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Multidisciplinary Educational Assessment Model Using Genetic Algorithms
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DOI：10.1007/978-3-031-98284-2_9.png)
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Abstract
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This paper presents an interdisciplinary assessment method using items grouped according to the disciplines targeted to identify student competencies. For each subject, a weight is used depending on the requirements of the assessor. The model we present uses different databases, one for each subject used for assessment, and the selection of items for assessment is done using genetic algorithms. The model implementation is done using the Python language. Based on the results, the genetic algorithm efficiently optimizes the generation of multidisciplinary tests and its performance can be improved by dynamically adapting parameters and diversifying solutions to avoid stagnation.
Keywords:
genetic algorithm
modelling
assessment
multidisciplinary
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