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Associative Learning01:27

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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.
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Metacognition is a conscious process where individuals are aware of their cognitive and executive processes, such as planning before solving a problem or self-monitoring during reading. For instance, a writer may need help with composing a piece. The situation involves a writer who is working on a piece of writing, but while doing so, they realize that something is missing. They notice that their characters lack depth or details. This realization occurs because the writer is reflecting on their...
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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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整合和检索来自异质平台的学习分析数据,使用本体学对齐:基于图形的方法.

Mohd Hafizan Musa1,2, Sazilah Salam1,3, Siti Feirusz Ahmad Fesol4

  • 1Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, 76100 Melaka, Malaysia.

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概括
此摘要是机器生成的。

这项研究引入了一种用于管理异质教育数据的新本体学,改进了洞察力和决策. 学生绩效和课程本体学增强了教育机构的资源管理和绩效评估.

关键词:
图形数据库中的图形数据库.基于图形的数据检索模型,用于通过本体设计从异质平台集成学习分析.异质的学习平台 异质的学习平台知识图是知识图.学习分析学习分析.大学本体论的存在学.

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科学领域:

  • 计算机科学 计算机科学
  • 教育技术的教育技术.
  • 数据科学数据科学数据科学

背景情况:

  • 教育机构面临的挑战是异构的数据集成和检索.
  • 现有的实体学需要即兴化,以解决新的教育数据主题.
  • 高等教育中需要先进的数据驱动决策.

研究的目的:

  • 利用本体图数据库,探索跨平台整合和检索异质数据.
  • 增强教育洞察力,并使数据驱动的决策.
  • 为管理学生和课程绩效数据提出一个创新的本体学.

主要方法:

  • 开发了一种创新的本体论,即学生表现和课程本体论.
  • 改进了现有的实体,并根据初步采访引入了新的实体.
  • 利用本体图形数据库进行数据集成和检索.
  • 包含了基于绩效评估的数据检索评估矩阵.

主要成果:

  • 成功解决了数据积累和异质性的问题.
  • 开发了一个适合处理大型数据集的可扩展模型.
  • 启用了对课程,学生和MOOC的增强资源管理和评估.
  • 确认了基于绩效评估的数据检索过程.

结论:

  • 拟议的学生表现和课程本体学有效地整合了异构的教育数据.
  • 本体图形数据库增强了教育洞察力和数据驱动的决策.
  • 该模型为管理和评估教育数据提供了一个可扩展的解决方案.