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Intelligent machine for ontological representation of massive pedagogical knowledge based on neural networks

机译：基于神经网络的大规模教学知识的本体论代表智能机器

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Higher education is increasingly integrating free learning management systems (LMS). The main objective underlying such systems integration is the automatization of online educational processes for the benefit of all the involved actors who use these systems. The said processes are developed through the integration and implementation of learning scenarios similar to traditional learning systems. LMS produce big data traces emerging from actors’ interactions in online learning. However, we note the absence of instruments adequate for representing knowledge extracted from big traces. In this context, the research at hand is aimed at transforming the big data produced via interactions into big knowledge that can be used in MOOCs by actors falling within a given learning level within a given learning domain, be it formal or informal. In order to achieve such an objective, ontological approaches are taken, namely: mapping, learning and enrichment, in addition to artificial intelligence-based approaches which are relevant in our research context. In this paper, we propose three interconnected algorithms for a better ontological representation of learning actors’ knowledge, while premising heavily on artificial intelligence approaches throughout the stages of this work. For verifying the validity of our contribution, we will implement an experiment about knowledge sources example.
机译：高等教育越来越多地整合自由学习管理系统（LMS）。这些系统集成的主要目标是在线教育流程的自动化，从而为使用这些系统的所有涉及的演员的利益。所述过程是通过与传统学习系统类似的学习场景的集成和实现来开发的。 LMS产生从在线学习中的演员的交互出现的大数据痕迹。但是，我们注意到没有足够的仪器代表大迹线提取的知识。在这种情况下，手头的研究旨在将通过交互转换为大知识产生的大数据，该知识可以通过落在给定的学习域内的给定学习级别内的演员在MoOC上使用，成为正式或非正式的。为了实现这种目标，除了在我们的研究背景相关的人工智能的方法之外，还采取了本体的方法，即：映射，学习和富集，除了基于人工智能的方法。在本文中，我们提出了三种互连的算法，以获得学习演员知识的更好本体论代表性，而在整个工作阶段的阶段大量对人工智能方法进行预订。为了验证我们贡献的有效性，我们将实施关于知识来源示例的实验。

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