智能数学问题解决辅导系统框架

Mohamad Ariffin Abu Bakar, Ahmad Termimi Ab Ghani, Mohd Lazim Abdullah
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引用次数: 0

摘要

本研究提出了一个新颖的框架,用于重新设计智能辅导系统(ITS)中的问题解决活动,该系统被称为智能神经机制数学问题解决辅导系统(IN-MP-STS)。这篇概念论文提出了一种新的智能辅导系统方法,它将神经科学机制的元素作为一种学习策略,侧重于通过神经机制优化大脑的能力。它还引入了模糊神经网络(FNN)作为调节评估和分析结果的工具。这一框架为 ITS 模块中的授课方式和学习方法提供了另一种视角。通过有效整合动机、激活、调节、执行、记忆和互动等神经科学机制要素,可以实现深度学习,从而提高学生的能力。该框架还提出了基于 FNN 的自适应评估组件,这将增强系统中的测量和反馈模块。有必要修改智能学习系统和软计算方法(如神经网络研究)的结合方式,使学习测量和评估更加透明。这一创新尚未完全公开,因此鼓励研究人员进一步测试所提出的概念,以评估其与现有系统和伦理考虑的一致性。该框架增强了 FNN 的概念研究成果,并将基于神经科学的策略纳入了 ITS 模型中的架构和自主解决问题的技能。它还为问题解决学习的发展提供了参考。IN-MP-STS 有可能显著提高学生的能力和才干,从而促进更全面、整体和可持续的 ITS 的发展。这种方法还有可能丰富现有关于神经网络可持续性的文献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Intelligent Mathematics Problem-Solving Tutoring System Framework
This study proposed a novel framework for redesigning problem-solving activities in an intelligent tutoring system (ITS) called the intelligent neural-mechanistic mathematics problem- solving tutoring system (IN-MP-STS). This concept paper presents a new approach to ITS by incorporating elements of neuroscience mechanisms as a learning strategy that focuses on optimizing the brain’s ability through neural mechanisms. It also introduces fuzzy neural networks (FNNs) as a tool for modulating assessment and analyzing outcomes. This framework offers an alternative perspective on delivery methods and learning approaches in the ITS module. By effectively integrating neuroscience mechanistic elements such as motivation, activation, regulation, execution, memorization, and interactivities, deep learning can be achieved, leading to improved student competence. This framework also proposes an adaptive assessment component based on FNNs, which will enhance the measurement and feedback modules in the system. It is necessary to modify the way that ITS and soft computing methods, such as the study of neural networks (NNs), are combined to make learning measurement and assessment more transparent. This innovation has not been fully disclosed, so researchers are encouraged to further test the concepts presented to assess their alignment with the existing system and ethical considerations. This framework enhances the conceptual research findings of FNNs and incorporates neuroscience-based strategies into architecture and autonomous problem-solving skills within an ITS model. It also offers references for the development of problem-solving learning. IN-MP-STS has the potential to significantly enhance students’ competencies and abilities, thereby fostering the development of more comprehensive, holistic, and sustainable ITS. This approach also has the potential to enrich the existing literature on the sustainability of neural networks.
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