Connectivism
Connectivism views learning as a network phenomenon shaped by technology and social participation. It helps explain how people learn in digitally saturated environments where information is abundant and constantly changing. This page summarizes the framework, language, uses, and critiques, drawing on Goldie’s overview of connectivism in medical education.
What is Connectivism
- Learning arises by forming and traversing connections among nodes in a network of people, resources, and technologies.
- Knowledge is distributed across networks and is dynamic. The capacity to find, filter, and update matters more than static recall.
- Networks benefit from diversity, autonomy, openness, and rich connectivity among nodes.
- Principles attributed to Siemens include: learning rests in diversity of opinion, connecting to specialized sources, maintaining connections for continual learning, seeing patterns and relationships across domains, valuing currency, and treating decision making as part of learning.
- Influences and supports referenced by connectivist authors include chaos and complexity perspectives, connectionism and associationism, and graph theory.
Core Ideas and Vocabulary
- Node A person, group, platform, repository, device, or site where knowledge is accessed or produced.
- Network Two or more nodes linked to share resources and activity.
- Diversity A wide range of perspectives enhances learning quality within the network.
- Autonomy Participants choose how and when to contribute and connect.
- Openness New ideas and perspectives can enter and circulate.
- Connectivity The extent and strength of links among nodes.
- Personal Learning Environment (PLE) A learner’s set of digital tools and spaces used to gather, create, and share resources.
- Distributive knowledge Knowledge that resides across people and technologies rather than within a single mind or source.
- Currency Keeping knowledge up to date is a core goal in fast changing domains.
- Pattern recognition Learning is recognizing salient patterns in streams of activity across the network.
- cMOOC vs xMOOC cMOOCs are open, network centric experiences aligned with connectivist principles. xMOOCs are platform courses modeled after traditional curricula with videos, quizzes, and forums.
- Social presence The sense of facilitator and peer presence that supports engagement and autonomy in online communities.
- Learning analytics Using data from networked activity to understand and improve learning and provide feedback.
Applications
Goldie highlights applications with a focus on medical education, which transfer well to other professional programs.
- Networked courses and cMOOCs Learners connect via blogs, wikis, social media, RSS feeds, synchronous sessions, and shared hubs. Contributions are aggregated and recirculated for the community.
- Classroom practice Instructors can scaffold the use of web tools, promote critical sourcing and attribution, and design authentic, collaborative tasks that produce sharable artifacts.
- Communities of practice Build multidisciplinary networks so learners interact with knowledgeable others and peers across institutions and roles.
- Curriculum scale Shared online environments can pool resources for core content. Local instructors shift toward facilitation, feedback, and small group guidance.
- Assessment and analytics Evaluate not only individual outcomes but also how learners connect, contribute, and curate. Use analytics to surface patterns in engagement and progress.
- Professional identity and lifelong learning Early participation in open communities fosters habits of seeking current information, filtering noise, and collaborating across boundaries.
Criticisms
- Novelty questioned Critics argue many connectivist ideas overlap with existing theories such as social constructivism, activity theory, situated learning, and social cognitive views.
- Conceptual gaps Claims about learning residing in non human appliances are contested. The role of the other in the network and the evolution of interaction can be underdeveloped in descriptions.
- Individual focus concern Some analyses suggest explanations still lean toward the individual learner despite arguing for distributed knowledge.
- Empirical base Research is mixed and often centered on MOOCs, with issues like high attrition, uneven interaction, and variable diversity. Facilitator social presence appears to matter for autonomy and participation.
- Curriculum tensions A process oriented view can raise concerns about direction, control, and assessment. Clear outcomes, facilitation, and analytics can mitigate these concerns but require careful design.
Foundational Paper: Siemens (2004)
George Siemens’ original 2004 article, “Connectivism: A Learning Theory for the Digital Age,” introduced the idea that traditional theories—behaviorism, cognitivism, and constructivism—no longer fully describe learning in a digital, connected world. Siemens argued that learning now occurs through the creation and navigation of networks that link information, people, and technology.
