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Please use this identifier to cite or link to this item: http://dspace.vgtu.lt/handle/1/3914

Title: Computer Adaptive Testing Using Upper-Confidence Bound Algorithm for Formative Assessment
Authors: Meleško, Jaroslav
Novickij, Vitalij
Keywords: formative assessment
Upper-Confidence Bound algorithm
Multi-Armed Bandit algorithm
e-Learning
intelligent tutoring systems
education
Issue Date: 2019
Publisher: MDPI
Citation: Melesko, J.; Novickij, V. Computer Adaptive Testing Using Upper-Confidence Bound Algorithm for Formative Assessment. Appl. Sci. 2019, 9, 4303.
Series/Report no.: 9;20
Abstract: There is strong support for formative assessment inclusion in learning processes, with the main emphasis on corrective feedback for students. However, traditional testing and Computer Adaptive Testing can be problematic to implement in the classroom. Paper based tests are logistically inconvenient and are hard to personalize, and thus must be longer to accurately assess every student in the classroom. Computer Adaptive Testing can mitigate these problems by making use of Multi-Dimensional Item Response Theory at cost of introducing several new problems, most problematic of which are the greater test creation complexity, because of the necessity of question pool calibration, and the debatable premise that different questions measure one common latent trait. In this paper a new approach of modelling formative assessment as a Multi-Armed bandit problem is proposed and solved using Upper-Confidence Bound algorithm. The method in combination with e-learning paradigm has the potential to mitigate such problems as question item calibration and lengthy tests, while providing accurate formative assessment feedback for students. A number of simulation and empirical data experiments (with 104 students) are carried out to explore and measure the potential of this application with positive results.
Description: This article belongs to the Special Issue Smart Learning
URI: http://dspace.vgtu.lt/handle/1/3914
ISSN: 2076-3417
Appears in Collections:Moksliniai straipsniai / Research articles

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