Learning Through Inference

Inferential learning requires three distinct, but connected, phases. First, learners build models of situations and systems. This model building brings foundational knowledge togethering a manner that allows learners to make predictions about what will happen in specific situations. This represents deeper learning as the learners must sufficiently understand and generalize what they have learned to make predictions. Second, the models are applied in a logic manner. Third, when observation suggests the model does not accurately predict new situations, learners update their model or assess their logic. Inferential learning becomes an iterative process as the model is improved through additional knowledge and the learners becomes a more skilled and nuanced reasoner.  

Because many models can be constructed and inferences made based on irrelevant observations or inaccurate or inefficient models, this type of learning is best accomplished with the guidance of a teacher. When interacting about models, the leaners and the students are leveraging social learning to construct shared knowledge, thus it can be very effective. As learners develop expertise, they also tend to be more capable of applying what they have learned to new situations. Readers with greater expertise can read more and more complex information, understand it, and integrate the ideas into their existing knowledge. In a similar way, skilled engineers can apply principles to build different types of structures and solve problems that have not been encountered previously. 

One’s ability to reason is affected by a number of factors. Formal reasoning is affected by accumulated knowledge, so those with greater experience may make decisions equal to those with lesser experience, but the younger individual relies on sound reasoning, but the older individual relies on accumulated knowledge.