- Learning as Network Formation: Knowledge exists in nodes—ideas, people, digital tools, and databases—connected by pathways where information flows. Learning is the process of forming and strengthening these connections.
- Dynamic and Distributed Knowledge: No individual can hold all relevant information. The ability to locate and connect to current, accurate sources is more important than memorization.
- Role of Emotion and Motivation: Emotions and personal relevance determine whether learners form lasting connections. Repeated exposure and reflection deepen those connections.
- Pattern Recognition: Understanding emerges from recognizing patterns and relationships among ideas across fields, not just within one domain.
- Integration with Instructional Design: Siemens connected his model to Gagné’s Nine Events and Chickering’s Seven Principles, showing that connectivist design can still incorporate structured guidance, feedback, and collaboration while embracing autonomy and openness.
- Instructional Implication: Designers should create environments that promote exploration, continual updating, and peer interaction. Systems should make knowledge visible and accessible across platforms, emphasizing networked thinking over linear sequencing.
In essence, Siemens reframed learning as the ability to navigate complexity by building and maintaining networks of information and relationships. For instructional designers, this underscores the value of open, connected environments and adaptable learning architectures.
Further Foundation: Downes (2005)
Stephen Downes expanded on Siemens’ ideas by defining connective knowledge—a form of knowing that arises through interactions and connections rather than from isolated facts or representations. He emphasized that learning and knowledge are both distributed and emergent across networks of people, artifacts, and experiences.
Main Points for Instructional Designers
- Types of Knowledge: Downes introduced connective knowledge as distinct from qualitative and quantitative knowledge. It exists within relationships and interactions—understanding how nodes influence and transform each other.
- Knowledge as Connection: Learning occurs through interaction, not observation. The value lies in understanding the connection itself rather than in counting or correlating data.
- Interpretation and Emergence: All knowledge is interpretive. Meaning and understanding emerge from the dynamic interactions between connected elements—just as a community produces insights beyond those of individuals.
- Distribution and Organization: Knowledge and meaning are distributed across networks. The structure of these networks determines what can be learned or known.
- Personal, Social, and Public Knowledge: Downes describes three interrelated levels:
- Personal – internal neural and cognitive organization.
- Social – knowledge that arises through interaction within communities.
- Public – shared, codified knowledge like language and media.
- Knowing as Organization: To “know” is to be organized in a way that enables responsive action. Learning is the reorganization of one’s personal and social networks.
- Reliable Networks and Design Implications: Effective learning environments mirror the qualities of reliable networks:
- Diversity – multiple viewpoints enrich the system.
- Autonomy – learners act freely and authentically.
- Interactivity – knowledge grows through interaction, not isolation.
- Openness – new ideas and participants can enter freely.
Downes’ central idea is that knowledge is grown, not transmitted. Like neurons forming new pathways, learners and societies generate understanding through active connection. Instructional designers can apply this by building open, participatory learning spaces where connections are encouraged and meaning emerges through collaboration.
References
- Siemens, G. 2004. Connectivism: A Learning Theory for the Digital Age. Retrieved from http://www.connectivism.ca.
- Downes, S. 2005. An Introduction to Connective Knowledge. Retrieved from https://www.downes.ca/post/33034.
- Siemens, G. 2005. Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 1–8.
- Downes, S. 2012. Connectivism and connective knowledge. Essays on meaning and learning networks.
- Bell, F. 2011. Connectivism in theory informed research and innovation in technology enabled learning. IRRODL, 12, 98–118.
- Kop, R., and Hill, A. 2008. Connectivism: Learning theory of the future or vestige of the past. IRRODL, 9, 1–13.
- Mackness, J., Mak, S., and Williams, R. 2010. The ideals and reality of participating in a MOOC. Networked Learning Conference.
- Cheston, C., Flickinger, T., and Chisolm, M. 2013. Social media in medical education. Academic Medicine, 88, 893–901.
- Clow, D. 2013. An overview of learning analytics. Teaching in Higher Education, 18, 683–695.
Note Source content for this summary is from Goldie’s review, Siemens’ foundational papers, and related research, adapted for instructional design study use.