一个学生学业绩预测模型,基于间隔信念规则的基础
Wenkai Zhou1, Yunsong Li1, Jiaxing Li1
1School of Computer Science and Information Engineering, Harbin Normal University, Harbin, 150025, China.
Scientific reports
|August 20, 2025
概括
本研究引入了一种改进的间隔信念规则基础 (IBRB-C) 模型,用于学生绩效预测 (SPP). IBRB-C模型有效地解决了传统信念规则基础系统中的挑战,证明了卓越的预测准确性.
科学领域:
- 教育数据挖掘教育数据挖掘
- 教育中的人工智能
- 机器学习为学生的成功
背景情况:
- 学生绩效预测 (SPP) 对于及时进行教育干预至关重要.
- 传统的信念规则基础 (BRB) 模型面临诸如属性爆炸和有限的专家知识集成等挑战.
- 开发强大的SPP模型对于改善教育成果至关重要.
研究的目的:
- 为了提出一个新的SPP模型,以随机森林属性选择 (IBRB-C) 的间隔信念规则为基础.
- 提高学生成绩预测的准确性和效率.
- 克服传统BRB模型在处理复杂数据集和专家知识方面的局限性.
主要方法:
- 使用随机森林 (RF) 进行属性选择以减轻属性爆炸.
- 采用间隔BRB结构来管理预测中的不确定性.
- 整合专家知识与Kmeans++算法进行参数确定.
- 使用P-CMA-ES算法优化IBRB-C模型.
主要成果:
- 拟议的IBRB-C模型实现了毕业生申请的0.0024和GPA的0.1014的平均平方误差 (MSE).
- 废弃实验验证了IBRB-C方法的有效性和合理性.
- 对比实验证实了IBRB-C模型对现有方法的优越性.
结论:
- IBRB-C模型在学生绩效预测方面取得了重大进展.
- 这种方法有效地平衡了专家知识和数据驱动的属性选择.
- 该模型的高精度证明了其在教育环境中实际应用的潜力.
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