Connectivism

Introduction

The traditional learning theories of Behaviorism, Cognitivism, and Constructivism (both Cognitive Constructivism and Social Constructivism or Socioculturalism) have long shaped instructional design practices. However, these theories emerged before the transformative impact of technology on learning environments. Behaviorism, Cognitivism, and Constructivism were developed in an era when learning primarily occurred in structured classroom settings with limited access to external information sources. At that time, tools like the internet, digital collaboration platforms like shared Google docs or Zoom, and education data analytics like Moodle or Canvas LMS, which are now integral to how we learn and communicate, were nonexistent. As a result, these theories do not fully account for the complexities introduced by modern technological advancements, such as the ability to access vast networks of knowledge in real-time or the role of artificial intelligence in facilitating personalized learning experiences. As the digital age reshapes the way knowledge is created, shared, and applied, a new theoretical framework is necessary. Connectivism, as a learning theory for the digital age, addresses these changes by emphasizing the role of networks, connections, and technology in learning.

Origins of Connectivism

The development of connectivism owes much to the contributions of George Siemens and Stephen Downes. Siemens first introduced the concept in 2004 through a blog post, which he later expanded into an article in 2005 titled “Connectivism: A Learning Theory for the Digital Age” (Siemens, 2005). Siemens emphasized how learning extends beyond individuals, residing in networks of information and connections. Stephen Downes, in his publication “An Introduction to Connective Knowledge” (Downes, 2005), highlighted the role of networks in creating and navigating knowledge. Together, they delivered the first Massive Open Online Course (MOOC) in 2008, embodying connectivist principles by fostering global collaboration and leveraging diverse digital tools. These theorists positioned connectivism as a response to the challenges of the digital age, integrating ideas from chaos, network, complexity, and self-organization theories

Connectivism emerged from the intersection of chaos, network, and complexity theories. It was developed to address the limitations of traditional theories in explaining how learning occurs in technology-rich environments. Driscoll (2000) defines learning as “a persisting change in human performance or performance potential…[which] must come about as a result of the learner’s experience and interaction with the world” (p.11). Connectivism builds on this foundation by acknowledging that learning can occur outside individuals, such as within organizations or databases, and that the process of forming connections is central to learning.

The concept is grounded in the exponential growth of knowledge. Gonzalez (2004) describes the challenges of rapidly diminishing knowledge life:

“One of the most persuasive factors is the shrinking half-life of knowledge. The “half-life of knowledge” is the time span from when knowledge is gained to when it becomes obsolete. Half of what is known today was not known 10 years ago. The amount of knowledge in the world has doubled in the past 10 years and is doubling every 18 months according to the American Society of Training and Documentation (ASTD). To combat the shrinking half-life of knowledge, organizations have been forced to develop new methods of deploying instruction.”

This rapid evolution necessitates new methods of learning and teaching, as the traditional notion of static knowledge is no longer viable.

Fundamental Tenets of Connectivism

Connectivism posits several principles that differentiate it from traditional learning theories:

• Learning and knowledge rest in the diversity of opinions.
  • For example, students participating in an online discussion forum gain a richer understanding of a topic by engaging with diverse perspectives from peers around the world.
• Learning is a process of connecting specialized nodes or information sources.
  • For instance, a researcher using multiple databases and expert networks to compile data on a new subject demonstrates this principle.
• Learning may reside in non-human appliances.
  • For example, an AI-driven learning platform storing customized lessons and assessments for individual learners exemplifies this idea.
• The capacity to know more is more critical than what is currently known.
  • An example is a professional who builds connections in a new field to access relevant expertise when required, rather than relying solely on their existing knowledge.
• Nurturing and maintaining connections is needed to facilitate continual learning.
  • For instance, a scientist attending regular conferences to stay connected with advances in their field illustrates this principle.
• The ability to see connections between fields, ideas, and concepts is a core skill.
  • For example, interdisciplinary teams combining knowledge from biology and computer science to develop bioinformatics solutions demonstrate this skill.
• Currency—up-to-date knowledge—is the goal of connectivist learning.
  • An example is a journalist who uses real-time news feeds and social media to report on breaking stories.
• Decision-making is itself a learning process, shaped by shifting realities and contexts.
  • For instance, a company adapting its marketing strategy in response to emerging consumer trends highlights this principle.

Connectivism also emphasizes the role of networks and small-world theories, where weak ties allow for the exchange of information between disparate groups, fostering innovation and creativity. For example, Massive Open Online Courses (MOOCs) like Coursera, edX, or Khan Academy provide structured courses in a digital environment, leveraging tools like the internet and digital collaboration platforms to connect learners globally. A student in India can take a course from an Ivy League professor in the United States, collaborate with peers from Europe through discussion forums, and use data analytics provided by the platform to track their progress and identify areas for improvement.

