Stochastic decision model for arithmetic programming.
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Authors
Haque, Mohammad Zia-Ul
Subjects
Markovian decision model
branching criteria
all-or-none learning theory
programmed learning
probabilistic decision model
model for arithmetic programming
stochastic model
arithmetic learning
advancement criteria
branching criteria
all-or-none learning theory
programmed learning
probabilistic decision model
model for arithmetic programming
stochastic model
arithmetic learning
advancement criteria
Advisors
Arima, J.K.
Date of Issue
1976
Date
March 1976
Publisher
Monterey, California. Naval Postgraduate School
Language
en_US
Abstract
Few if any validated guidelines exist for making decisions about the
design, media, or format of new instructional products. This study examined
strings of programmed learning responses to create general guidelines for
making such decisions. Using a Markov model, tables were developed relating
the expected proportion of students to be in a solution state at a given
accuracy level and at a given level of confidence with respect to the
length of response strings.
Type
Thesis
Description
Series/Report No
Department
Operations Analysis
Organization
Naval Postgraduate School
Identifiers
NPS Report Number
Sponsors
Funder
Format
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
Copyright is reserved by the copyright owner.