Abstract
Artificial intelligence and especially Multi-Agent systems play a very important role within the technologies that have been applied to improve learning since they seek for the personalization and automation of this process. The literature reports important works, most of them focused on the instructional process or addressed to the virtual educational resource delivery. However, as a reflection of the traditional education situation, the assessment component and more specifically the adaptive assessment have not been given the same relevance within the learning process. Based on the premise that learning assessment is a fundamental component, which seeks to provide feedback from learning outcomes, it is proposed an assessment system that adapts to the specific conditions of each student. To do so, the system is able to recognize the academic differences and the learning style of the apprentices by modeling through a Multi-Agent System. In addition, this approach takes benefit from the great distribution advantages granted by this artificial intelligence technique. The preliminary validation of the system shows the possibilities that are open by using this kind of approaches.
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Acknowledgments
The research was partially funded by the project “Fortalecimiento docente desde la alfabetización mediática Informacional y la CTel, como estrategia didáctico-pedagógica y soporte para la recuperación de la confianza del tejido social afectado por el conflicto” code SIGP 58950 of the program “Reconstrucción del tejido social en zonas de pos-conflicto en Colombia” whit code SIGP 57579 supported by Fondo Nacional de Financiamiento para la Ciencia, la Tecnología y la Innovación, Fondo Francisco José de Caldas whith contract 213-2018.
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Duque-Méndez, N.D., Tabares-Morales, V., Ovalle, D.A. (2020). Intelligent Agents System for Adaptive Assessment. In: Gennari, R., et al. Methodologies and Intelligent Systems for Technology Enhanced Learning, 9th International Conference. MIS4TEL 2019. Advances in Intelligent Systems and Computing, vol 1007 . Springer, Cham. https://doi.org/10.1007/978-3-030-23990-9_20
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DOI: https://doi.org/10.1007/978-3-030-23990-9_20
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