Computer Science – Learning
Scientific paper
Dec 2006
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006aas...20924805p&link_type=abstract
2007 AAS/AAPT Joint Meeting, American Astronomical Society Meeting 209, #248.05
Computer Science
Learning
Scientific paper
We present three learning models that make different assumptions about how the rate of a student's learning depends on the amount that they know already. These are motivated by the mental models of Tabula Rasa, Constructivist, and Tutoring theories. These models predict the postscore for a given prescore after a given period of instruction. Constructivist models show a close connection with Item Response Theory. Comparison with data from both Hake and MIT shows that the Tabula Rasa models not only fit incomparably better, but fit the MIT data within error across a wide range of pretest scores. We discuss the implications of this finding.
Bao Ling
Lee Yeonbae
Pritchard David E.
No associations
LandOfFree
Rate of Learning Models, Mental Models, and Item Response Theory does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Rate of Learning Models, Mental Models, and Item Response Theory, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Rate of Learning Models, Mental Models, and Item Response Theory will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-960571