通过创新的贝叶斯式问卷验证方法验证混合学习可用性评估问卷 (BLUE-Q)
Anish Kumar Arora1,2, Charo Rodriguez1,3, Tamara Carver3
1Family Medicine Education Research Group, Department of Family Medicine, Faculty of Medicine & Health Sciences, McGill University, Montréal, QC, Canada
Journal of educational evaluation for health professions
|November 6, 2024
概括
这项研究验证了使用贝叶斯方法的混合学习可用性评估问卷 (BLUE-Q),简化了卫生专业教育研究. 该过程有效地完善了问卷,使验证更容易获得.
科学领域:
- 健康 专业 教育 卫生 专业 教育
- 教育技术的教育技术
- 贝叶斯统计学贝叶斯统计学
背景情况:
- 验证评估工具至关重要,但往往复杂且资源密集.
- 混合学习可用性评估问卷 (BLUE-Q) 要求对健康专业教育进行验证.
- 贝叶斯对问卷验证的方法在这个领域没有得到广泛的应用.
研究的目的:
- 通过贝叶斯学方法验证BLUE-Q的健康专业教育.
- 为卫生专业教育研究人员提供贝叶斯式问卷验证教程.
主要方法:
- 招募了10名混合学习专家进行面试者管理的调查.
- 专家们根据可用性领域的5分利克特级别对BLUE-Q项目进行了评分.
- 用描述性统计数据分析了评级,并将其转换为贝叶斯先前分布.
主要成果:
- 由于专家认可概率较低,删除了31个定量项目.
- 定性评论为清晰度和逻辑流程提供了信息修订.
- 最终的BLUE-Q版本包括23个利克尔特尺度和6个开放项.
结论:
- 贝叶斯式问卷验证提供了一个简单,高效和严格的方法,用于内容和项目域相关性.
- 这种方法可以克服在研究中验证问卷的常见障碍.
- 经过验证的BLUE-Q适用于评估健康专业教育中的混合学习可用性.
更多相关视频
13:44Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
Published on: December 9, 2022
3.5K
06:02Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios
Published on: October 6, 2020
2.2K
相关概念视频
Detection of Gross Error: The Q Test
5.6K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
5.6K
Cochran's Q Test
230
Cochran's Q Test is a nonparametric statistical test used to determine if there are potential differences in the outcomes of three or more related groups on a binary (yes/no) or dichotomous outcome. It is essentially an extension of the McNemar Test, which is limited to two related samples - Cochran's Q test can handle three or more related samples, making it more versatile in scenarios where subjects are measured under multiple conditions. The test statistic follows a Chi-Square...
230
Reliability and Validity
12.7K
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.
12.7K
Confirmation Biases
5.5K
The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
5.5K
Data Validation
146
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
Key parameters for method validation include:
146
Response Surface Methodology
92
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:
The process of RSM involves several key steps:
92
