Using the maximum likelihood method to estimate test complexity levels

Consideration was given to the problem of estimating the levels of complexity of the test tasks for the remote education system. It was assumed that the random responses of the subjects obey the logistic distribution and the levels of student readiness are not known in advance. An algorithm based on...

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Bibliographic Details
Published in:Automation and remote control Vol. 75; no. 4; pp. 607 - 621
Main Authors: Kibzun, A. I., Inozemtsev, A. O.
Format: Journal Article
Language:English
Published: Moscow Pleiades Publishing 01.04.2014
Subjects:
CAE) and Design
Calculus of Variations and Optimal Control; Optimization
Complexity
Computer-Aided Engineering (CAD
Concavity
Control
Logistics
Mathematical analysis
Mathematical Programming Problems
Mathematics
Mathematics and Statistics
Mechanical Engineering
Mechatronics
Remote control
Robotics
Students
Systems Theory
Task complexity
Tasks
Strict Concavity
Task Complexity
Readiness Level
Remote Control
Maximum Likelihood Method
ISSN:0005-1179, 1608-3032
Online Access:Get full text
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Summary:Consideration was given to the problem of estimating the levels of complexity of the test tasks for the remote education system. It was assumed that the random responses of the subjects obey the logistic distribution and the levels of student readiness are not known in advance. An algorithm based on the methods of maximum likelihood and Broyden-Fletcher-Goldfarb-Shanno was proposed to calculate the task complexity. Strict concavity of the logarithmic likelihood function was established, and an example was considered.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:0005-1179
1608-3032
DOI:10.1134/S000511791404002X