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相关概念视频

Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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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...
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Decision Making: P-value Method01:09

Decision Making: P-value Method

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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...
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Response Surface Methodology01:16

Response Surface Methodology

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Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
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Self-Evaluation Maintenance Model01:29

Self-Evaluation Maintenance Model

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The Self-Evaluation Maintenance (SEM) model offers a psychological framework to understand how individuals’ self-esteem is influenced by the achievements of others, particularly those with whom they share close personal bonds. The SEM model operates when personal rather than social identity guides individuals. Central to this model is the notion that individuals have an inherent desire to preserve a favorable self-image, which is continuously shaped by interpersonal comparisons and...
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Data Validation01:03

Data Validation

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
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Reliability and Validity01:29

Reliability and Validity

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Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
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相关实验视频

Updated: Jan 14, 2026

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
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用模糊的德尔菲方法验证和优先考虑混合MOOC在英语学习中的实施关键指标.

Anh Tuan Pham1,2, Muhammad Ridhuan Tony Lim Abdullah1

  • 1Department of Management, Universiti Teknologi PETRONAS, Perak, Malaysia.

MethodsX
|October 27, 2025
PubMed
概括

这项研究验证了20个关键指标,用于混合大规模开放在线课程 (bMOOCs) 在英语语言学习使用的德尔菲方法 (FDM). 这为评估英语作为外语 (EFL) 的bMOOCs提供了一个结构化的框架.

关键词:
混合学习是一种混合学习.在 EFL 中使用 EFL 的教程.模糊的德尔菲分析.这些指标是指标,指标是指标.大规模的开放在线课程

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科学领域:

  • 教育技术的教育技术
  • 应用语言学 应用语言学
  • 决策科学 决策科学 决策科学

背景情况:

  • 大规模开放在线课程 (MOOCs) 在高等教育中越来越多地被采用,特别是混合MOOCs (bMOOCs).
  • 在英语作为外语 (EFL) 语境下,对于bMOOCs缺乏标准化的评估标准,这阻碍了有效的实施.
  • 随着COVID-19的流行,在线和混合学习环境的强大框架的需求加速了.

研究的目的:

  • 开发和验证一种结构化的方法,用于识别和优先考虑bMOOC在EFL中的实施的关键指标.
  • 建立专家对有效设计和评估bMOOC的基本子指标的共识.
  • 解决EFL混合学习标准化评估标准的现有差距.

主要方法:

  • 为了达到专家的共识,采用了八步过程 - - 模糊的德尔菲方法 (FDM).
  • 一个由15名专家组成的小组评估了6个预定义领域的21个拟议的子指标.
  • 模糊逻辑被用来处理不确定性,并加强专家之间的决策.

主要成果:

  • 建立了一套经过验证的20个关键子指标,用于在英语学习中实施bMOOC.
  • 这些指标被系统地分为六个领域:学习者分析,目标,材料和技术,教学方法,学习者参与和评估.
  • 在开发评估框架时,FDM过程证明了透明度和可复制性.

结论:

  • 经过验证的指标集为EFL bMOOCs的教学设计,政策制定和质量保证提供了基础框架.
  • 这种结构化方法提高了混合在线学习环境的评估框架的可靠性和有效性.
  • 这些发现支持在英语语言教育中系统地实施和不断改进bMOOC.