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相关概念视频

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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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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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
109
Heuristics01:21

Heuristics

111
Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
111
Decision Making01:20

Decision Making

140
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...
140
Aggregates Classification01:29

Aggregates Classification

344
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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相关实验视频

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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优化英语教学质量评估 (ETQE) 的决策树,使用人工蜂群 (ABC) 优化.

Yingying Cui1

  • 1Fundamental Education Department, Tourism College of Changchun University, Changchun 130000, Jilin, China.

Heliyon
|September 4, 2023
PubMed
概括

本研究介绍了一种基于人工智能的英语教学质量评估 (ETQE) 方法. 该方法使用优化的指标准确预测教学质量,改进了以前的方法.

科学领域:

  • 教育中的人工智能
  • 教育数据挖掘教育数据挖掘
  • 机器学习用于质量评估

背景情况:

  • 由于教育系统的不断变化,对自动英语教学质量评估 (ETQE) 的需求日益增加.
  • 现有的ETQE模型缺乏在各种条件下通用性和最佳性能.
  • 确定关键教学质量 (TQ) 指标对于有效的ETQE至关重要.

研究的目的:

  • 为准确和实用的ETQE提出一种基于人工智能的新方法.
  • 为指标选择和质量预测制定一个两阶段的方法.
  • 在不同的教育环境中验证方法的性能.

主要方法:

  • 利用人工蜂群 (ABC) 算法进行最佳指标子集选择 (24名候选人).
  • 采用分类和回归树 (CART) 模型,由ABC优化,用于质量预测.
  • 在面对面 (中学) 和在线 (大学) 教学环境中评估了该方法.

主要成果:

  • 在面对面和在线环境中实现了超过98.99%的预测准确性.
  • 与现有的ETQE方法相比,证明了至少1.11%的改进.
  • 由人工智能驱动的模型显示出高效率和通用性,适用于现实世界的应用.
关键词:
人工智能的人工智能是人工智能.语言教学语言教学超启发式算法 (Meta-heuristic algorithms) 是一种超启发式算法.教学质量教学的质量.

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结论:

  • 拟议的基于人工智能和元启发式算法的方法为ETQE提供了一个强大的解决方案.
  • 两阶段方法有效地识别了相关指标,并预测了教学质量.
  • 该方法的高准确性和通用性支持其在实际教育质量评估中的采用.