人工智能可以根据大学前的成绩预测临床前牙科学生的学业成绩:一项初步研究
Widya Lestari1, Adilah S Abdullah2, Afifah M A Amin2
1Department of Fundamental Dental and Medical Sciences, Kulliyyah of Dentistry, International Islamic University Malaysia, Kuantan, Malaysia.
Journal of dental education
|July 31, 2024
概括
机器学习模型和皮尔森相关系数 (PCC) 用于预测牙科学生的学业成绩. 虽然大学前的CGPA显示了一些预测能力,但没有单一的模型最终确定了成功的学生.
科学领域:
- 牙科教育 牙科教育
- 教育中的人工智能
- 预测分析是一种预测分析.
背景情况:
- 选择牙科学校申请人需要预测学术成功.
- 临床前牙科学生的表现对于整个课程的完成至关重要.
研究的目的:
- 使用机器学习 (ML) 模型预测临床前牙科学生的学术表现.
- 评估招生数据的预测能力,包括年龄,大学前CGPA和入学学期.
- 用皮尔森相关系数 (PCC) 评估大学前CGPA和牙科学校成绩之间的相关性.
主要方法:
- 应用的ML算法:逻辑回归 (LR),决策树 (DT),随机森林 (RF) 和支持向量机器 (SVM).
- 使用的入学参数:年龄,大学前累积成绩平均值 (CGPA) 和总入学学期.
- 雇佣PCC来确定大学前CGPA和牙科学校成绩之间的关系.
主要成果:
- 模型分类准确度在29%到57%之间,RF显示最高准确度.
- 大学前CGPA是一个预测指标,但单独不足以进行最佳预测.
- 射频在预测A,B和C等级方面表现出色;LR在预测故障方面表现出最高的回忆力.
结论:
- 可以应用ML算法和PCC来预测牙科学生的学业成绩.
- 每个算法都有不同的性能特征,需要考虑指标之间的权衡.
- 没有一个单一的ML模型能够在这个群体中预测学术成功的普遍优势.
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