A predictive model for classifying college students' academic performance based on visual-spatial skills

TL;DR

This paper creates a model to predict college students' academic performance using their visual-spatial skills and personal attributes.

Contribution

A novel deep neural network model is proposed to classify academic performance based on visual-spatial skills and student attributes.

Findings

Visual-spatial skills significantly influence the academic performance of science and engineering students.

The proposed model achieves high accuracy in predicting academic performance categories.

The model contributes to personalized and intelligent educational practices.

Abstract

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.

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