Ji Min, Le Jintao, Chen Bolun, Li Zhe
College of Marxist, Huaiyin Institute of Technology, Huaian, China.
College of Educational Sciences, Yangzhou University, Yangzhou, China.
Front Psychol. 2024 Jul 30;15:1434015. doi: 10.3389/fpsyg.2024.1434015. eCollection 2024.
As the application of visual-spatial skills in academic disciplines, vocational fields and daily life is becoming more and more prominent, it is of great theoretical and practical significance how to make use of big data and artificial intelligence technology to conduct research on the relationship between visual-spatial skills and students' grades. This paper explores and analyses from the perspective of artificial intelligence, combining students' visual-spatial skills and students' specific attribute characteristics to construct an expert system, which defines the prediction of academic performance as a classification problem corresponding to the five categories of excellent, good, moderate, passing, and weak, respectively, and based on which a deep neural network-based classification prediction model for students' performance is designed. The experimental results show that visual-spatial skills plays an important role in the professional learning of science and engineering students, while the classification model designed in this paper has high accuracy in the grade prediction process. This paper not only helps to fill the gaps in the current research field, but is also expected to provide scientific basis for educational practice and promote the development of the education field in a more intelligent and personalized direction.
随着视觉空间技能在学术学科、职业领域和日常生活中的应用越来越突出，如何利用大数据和人工智能技术对视觉空间技能与学生成绩之间的关系进行研究具有重要的理论和实践意义。本文从人工智能的角度进行探索和分析，结合学生的视觉空间技能和学生的具体属性特征构建专家系统，将学业成绩预测定义为分别对应优秀、良好、中等、及格和差五类的分类问题，并在此基础上设计了基于深度神经网络的学生成绩分类预测模型。实验结果表明，视觉空间技能在理工科学生的专业学习中起着重要作用，而本文设计的分类模型在成绩预测过程中具有较高的准确率。本文不仅有助于填补当前研究领域的空白，还有望为教育实践提供科学依据，推动教育领域朝着更加智能化和个性化的方向发展。
