A Systematic Model of an Adaptive Teaching, Learning and Assessment Environment Designed Using Genetic Algorithms

The educational assessment is an essential task within the educational process. The generation of right and correct assessment content is a determinant process within the assessment. The creation of an automated method of generation similar to a human experienced operator (teacher) deals with a comp...

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Vydáno v:Applied sciences Ročník 15; číslo 7; s. 4039
Hlavní autoři: Popescu, Doru Anastasiu, Bold, Nicolae, Stefanidakis, Michail
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.04.2025
Témata:
Algorithms
assessment item
assessment test
Computational linguistics
Customization
Education
educational assessment
educational content
Educational evaluation
genetic algorithm
Genetic algorithms
Language processing
Learning management systems
Methods
Natural language interfaces
Teaching
tree
ISSN:2076-3417, 2076-3417
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Shrnutí:The educational assessment is an essential task within the educational process. The generation of right and correct assessment content is a determinant process within the assessment. The creation of an automated method of generation similar to a human experienced operator (teacher) deals with a complex series of issues. This paper presents a compiled set of methods and tools used to generate educational assessment content in the form of assessment tests. The methods include the usage of various structures (e.g., trees, chromosomes and genes, and genetic operators) and algorithms (graph-based, evolutionary, and genetic) in the automated generation of educational assessment tests. This main purpose of the research is developed in the context of the existence of several requirements (e.g., degree of difficulty, item topic), which gives a higher degree of complexity to the issue. The paper presents a short literature review related to the issue. Next, the description of the models generated in the authors’ previous research is presented. In the final part of the paper, the results related to the implementations of the models are presented, as well as results and performance. Several conclusions were drawn based on this compilation, the most important of them being that tree and genetic-based approaches to the issue have promising results related to performance and assessment content generation.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app15074039