Using Hidden Markov Models (HMMs) to Understand Student Activity Data
by
Won Ng
Recording and analyzing log files of student actions generated when using
computer-based learning tools is one method of unobtrusive educational
assessment meant to complement more traditional methods. Since manually
reviewing each log can be a long and involved process, we are
investigating the use of the statistical method of hidden Markov modeling
to analyze these logs. Our primary goal is to explore and discover what
kind of information about students' learning patterns HMMs can extract
from these logs. We are experimenting with and modifying the model
parameters to determine the types of events in the log files that will
yield interesting or valuable results.
Advised by Steve Tanimoto
EE1 037
Wednesday
April 21, 2004
3:30 - 4:20 pm