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ARCADE SOFTWARE PRODUCTS
This NSF-funded project resulted in the development of new
statistical software intended to support research in the areas
of text comprehension and educational technology for non-commercial
applications and academic research purposes only.
Knowledge Digraph Contribution Analysis Software
Dr. Golden is currently developing a statistical model which
allows the user to incorporate prior knowledge about the semantic
and syntactic relationships among the elements in a text. This statistical
model is called
Knowledge Digraph Contribution (KDC) Analysis.
The parameters of the
model can be estimated from human recall, summarization, and question-answering
data. Each parameter may be interpreted as the relative strength of a different
knowledge schema. The
parameters of the KDC model are uniquely determined and the large
sample probability distribution of the estimates of the parameters can
be derived for large sample sizes. These mathematical results are relevant
for deriving customized statistical tests for testing hypotheses about the
relevance of specific knowledge schema weighting parameters. Statistical
tests for deciding which of several text knowledge schemata "best-fits"
a given set of recall data have also been developed. More recently, methods
of sampling from the KDC probability model have been derived which allow one
to generate synthetic recall protocols and then compare these synthesized recall
protocols with actual human recall protocols for the purpose of evaluating the
validity of the user-specified "knowledge analyses" of the text.
A prototype version of this software package is now available to text
comprehension researchers.
Software to Support Semantic Coding of Human Free Response Data
Dr. Golden and his graduate students are also working on the
AUTOCODER/ASMURF project which
is a software tool which facilitates the coding of recall,
summarization, talk-aloud, and question-answering protocol data.
ARCADE HOME PAGE
This material is based upon work supported by the National Science
Foundation under Grant No. 0113669. Any opinions, findings, and
conclusions or recommendations expressed in this material are those
of the author(s) and do not necessarily reflect the views of the
National Science Foundation.