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相关概念视频

Prediction Intervals01:03

Prediction Intervals

3.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Student t Distribution01:31

Student t Distribution

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The population standard deviation is rarely known in many day-to-day examples of statistics. When the sample sizes are large, it is easy to estimate the population standard deviation using a confidence interval, which provides results close enough to the original value. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
The Student t distribution was developed by William S. Goset (1876–1937) of the...
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相关实验视频

Updated: Jan 17, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

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

Published on: December 15, 2023

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学生绩效预测和隐私保护的透适应差异隐私联合学习:Python编程中的一个案例研究.

Shanwei Chen1, Xiuzhi Qi2

  • 1College of Education, Baoji University of Arts and Sciences, Baoji, China.

Frontiers in artificial intelligence
|September 24, 2025
PubMed
概括

在工程教育中,保护学生数据隐私至关重要. 输入适应差异隐私联合学习 (EADP-FedAvg) 方法提高了学生绩效预测的准确性,同时保护了敏感的教育数据.

关键词:
在Python编程中使用Python编程.分布式数据分析分布式数据分析适应差异隐私隐私 适应差异隐私联合学习的联合学习预测学生的表现 预测学生的表现

相关实验视频

Last Updated: Jan 17, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

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

Published on: December 15, 2023

5.3K

科学领域:

  • 教育技术的教育技术
  • 数据 隐私 数据 隐私 数据
  • 机器学习 机器学习

背景情况:

  • 工程教育的数字化转型需要强有力的数据隐私措施.
  • 数据驱动的教学需要平衡学生绩效分析与隐私保护.
  • 现有的联合学习方法可能无法充分解决教育数据中的隐私问题.

研究的目的:

  • 提出一种透适应差异隐私联合学习 (EADP-FedAvg) 方法.
  • 为了提高学生绩效预测的准确性,同时确保数据隐私.
  • 解决工程课程中分析隐私敏感教育数据的挑战.

主要方法:

  • 利用了电子工程学生的Python编程课程的在线测试记录.
  • 实现了一个多层感知器 (MLP) 模型,使用10个分布式客户端进行联合学习.
  • 应用了EADP-FedAvg提出的方法,使用不同的隐私预算 (ε = 0.1,1e-6,1.0).

主要成果:

  • EADP-FedAvg的测试准确率为92.7%,宏观平均得分为92.1%.
  • 该方法的值为0.207,表明有效的隐私保护.
  • 性能超过了标准的联合学习,接近集中式学习的准确性.

结论:

  • 通过根据输出调整噪音水平,EADP-FedAvg有效地平衡了隐私保护和模型准确性.
  • 提出的方法为分析隐私敏感的教育数据提供了一种新且有效的解决方案.
  • 这种方法支持数据驱动的教学在工程教育,而不会影响学生的隐私.