Many theories about how humans have been learning have been proposed over the years, and these theories continue to change today. From the Behaviorism theory which included the Conditioning model of Pavlov; Humanism theory which included Maslow’s Hierarchy of Needs model; to Identity Theory and Erikson’s Stages of Development model, there are many on the market and it’s difficult to make sense of which is the most productive and efficient.
How does a genius like Einstein learn to think? Is it a matter of using the brain more fully? It is a question whose answer remains mysterious. Nonetheless, understanding the theories of learning can shed some light on how we come to know what we know.
As we learn more about how the human brain works and as we become increasingly adept at constructing models of cognition, theories of learning are changing also. The following infographic is an overview of the history and particulars of learning theories, with their various pros and cons.
From the research below, the Traditional Blended Learning Theory, when combined with Connectivism, gives a very efficient and productive learning framework in general. However, different approaches are suited to different people, and no one theory is a one-size-fits-all. Some people learn best from a hands-on approach, while others learn more abstractly.
Given the increasingly implemented role of technology in education, the importance of Connectivism cannot be overstated.
Connectivism is best thought of in terms of a metaphor of technology such as computers, mobile phones, tablets, television, etc, and how they operate and how we interact and learn in similar ways. This includes how we interact with social groups in a cultural context.
It is a theory which involves recognizing and interpreting patters and connecting information from different sources and creating networks of complexity. Some networks are more complex than others, and some links are stronger than others. It is a theory of learning which is primarily concerned with connections that are made and how these connections are gathered into a network of meaningful complexity.