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相关概念视频

Cognitive Learning01:21

Cognitive Learning

981
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...
981
Introduction to Learning01:18

Introduction to Learning

908
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...
908
Observational Learning01:12

Observational Learning

802
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
802
Associative Learning01:27

Associative Learning

1.2K
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...
1.2K
Classification of Systems-I01:26

Classification of Systems-I

543
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
543
Aggregates Classification01:29

Aggregates Classification

956
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...
956

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相关实验视频

在物联网增强的电子学习中使用联合学习和图形卷积网络的自适应课程建议.

Huizhong Pu1, Yan Hua2

  • 1School of Accountancy, Wuxi City College of Vocational Technology, Wuxi, 214153, Jiangsu, China. snoopy_phz@outlook.com.

Scientific reports
|November 26, 2025
PubMed
概括

本研究介绍了一种保护隐私的联合学习 (FL) 系统,使用图形卷积网络 (GCN) 进行个性化的电子学习建议. 它通过整合实时物联网数据和DistilBERT功能来增强课程建议.

关键词:
课程推 课程推蒸贝尔特 蒸贝尔特 是一个蒸.电子学习 (e-learning) 是一个电子学习系统.联合学习 (FL)图形卷积网络 (GCN) 的图形.这就是为什么物联网是物联网物联网.MOOCs 是一个免费的MOOC.个性化学习个性化学习保护隐私 - 保护隐私可扩展性 可扩展性

相关实验视频

科学领域:

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

背景情况:

  • 电子学习平台和大规模开放在线课程 (MOOC) 的普及需要先进的推系统.
  • 现有的系统经常在用户隐私方面扎,并适应物联网 (IoT) 环境中的动态学习者交互.

研究的目的:

  • 为物联网集成的电子学习开发一个隐私意识的推架构.
  • 通过捕捉复杂的用户-课程交互和内容语义来提高课程建议的准确性和相关性.

主要方法:

  • 利用联合学习 (FL) 进行保护隐私的教育数据分布式培训.
  • 使用图形卷积网络 (GCN) 来建模复杂的用户课程关系和高阶依赖关系.
  • 集成的DistilBERT用于从课程描述和实时物联网数据中提取语义特征,以实现动态上下文意识.

主要成果:

  • 拟议的FL-GCN方法在建议准确性和个性化方面明显优于基线方法.
  • 展示了物联网数据的有效集成,以提供上下文意识和自适应的课程建议.
  • 在捕捉用户与课程交互中的复杂关系依赖性方面取得了卓越的性能.

结论:

  • 开发的架构为现代电子学习推系统提供了可扩展和保护隐私的解决方案.
  • 这种方法可以在物联网集成的教育环境中增强用户参与度和个性化.
  • 在全球范围内促进适应性,安全和高效的学习体验的进步.