在混合智能学习环境的框架下,发展对学生数学知识评估的工具和方法支持

O. Druzhinina, I. A. Karpacheva, O. Masina, A. Petrov
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引用次数: 0

摘要

本文致力于开发一个综合知识库和人工智能工具,用于在混合智能学习环境的框架内评估学生的数学知识。根据小学生数学知识智力评价模型和创造潜力模型的结构图,形式化了相应的知识库结构。在数据挖掘方法的框架内开发了知识库,创建了一个数学知识监控和评估模块。作为知识库发展的方法论基础,V.P. Bespalko关于以连续同化水平的形式表示人类活动的可能结构的思想,反映了学生在学习过程中对给定学科的经验的发展。同化水平(学生、典型、启发式、研究)是通过解决相应类型问题的能力来决定的。同化系数是衡量学生对教育材料同化程度(训练水平)的形式化指标;对某一特定主题或章节的材料同化程度的评估,在上述每一个级别上都有规定,并且在一般情况下也有规定。本文考虑了为评估小学生代数技能和能力而建立知识库的例子。为了模拟教育过程,我们提出了基于机器学习和人工神经网络的聚类、降维和分类问题的解决方法。提出了开发集成知识库和人工智能工具的基础,以确保教育过程。所获得的结果旨在创造方法,提供操作学习过程,知识,能力和程序的控制和评估,学生在信息和教育知识环境中的学科和专业能力的形成水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DEVELOPMENT OF INSTRUMENTAL AND METHODOLOGICAL SUPPORT FOR THE ASSESSMENT OF STUDENTS ' KNOWLEDGE IN MATHEMATICS IN THE FRAMEWORK OF HYBRID INTELLIGENT LEARNING ENVIRONMENT
The article is devoted to the development of an integrated complex of knowledge base and artifi-cial intelligence tools for assessing students' knowledge in mathematics within the framework of a hybrid intellectual learning environment. In accordance with the structural diagrams of mod-els of intellectual assessment of knowledge and creative potential of schoolchildren in mathe-matics, the structure of the corresponding knowledge base is formalized. The knowledge base is developed within the framework of data mining methods to create a module for monitoring and assessing knowledge in mathematics. As a methodological basis for the development of a knowledge base, the idea of V.P. Bespalko about the representation of the possible structure of human activity in the form of suc-cessive levels of assimilation, reflecting the development of the student's experience in a given subject in the learning process. The levels of assimilation (student, typical, heuristic, research) are determined through the ability to solve problems of the corresponding type. A formalized in-dicator of a student's achievement of a particular level of assimilation of educational material (level of training) is the coefficient of assimilation; assessment of the assimilation of material on a specific topic or section is provided for both at each of the above levels, and in general. The examples of the formation of a knowledge base for assessing the skills and abilities of schoolchildren in algebra are considered. To model the educational process, we propose methods for solving clustering, dimen-sionality reduction and classification problems based on machine learning and artificial neural networks. The foundations for the development of integrated complexes of knowledge bases and artificial intelligence tools to ensure the educational process are presented. The results obtained are aimed at creating methods that provide the processes of operational learning, control and assessment of knowledge, competencies and procedures, the level of formation of subject and professional competencies of students in the information and educational intellectual environment.
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