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相关概念视频

Cognitive Learning01:21

Cognitive Learning

517
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...
517
Introduction to Learning01:18

Introduction to Learning

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
530
Associative Learning01:27

Associative Learning

572
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...
572
Bar Graph01:07

Bar Graph

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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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Aggregates Classification01:29

Aggregates Classification

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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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Ogive Graph01:07

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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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相关实验视频

Updated: Sep 11, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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知识图的卷积网络与用户对课程推的偏好.

Zhong Hua1, Jianbai Yang2, Weidong Ji1

  • 1College of Computer Science and Information Engineering, Harbin Normal University, Harbin, 150025, China.

Scientific reports
|August 18, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了KGCN-UP,这是一个针对大规模开放在线课程 (MOOC) 的新型推模型. 通过利用知识图 (KG) 和用户偏好,KGCN-UP增强了课程建议,显著提高了准确性.

关键词:
图形卷积网络.图形卷积网络.知识图表知识图表个性化推个人推.

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 教育技术的教育技术

背景情况:

  • 大规模开放在线课程 (MOOCs) 正在迅速扩大,在个性化课程发现方面创造了挑战.
  • 知识图 (KG) 提供丰富的语义信息,以改善推系统,特别是解决MOOC中的数据稀疏性.
  • 现有的KG增强推算法强调了利用侧面信息以提高准确性的重要性.

研究的目的:

  • 引入KGCN-UP (Knowledge Graph Convolutional Networks with User Preferences),这是一个用于预测用户与课程交互概率的新型模型.
  • 通过利用KG内部的关系链和语义关系来增强用户表示和项目表示.
  • 解决数据稀缺问题,提高MOOC建议的质量.

主要方法:

  • 开发了KGCN-UP的两个关键模块:用户偏好传播和项目邻居增强.
  • 用户偏好模块通过关系链和KG中的动态注意力来改进用户偏好.
  • 项目邻居模块通过聚合语义关系和基于关系类型的注意力权重来增强项目表示.

主要成果:

  • KGCN-UP有效地利用了来自KG的高阶结构和语义信息.
  • 该模型在现实数据集上显著优于现有的最先进的推模型.
  • 经验结果表明,推准确度得到了显著改善.

结论:

  • 在MOOC环境中,KGCN-UP提供了一种强有力的方法来个性化推课程.
  • 该模型能够改进用户偏好并增强项目表示的能力,解决了推系统的关键挑战.
  • 研究结果表明,KGCN-UP可以显著提高用户对在线学习平台的满意度和参与度.