Knowledge Acquisition - Learning Theory

Generative Knowledge

• How is Abstract, Generative Knowledge Acquired? A Comparison of Three Learning Scenarios

  Abstract - Several theories of learning have been proposed to account for the acquisition of abstract, generative knowledge including schema theory, analogical learning and implicit learning. ....

  Theories of Deep Learning - In order to solve complex, novel problems one must be able to retrieve previously learned information from memory and apply it to the current situation. For instance, students learning geometry need to be able to apply mathematical formulas acquired during study to novel problems encountered at test. Although surface features of the problems change (e.g., specific values: a=5 to a=15) the abstract operators used to solve the problems stay the same. Thus, in order for the knowledge gained from study to be helpful on the test it must be both abstract and generative. How such deep knowledge is acquired remains a central question for researchers in psychology, philosophy and education.

• Deep knowledge
  Knowledge is deep or thick when it concerns the central ideas of a topic or discipline and because such knowledge is judged to be crucial to a topic or discipline. Knowledge is deep when relatively complex connections are established to central concepts.
  
  Knowledge is shallow, thin or superficial when it is not connected with significant concepts or central ideas of a topic or discipline, and it is dealt with only in an algorithmic or procedural fashion. Knowledge is also shallow when important, central ideas have been trivialized by the teacher or students, or when it is presented as non-problematic. This superficiality can be due, in part, to instructional strategies such as when teachers cover large quantities of fragmented ideas and bits of information that are unconnected to other knowledge.

• TEACHING FOR UNDERSTANDING: David Perkins

  • Force student to work through performance of understanding.

  • Simutrans is an excellent tool for an automated process that forces a understanding performance. For example, after the students demonstrating a basic understanding of economics in the game, then start to vary the basic parameters to force application of core knowledge.