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ERIC Number: EJ816473
Record Type: Journal
Publication Date: 2008
Pages: 8
Abstractor: As Provided
Reference Count: 38
ISSN: ISSN-1554-9178
Mathematical Learning Models that Depend on Prior Knowledge and Instructional Strategies
Pritchard, David E.; Lee, Young-Jin; Bao, Lei
Physical Review Special Topics - Physics Education Research, v4 n1 p010109-1--010109-8 Jan-Jun 2008
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. (Contains 1 table, 2 figures, and 38 notes.)
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Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A