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  • Enhancing knowledge integration from multiple experts to guiding personalized learning paths for testing and diagnostic systems

Sección IA: Inteligencia artificial Bibliografía

Enhancing knowledge integration from multiple experts to guiding personalized learning paths for testing and diagnostic systems

Dechawut Wanichsan
Patcharin Panjaburee
Sasithorn Chookaew
2021
Computers & Education: Artificial Intelligence
2
https://www.sciencedirect.com/science/a…
artículo
estudio experimental
estudio empírico
inteligencia artificial
tecnología educativa
evaluación
educación superior
IA y evaluación
IA y aprendizaje
estudio empírico

Texto completo

The testing and diagnostic systems have been considered to be useful for students because they can point to their learning problems to provide helpful suggestions for improving the knowledge. The previous approach shows that using the knowledge from multiple experts can develop a testing and diagnostic system that provides more accurate suggestions for learners comparing to the system using a single expert. Nevertheless, the low-quality knowledge integration method of the multi-expert approach can provide some inaccurate learning suggestions. This work proposes a practical method for enhancing knowledge integration from multiple experts to provide more effective learning suggestions. An experiment has been conducted on freshmen in university to evaluate the effectiveness of the proposed method. The results show that the proposed approach not only improves the learning achievements of the students but also decreases the number of reconsidering cases.

Texto completo en abierto (CC BY-NC-ND 4.0).
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