What is it about?
Learning paths are important because they enable the personalization and optimization of the learning process. This paper introduces Artificial Intelligence Planning Models for generating them. The resulting models consider a rich set of properties from the education domain. We evaluate such models using mathematical programming, providing a first time analysis of their properties and their impact on solution synthesis.
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Why is it important?
We present a flexible conceptual framework that allows the representation of curricula information as Artificial Intelligence Planning and Mathematical Programming models to facilitate the generation of learning paths by domain independent algorithms. We expect that the results of this research can be important to education researchers and computer scientists in the quest of scalable systems that capture more flexible standards to model learning and compute more informed learning paths for students.
Perspectives
I hope this article could lead to strong collaborations between computer scientists and educators. We need more informed learning paths to students if we want to have a positive impact in their learning process. Things like emotions, student engagement, learning styles, and so on, are important; and we need approaches that can model them. Therefore, I hope this article could ignite some conversation on learning path generation between interdisciplinary research communities in the near future.
Romeo Sanchez
Universidad Autonoma de Nuevo Leon
Read the Original
This page is a summary of: Design and evaluation of planning and mathematical models for generating learning paths, Computational Intelligence, August 2017, Wiley,
DOI: 10.1111/coin.12134.
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