Student’s performance prediction based on an improved multi-view hypergraph neural network

IF 1.7 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2024-03-15 DOI:10.1080/0952813x.2024.2328234

Xuefen Lin, Yada Guo

引用次数: 0

Abstract

Predicting students’ academic performance is a crucial area of research in Educational Data Mining. Efficient performance prediction can significantly improve instructional effectiveness, facilitat...

查看原文本刊更多论文

基于改进的多视图超图神经网络的学生成绩预测

预测学生的学习成绩是教育数据挖掘的一个重要研究领域。高效的成绩预测能极大地提高教学效果，促进学生学习成绩的提高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Experimental & Theoretical Artificial Intelligence 工程技术-计算机：人工智能

CiteScore

6.10

自引率

4.50%

发文量

审稿时长

>12 weeks

期刊介绍： Journal of Experimental & Theoretical Artificial Intelligence (JETAI) is a world leading journal dedicated to publishing high quality, rigorously reviewed, original papers in artificial intelligence (AI) research. The journal features work in all subfields of AI research and accepts both theoretical and applied research. Topics covered include, but are not limited to, the following: • cognitive science • games • learning • knowledge representation • memory and neural system modelling • perception • problem-solving