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相关概念视频

Levels of Use of a GIS01:29

Levels of Use of a GIS

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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
109
Ordinal Level of Measurement00:55

Ordinal Level of Measurement

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The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
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Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
551
Purposive Learning01:22

Purposive Learning

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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
208
Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
596
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

105
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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相关实验视频

Updated: Sep 17, 2025

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools
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基于大数据的学习组织能力的概念化和规模开发.

Nesrin Alkan1, Deniz Ersan Yilmaz1, Bilal Baris Alkan2

  • 1Faculty of Economics and Administrative Sciences, Akdeniz University, Antalya, Türkiye.

Frontiers in big data
|July 4, 2025
PubMed
概括

本研究介绍了基于大数据的学习组织能力 (BD-LOC) 规模,这是衡量组织如何从大数据中学习的新工具. 经过验证的规模提供了一种可靠的方式来评估和提高学习能力,以获得战略优势.

科学领域:

  • 组织学习 组织学习
  • 大数据分析大数据分析
  • 管理科学 管理科学

背景情况:

  • 组织需要加强学习和适应能力,以获得竞争优势.
  • 缺乏验证的工具来衡量大数据驱动的学习能力.
  • 大数据显著影响组织的学习过程.

研究的目的:

  • 开发和验证可靠的尺度来评估基于大数据的学习组织能力.
  • 为大数据驱动的学习提供量化衡量.
  • 在大数据的背景下解决有关学习组织评估的文献上的差距.

主要方法:

  • 采用了两阶段的研究设计.
  • 在232名经理的探索性因素分析 (EFA) 中,在三个因素中确定了22个项目.
  • 对128名经理进行的确认因素分析 (CFA) 验证了尺度的结构和心理特征.

主要成果:

  • EFA揭示了该规模的明确的三因素结构.
  • CFA证实该模型与数据相匹配,并显示出良好的心理测量特性.
  • 最终的基于大数据的学习组织能力 (BD-LOC) 尺度表现出高的内部一致性和构造有效性.
关键词:
大数据就是大数据.数字化转型数字化转型学习组织学习组织.组织能力 组织能力规模发展发展规模发展.

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结论:

  • 在大数据时代,BD-LOC尺度是评估组织学习能力的有效和可靠工具.
  • 该工具帮助组织制定战略决策,创新和提高运营效率.
  • 该研究有助于有效实施数字化转型战略.