a network, within which there are many nodes or communities. This is a somewhat controversial position, as it means that learning does not happen exclusively within the human mind but can also take place within organizations or even within computers.
Connectivism and the Digital Age
In a 2005 paper, George Siemens calls connectivism “A Learning Theory for the Digital Age.” In that work, he argues that traditional learning theories like behaviorism, cognitivism, and constructivism are less relevant in the age of computers, as they cannot account for the new ways that we source and store knowledge.
“These theories do not address learning that occurs outside of people (i.e., learning that is stored and manipulated by technology),” Siemens says. “They also fail to describe how learning happens within organizations.”
One of the central tenets of connectivism is the idea that knowledge can be stored in a digital format, not just in one’s mind. But this is not an admission that humans themselves know less than before; rather, according to Siemens, it shows that “technology performs many of the cognitive operations previously performed by learners” such as information storage and retrieval. By offloading these operations to computers, we give ourselves freedom to focus on other things such as locating information and making decisions.
Connectivism provides a lens through which to view learning and knowledge in the digital age. Crucially, it helps us account for new types of learning and knowledge that do not fit particularly well into theories like behaviorism and cognitivism. Examples include:
- Learning how to communicate in Mandarin using the Google Translate app
- Knowing how to change the battery in your laptop by finding the most reliable YouTube tutorials
How to Apply the Principles of Connectivism in the Classroom
Most educators have grappled with the pros and cons of students using the internet to complete a task. An assignment copied straight from Wikipedia likely won’t score top marks. On the other hand, students who know how to obtain information from a variety of online sources often have a firmer grasp on concepts than students who lack that skill.
The connectivism learning theory places a high value on the ability to source information, considering it a genuine demonstration of learning. “Our ability to learn what we need for tomorrow is more important than what we know today,” Siemens argues. “As knowledge continues to grow and evolve, access to what is needed is more important than what the learner currently possesses.”
This unique position lets us think of knowledge in a different way. In addition to know-how (e.g., knowing how to ride a bike) and know-that (e.g., knowing that a bicycle has two wheels), connectivism allows for a kind of knowledge we can call know-where (e.g., knowing where to find instructions for how to repair a puncture).
Teachers can use this philosophy in the classroom. While most lessons involve teaching skills or information, others could be used to show students how to source information using search engines, databases, or web forums. Providing them with know-where, in other words. Instead of putting too much emphasis on the information itself, students could be taught how to recognize reliable and unreliable sources, how to make connections between different sources, and how to feed their own findings and knowledge back into the network.
Of course, teachers don’t have to commit wholeheartedly to connectivism in order to use it to their advantage. Naturally, we want our students to internalize and retain knowledge, as they won’t always have access to the internet. But we can also encourage them to source information in a smart and selective way.
The Future of Connectivism in Education
Since connectivism was introduced almost 20 years ago, students’ use of technology has increased. Almost all American teenagers now have access to a smartphone, giving them instant access to a world of information.
Furthermore, most students experienced fully online or hybrid learning during the pandemic, during which traditional teaching environment had to be swiftly replaced by a digital model with its own unique advantages and challenges. During this period, teachers had to adopt certain connectivist principles, as students were naturally using the internet and its endless supply of information during most lessons.
Although the theory of connectivism is less of a talking point in the 2020s than it was in the 2000s, its principles continue to be relevant. Just as teachers had to think hard about how to treat internet-sourced work back then, now they must consider even thornier subjects such as how to assess AI-generated content. Can a student be said to be “learning” by submitting prompts to a large language model (LLM) to generate an essay? Or could the ease with which students can use LLMs rekindle an interest in traditional learning theories that place value on knowledge that is internally retained?
Teachers looking for inspiration about learning in the digital age can take a look at our classroom quick start guide, which offers practical STEM resources that encourage collaborative and connected learning.