规范化的多路径XSENet集成器,用于提高高等教育学生绩效预测
Eman Ali Aldhahri1, Abdulwahab Ali Almazroi2, Nasir Ayub3
1Department of Computer Science and Artificial Intelligence, Collage of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia.
PeerJ. Computer science
|September 24, 2025
概括
本研究介绍了XSEJNet,这是一种用于预测学生成绩的新型混合模型. XSEJNet实现了97.98%的准确性,为教育数据挖掘提供了一个可扩展的解决方案.
科学领域:
- 教育数据挖掘教育数据挖掘
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 越来越多的教育数据需要先进的预测模型来进行机构规划和学生支持.
- 预测学生表现 (低,中,高) 对于及时干预至关重要.
研究的目的:
- 开发和评估XSEJNet,这是一种用于准确预测学生成绩的新型混合模型.
- 提高教育数据挖掘中的预测准确性和计算效率.
主要方法:
- 拟议的XSEJNet:一种混合模型,将ResNeXt架构与挤压激发 (SE) 注意力机制集成在一起.
- 使用Jaya优化算法进行超参数调整.
- 分析结构化和非结构化学术数据以捕捉高维特征.
主要成果:
- XSEJNet的预测准确率达到了97.98%.
- 超过了传统的机器学习和先进的技术,如RLCHI,AEO-XGBoost,Conv-DL和DualGNN.
- 与现有方法相比,证明了更快的融合和更低的计算开销.
结论:
- XSEJNet为现实世界的教育环境提供了一个可扩展和实用的解决方案.
- 支持早期干预,增强电子学习平台,并为机构决策提供信息.
- 为开发包容性,数据驱动和可持续的学术系统做出贡献.
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