Type:

Other

Description:

We present mathematical learning models—predictions of student’s knowledge vs amount of instruction—that are based on assumptions motivated by various theories of learning: tabula rasa, constructivist, and tutoring. These models predict the improvement (on the post-test) as a function of the pretest score due to intervening instruction and also depend on the type of instruction. We introduce a connectedness model whose connectedness parameter measures the degree to which the rate of learning is proportional to prior knowledge. Over a wide range of pretest scores on standard tests of introductory physics concepts, it fits high-quality data nearly within error. We suggest that data from MIT have low connectedness (indicating memory-based learning) because the test used the same context and representation as the instruction and that more connected data from the University of Minnesota resulted from instruction in a different representation from the test.

Subjects:

  • Education > General

Education Levels:

  • Grade 1
  • Grade 6
  • Grade 8
  • Grade 9

Keywords:

stimlulus sampling theory,NSDL,Education Foundations,Undergraduate (Lower Division),Rescorla-Wagner model,tutoring model,Physics Education Research,General Physics,Graduate/Professional,oai:nsdl.org:2200/20081005191952512T,MBT,FCI,Higher Education,simple connected model,Learning Theory,connectedness model,Physics,memory-based learning,MASTERINGPHYSICS,pure memory model,NSDL_SetSpec_439869,Education

Language:

English

Access Privileges:

Public - Available to anyone

License Deed:

Creative Commons Attribution Non-Commercial Share Alike

Collections:

None
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