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Multiple Regression01:25

Multiple Regression

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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相关实验视频

Updated: Jan 10, 2026

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students
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Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

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一个多因素机器学习框架,用于预测和分析学生的学业表现,使用行为,财务和可穿戴数据.

A L Akash Devaraje Urs1, Akshay Sudharshan1

  • 1Amrita Vishwa Vidyapeetham Mysuru, India.

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|November 26, 2025
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概括
此摘要是机器生成的。

这项研究使用机器学习来通过分析生活方式,财务和可穿戴设备数据来预测学生的学业表现. 它可以识别有风险的学生,并为高等教育的早期干预创建个人资料.

关键词:
在CGPA预测预测.高等教育中的机器学习学生的学术表现预测预测在学习分析中的可穿戴技术.

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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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科学领域:

  • 教育数据挖掘教育数据挖掘
  • 机器学习在教育中的应用
  • 学生表现分析 学生表现分析

背景情况:

  • 学术成功受到传统指标之外的多方面的因素的影响.
  • 整合多样化的数据来源提供了关于学生福祉和表现的整体观点.
  • 现有的预测模型往往缺乏全面的数据集成.

研究的目的:

  • 开发和验证用于预测学生学业绩 (CGPA) 的机器学习框架.
  • 用行为,财务和可穿戴数据将学生分为不同的学术和压力风险类别.
  • 为教育机构建立一个可解释和可重复使用的预测分析管道.

主要方法:

  • 数据预处理和功能工程,包括创建金融压力和复合压力指数.
  • 基准测试多重回归模型 (随机森林实现R2 ≈ 0.30) 来预测CGPA.
  • 采用无监督的集群 (K-Means,聚合) 进行学生细分和分析可穿戴数据的相关性.

主要成果:

  • 随机森林模型在预测CGPA方面表现出最高的准确性.
  • 确定了生活方式,财务和生理数据与学术成果之间的重要关系.
  • 成功地将学生分成可解释的学术和压力风险概况.

结论:

  • 拟议的多因素机器学习框架有效预测学生的学业成绩.
  • 学生分析有助于早期发现有风险的个人,以便及时提供支持.
  • 可适应的蓝图支持教育机构实施先进的预测分析.