Relevance to Instructional Design

In instructional design, connectivism challenges the assumption that learning occurs solely within the individual. Instead, it shifts the focus to designing environments that foster connections and networks. By leveraging technology and networked communities, instructional designers can create opportunities for learners to access diverse information sources, collaborate in real-time, and engage in continuous learning.

Practical applications include:

• Incorporating social network analysis to understand and enhance knowledge flow.
• Designing learning activities that encourage collaboration across diverse perspectives.
• Utilizing digital tools to connect learners with experts, communities of practice, and relevant resources.

Connectivism underscores the importance of adaptability and pattern recognition in learning, equipping learners to navigate the complexities of a rapidly changing information landscape. For example, in a classroom setting, instructors can design problem-based learning activities where students analyze real-world scenarios, such as assessing trends in climate change data, to identify patterns and adapt their solutions as new information emerges.

Limitations of Connectivism

Connectivism offers valuable insights into modern learning but also has its drawbacks. Critics point out that it lacks solid research support and clear steps for putting it into practice (Gonzalez, 2004). It focuses heavily on technology and networks, sometimes ignoring how people think and feel during learning. Another issue is that it assumes everyone has reliable internet and access to digital tools, which isn’t true in all parts of the world (Driscoll, 2000). While it’s good for explaining complex, fast-changing knowledge, more research is needed to make it easier to use in real-world settings.

Conclusion

Connectivism provides a framework for understanding learning in the digital age, where knowledge is dynamic, decentralized, and interconnected. By emphasizing the importance of networks, technology, and diversity of thought, it addresses the shortcomings of traditional theories and aligns with the realities of contemporary learning environments. Instructional designers and educators should consider Connectivism to create learning experiences that prepare individuals for the challenges of a rapidly evolving world.

Reflection

Use the H5P Documentation tool below to answer the questions for Part 1 of your Oar and Rubber Boot assignment (ORB) on connectivism. Once you’ve worked through the H5P, select Create Document, and then copy/paste the document into your ORB Google doc for this theory. You’ll complete Part II of this assignment in your Google doc.

References

Driscoll, M. (2000). Psychology of Learning for Instruction. Needham Heights, MA, Allyn & Bacon.

Downes, S. (2007, February 3). What Connectivism Is. Half an Hour. https://halfanhour.blogspot.com/2007/02/what-connectivism-is.html

Gonzalez, C., (2004). The Role of Blended Learning in the World of Technology. Retrieved December 10, 2004 from https://edtechbooks.org/-Pt.

Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1).

Stephenson, K., (Internal Communication, no. 36) What Knowledge Tears Apart, Networks Make Whole.Retrieved December 10, 2004 from https://edtechbooks.org/-Mg.

Additional Information

Barabási, A. L., (2002) Linked: The New Science of Networks, Cambridge, MA, Perseus Publishing.

Buell, C. (undated). Cognitivism. Retrieved December 10, 2004 from https://edtechbooks.org/-Gw.

Brown, J. S., (2002). Growing Up Digital: How the Web Changes Work, Education, and the Ways People Learn. United States Distance Learning Association. Retrieved on December 10, 2004, from https://edtechbooks.org/-Zw

Gleick, J., (1987). Chaos: The Making of a New Science. New York, NY, Penguin Books.

Gredler, M. E., (2005) Learning and Instruction: Theory into Practice—5th Edition, Upper Saddle River, NJ, Pearson Education.

Kleiner, A. (2002). Karen Stephenson’s Quantum Theory of Trust. Retrieved December 10, 2004 from https://edtechbooks.org/-cA.

Landauer, T. K., Dumais, S. T. (1997). A Solution to Plato’s Problem: The Latent Semantic Analysis Theory of Acquisition, Induction and Representation of Knowledge. Retrieved December 10, 2004 from https://edtechbooks.org/-yt.

Rocha, L. M. (1998). Selected Self-Organization and the Semiotics of Evolutionary Systems. Retrieved December 10, 2004 from https://edtechbooks.org/-ju.

ScienceWeek (2004) Mathematics: Catastrophe Theory, Strange Attractors, Chaos. Retrieved December 10, 2004 from https://edtechbooks.org/-Dw.

Vaill, P. B., (1996). Learning as a Way of Being. San Francisco, CA, Jossey-Blass Inc.

Wiley, D. A and Edwards, E. K. (2002). Online self-organizing social systems: The decentralized future of online learning.Retrieved December 10, 2004 from https://edtechbooks.org/-Zn.