## A decision structure for teaching machines |

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### Contents

THE TEACHING MACHINE | 1 |

THE BASIC CONFIGURATION | 10 |

RESPONSE PROBABILITY ESTIMATION MODELS | 28 |

Copyright | |

4 other sections not shown

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### Common terms and phrases

a's and p's approximation assumed average error Bayes estimates beta distribution beta model block question branching network calculated Chapter coin probabilities coin-tossing coin-tossing problem column concept confidence intervals converge correct answer course data array decision criterion decision process decision structure density function dent dependent device equal error per question estimate the probability estimation methods estimation model example expected value fraction of correct incorrectly information blocks intrinsic programming intuitive model inverse function Iterative j**1 question likelihood function log f(x maximizes maximum-likelihood estimate microfilm nonblank entries normally distributed parameters particular past students path phototransistor possible posteriori distribution present priori distribution probability density probability density function probability distribution probability estimate problem process with experience question correctly random variable relay response array sample sense lines student's answer student's expected student's past history teaching machine program teaching system test questions timate tion true value unit impulse