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Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Decision Making01:20

Decision Making

Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...

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Updated: Jun 24, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

一种基于机器学习的方法来告知学生入学决策.

Tuo Liu1, Cosima Schenk1, Stephan Braun1

  • 1Institute of Psychology, Goethe University Frankfurt, 60323 Frankfurt am Main, Germany.

Behavioral sciences (Basel, Switzerland)
|March 28, 2025
PubMed
概括

本研究引入了用于大学招生的机器学习方法,通过计算统计不确定性来改善招生预测. 这种数据驱动的方法优化了申请人选择,与传统方法相比,减少了过度和不足的招生风险.

科学领域:

  • 高等教育管理的管理.
  • 教育中的数据科学教育中的数据科学
  • 预测分析是一种预测分析.

背景情况:

  • 大学招生面临着高申请量和有限的学习地点的挑战,需要战略管理.
  • 使用历史入学收益率的传统方法忽略了统计不确定性,导致最佳的入学决策和潜在的过度或不足入学.

研究的目的:

  • 开发和评估一种基于机器学习的新型方法,以优化学生入学决策.
  • 通过纳入统计不确定性来提高招生预测的准确性.

主要方法:

  • 在历史大学申请数据上训练和比较多个机器学习模型.
  • 开发了一个模型来预测注册申请人有条件,考虑到统计不确定性.
  • 应用最好的模型来估计个人入学概率,并将其汇总起来,以预测总入学和相关风险.

主要成果:

  • 拟议的机器学习方法在传统方法上表现出优越的性能.
  • 启用数据驱动调整被录取申请人的数量.
  • 有效控制过度和不足入学的风险.

结论:

  • 基于机器学习的方法为战略学生招生管理提供了更强大的数据驱动解决方案.
关键词:
招生简章 招生简章招生管理 招生管理招生收益率 招生收益率机器学习是机器学习.预测建模预测建模统计不确定性 统计不确定性

相关实验视频

Last Updated: Jun 24, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

  • 这种方法提高了入学预测的准确性,导致更有效的资源分配和改善学生入学结果.