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相关概念视频

Prediction Intervals01:03

Prediction Intervals

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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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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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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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Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

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In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
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Regression Toward the Mean01:52

Regression Toward the Mean

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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What is a Mode?01:07

What is a Mode?

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The mode is one of the commonly used measures of a central tendency. It is defined as the most frequent value in a data set.
There can be more than one mode in a data set if multiple values have the same highest frequency. For instance, suppose that the Statistics exam scores of 20 students are: 50; 53; 59; 59; 63; 63; 72; 72; 72; 72; 72; 76; 78; 81; 83; 84; 84; 84; 90; 93. Here, the mode is 72, as it occurs most frequently, five times.
A data set with two modes is called bimodal. For example,...
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相关实验视频

Updated: Sep 11, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
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通过使用多式联网数据,评估用于高等教育中公平和可解释的预测的集合模型.

Felipe Emiliano Arévalo-Cordovilla1,2, Marta Peña3

  • 1Faculty of Science and Engineering, Universidad Estatal de Milagro, Ciudadela Universitaria "Dr. Rómulo Minchala Murillo", km. 1.5 vía Milagro - Virgen de Fátima, Milagro, 091050, Ecuador. farevaloc@unemi.edu.ec.

Scientific reports
|August 11, 2025
PubMed
概括

这项研究使用Moodle数据开发了一种在线学生成功的预测模型. 早期成绩是关键预测因素,使早期干预能够减少高等教育的消耗.

关键词:
学业成绩学术成绩的表现早期预测预测的早期预测组合模型模型组合模型梯度增强可以提高梯度.学习分析学习分析.堆叠堆叠 在堆叠堆叠

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科学领域:

  • 学习分析学习分析
  • 教育数据挖掘教育数据挖掘
  • 高等教育研究 高等教育研究

背景情况:

  • 在线高等教育面临的挑战是学生缩.
  • 现有的预测模型往往缺乏全面的数据整合和与先进技术的比较.
  • 需要强大,可解释和公平的模型来预测学业绩的早期预测.

研究的目的:

  • 开发和评估用于在线工程课程中早期识别有风险的学生的预测框架.
  • 整合各种数据源,包括Moodle交互,学术历史和人口统计.
  • 为了比较各种机器学习模型的性能,并确定学术成功的关键预测因素.

主要方法:

  • 利用了2225名工程学生的数据,包括Moodle日志,学术记录和人口统计数据.
  • 应用SMOTE (合成少数人过量采样技术) 进行类平衡.
  • 评估了7个基础学习者 (包括随机森林,XGBoost,LightGBM) 和使用5倍分层交叉验证的堆叠组合.
  • 为了模型的可解释性,使用了SHAP (夏普利添加式解释) 分析.

主要成果:

  • 作为基本模型,LightGBM表现出优异的性能 (AUC=0.953,F1=0.950).
  • 堆叠组合没有显著改善性能,并且显示不稳定.
  • 早期的学业成绩被确定为顶级模型中最有影响力的预测因素.
  • 最终的模型在人口群体之间表现出强烈的公平性 (一致性=0.907).

结论:

  • 开发的模型有效地预测在线工程学生的学术表现.
  • 早期成绩是学生成功的关键指标,通知及时干预.
  • 该研究提供了学习分析的最新,可解释和公平的模型,帮助机构减少磨损.