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Physical Review Special Topics - Physics Education Research
written by David E. Pritchard, Young-Jin Lee, and Lei Bao
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 Levels Resource Types
Education Foundations
- Learning Theory
General Physics
- Physics Education Research
- Lower Undergraduate
- Graduate/Professional
- Reference Material
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© 2008 The American Physical Society
DOI:
10.1103/PhysRevSTPER.4.010109
ISSN Number:
1554-9178
NSF Number:
0457451
PACSs:
01.40.Fk
01.40.Ha
Keywords:
FCI, MASTERINGPHYSICS, MBT, Rescorla-Wagner model, connectedness model, memory-based learning, pure memory model, simple connected model, stimlulus sampling theory, tutoring model
Record Creator:
Metadata instance created July 4, 2008 by Lyle Barbato
Record Updated:
July 14, 2013 by Lyle Barbato
Last Update
when Cataloged:
May 20, 2008
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AIP Format
D. Pritchard, Y. Lee, and L. Bao, Phys. Rev. ST Phys. Educ. Res. 4 (1), 010109 (2008), WWW Document, (http://dx.doi.org/10.1103/PhysRevSTPER.4.010109).
AJP/PRST-PER
D. Pritchard, Y. Lee, and L. Bao, Mathematical learning models that depend on prior knowledge and instructional strategies, Phys. Rev. ST Phys. Educ. Res. 4 (1), 010109 (2008), <http://dx.doi.org/10.1103/PhysRevSTPER.4.010109>.
APA Format
Pritchard, D., Lee, Y., & Bao, L. (2008, May 20). Mathematical learning models that depend on prior knowledge and instructional strategies. Phys. Rev. ST Phys. Educ. Res., 4(1), 010109. Retrieved December 18, 2017, from http://dx.doi.org/10.1103/PhysRevSTPER.4.010109
Chicago Format
Pritchard, D, Y. Lee, and L. Bao. "Mathematical learning models that depend on prior knowledge and instructional strategies." Phys. Rev. ST Phys. Educ. Res. 4, no. 1, (May 20, 2008): 010109, http://dx.doi.org/10.1103/PhysRevSTPER.4.010109 (accessed 18 December 2017).
MLA Format
Pritchard, David E., Young-Jin Lee, and Lei Bao. "Mathematical learning models that depend on prior knowledge and instructional strategies." Phys. Rev. ST Phys. Educ. Res. 4.1 (2008): 010109. 18 Dec. 2017 <http://dx.doi.org/10.1103/PhysRevSTPER.4.010109>.
BibTeX Export Format
@article{ Author = "David E. Pritchard and Young-Jin Lee and Lei Bao", Title = {Mathematical learning models that depend on prior knowledge and instructional strategies}, Journal = {Phys. Rev. ST Phys. Educ. Res.}, Volume = {4}, Number = {1}, Pages = {010109}, Month = {May}, Year = {2008} }
Refer Export Format

%A David E. Pritchard
%A Young-Jin Lee
%A Lei Bao
%T Mathematical learning models that depend on prior knowledge and instructional strategies
%J Phys. Rev. ST Phys. Educ. Res.
%V 4
%N 1
%D May 20, 2008
%P 010109
%U http://dx.doi.org/10.1103/PhysRevSTPER.4.010109
%O application/pdf

EndNote Export Format

%0 Journal Article
%A Pritchard, David E.
%A Lee, Young-Jin
%A Bao, Lei
%D May 20, 2008
%T Mathematical learning models that depend on prior knowledge and instructional strategies
%J Phys. Rev. ST Phys. Educ. Res.
%V 4
%N 1
%P 010109
%8 May 20, 2008
%@ 1554-9178
%U http://dx.doi.org/10.1103/PhysRevSTPER.4.010109


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