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Regularized neuro-fuzzy AI model to aid score management in Online distance learning forums

机译:正规化的神经模糊AI模型,以帮助在线远程学习论坛中的分数管理

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This paper proposes to use an artificial intelligence (AI) model based on neuro-fuzzy techniques to aid in the automatic evaluation of notes originated from the student's interaction with their performed activities in online distance learning (ODL) forums. The evolution of non-classroom teaching allows new business opportunities and studies to emerge for the population. Some people who do not have enough time to attend traditional teaching choose distance learning to boost their tasks and money. The increasing demand for ODL courses creates challenges that are mainly aimed at automating the tasks commonly required for students' evaluation routines during their activities. For distance learning to maintain an acceptable level of costs, routine activities must be automated to reduce values related to more straightforward operations execution. In this paper, we will use a real dataset on evaluations of frequent interactions of academic in forums, allowing the obtained data to be submitted to a fuzzy neural network able to estimate the value of the student's score value according to the activities carried out by them, beyond the extraction of knowledge through fuzzy rules. Model outputs confirm that the approach may be feasible to automate the presence and participation process in ODL forums through a specialist system based on fuzzy rules. The tests performed with a resulting low RMSE of 1.37 suggest that our neuro-fuzzy-based AI approach performs better than traditional state-of-the-art regressive models.
机译:本文建议使用基于神经模糊技术的人工智能(AI)模型,以帮助自动评估来自学生在在线远程学习(ODL)论坛中的学生的互动。非课堂教学的演变允许新的商业机会和研究才能出现人口。有些人没有足够的时间参加传统教学选择远程学习,提升他们的任务和金钱。对ODL课程的日益增长的需求创造了主要旨在自动化在活动期间学生评估惯例所需的任务的挑战。对于保持可接受的成本水平的远程学习,必须自动化常规活动以减少与更直接的操作执行相关的值。在本文中,我们将在论坛中学术频繁交互的评估中使用真实数据集,允许获得的数据提交给模糊神经网络,能够根据他们执行的活动来估计学生的得分价值的价值超出了通过模糊规则提取知识的提取。模型输出确认,该方法可能是可行的,可以通过基于模糊规则的专业系统自动化ODL论坛中的存在和参与过程。由此产生的1.37的低RMSE进行的测试表明我们的神经模糊的AI方法比传统的最先进的回归模型更好。

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