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相关概念视频

Group Design02:01

Group Design

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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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Stratified Sampling Method01:16

Stratified Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
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Surveys02:16

Surveys

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Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
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Random Sampling Method01:09

Random Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Cross-Sectional Research01:50

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In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
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一种基于机器学习的方法,用于构建大学学生的群组配置文件.

Ran Song1,2, Fei Pang3, Hongyun Jiang1

  • 1School of Mathematics, Physics and Information, Shaoxing University, Shaoxing, Zhejiang, 312000, China.

Heliyon
|April 11, 2024
PubMed
概括

本研究开发了一种使用K-means集群和反向传播神经网络的新型学生概况模型,在分类四个不同的学生概况方面达到90.22%的准确性,以增强教育发展.

关键词:
分类预测预测的分类.集团概况分析 集团概况分析K-表示集群.神经网络的神经网络的神经网络调查采用问卷调查调查.

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科学领域:

  • 教育技术的教育技术
  • 教育中的数据科学教育中的数据科学
  • 高等教育研究 高等教育研究

背景情况:

  • 高等教育的数字化转型需要先进的学生概况.
  • 现有的学生分析方法往往缺乏全面性,依赖于单一的数据来源.
  • 大数据技术使学生教育发展的复杂分析成为可能.

研究的目的:

  • 使用问卷数据构建一个预测性的学生分析模型.
  • 通过改进学生分类,提高教育问卷的有效性.
  • 通过采用全面的多属性方法来解决先前研究的局限性.

主要方法:

  • 利用问卷调查收集各种学生数据.
  • 应用K-means集群算法用于初始学生数据分组.
  • 开发了一种使用反向传播神经网络进行分类的分类预测模型.

主要成果:

  • 确定了四种不同的学生个人资料:勤奋的学习者,认真的个人,有洞察力的成就者和道德倡导者.
  • 根据已识别的个人资料,成功标记学生组.
  • 为预测模型实现了高分类准确率90.22%.

结论:

  • 开发的模型提供了一种新且有效的方法,用于大学生简介.
  • 这些发现为教育研究人员和机构提供了有价值的方法参考.
  • 这项研究增强了基于问卷的方法在高等教育中的有用性.