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强大的内核极端学习机器,用于研究生学习绩效预测
Hongxing Gao1,2, Tianzi Xu3, Nan Zhang4
1Faculty of Education, Shaanxi Normal University, Xi'an, 710062, China.
Heliyon
|January 13, 2025
概括
导师与导师的关系对中国的研究生学习表现产生了重大影响. 一种新的强大的内核极端学习机器 (RK-ELM) 模型有效地预测了这种性能,考虑到入学动机和学习压力.
科学领域:
- 教育心理学教育心理学
- 机器学习应用 机器学习应用
- 高等教育研究 高等教育研究
背景情况:
- 在中国的研究生教育中,导师-导师关系至关重要,但最近的问题影响了学习质量.
- 了解导师关系,学生动机和学术压力之间的相互作用对于改善毕业生成绩至关重要.
研究的目的:
- 调查导师-导师关系如何影响研究生学习表现,通过入学动机和学习压力进行调节.
- 开发和验证一种新型的机器学习模型,对数据异常值具有稳定性,用于预测研究生学术成功.
主要方法:
- 开发了一个强大的内核极端学习机器 (RK-ELM) 模型,以处理异常数据并提高预测准确性.
- 从中国江省的873名全日制研究生中收集了数据,包括问卷结果和GPA.
- 该研究分析了导师关系的预测能力,入学动机和学习压力对学业绩的压力.
主要成果:
- 该RK-ELM模型在预测研究生学习绩效方面表现得很好.
- 导师 - 导师关系显著影响学习表现,但不是孤立的;它通过学习压力间接地起作用.
- 导师关系和入学动机的综合效应有效地预测了学习表现.
结论:
- 导师-导师关系是研究生成功的关键因素,通过学生的动机和压力进行调解.
- 拟议的RK-ELM模型提供了一个强大的方法来分析复杂的教育数据和预测学生的成绩.
- 建议采取专注于加强导师关系和管理学生压力的干预措施,以提高研究生教育质量.
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