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相关概念视频

Social Exchange Theory02:06

Social Exchange Theory

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We have discussed why we form relationships, what attracts us to others, and different types of love. But what determines whether we are satisfied with and stay in a relationship? One theory that provides an explanation is social exchange theory. According to social exchange theory, we act as naïve economists in keeping a tally of the ratio of costs and benefits of forming and maintaining a relationship with others (Rusbult & Van Lange, 2003).
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Relationship Formation02:12

Relationship Formation

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What do you think is the single most influential factor in determining with whom you become friends and whom you form romantic relationships? You might be surprised to learn that the answer is simple: the people with whom you have the most contact. This most important factor is proximity. You are more likely to be friends with people you have regular contact with. For example, there are decades of research that shows that you are more likely to become friends with people who live in your dorm,...
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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...
220
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...
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Social Loafing01:37

Social Loafing

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Another way in which a group presence can affect performance is social loafing—the exertion of less effort by a person working together with a group. Social loafing occurs when our individual performance cannot be evaluated separately from the group. Thus, group performance declines on easy tasks (Karau & Williams, 1993). Essentially individual group members loaf and let other group members pick up the slack. Because each individual’s efforts cannot be evaluated,...
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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...
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Updated: Jun 5, 2025

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
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一个基于对非正式电子学习环境的信任产生建议的框架.

Amjad Rehman1, Adeel Ahmed2, Tahani Jaser Alahmadi3

  • 1Artificial Intelligence & Data Analytics Lab (AIDA) CCIS Prince Sultan University, Riyadh, Saudi Arabia.

PeerJ. Computer science
|December 9, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种信任意识的深度神经推 (TDNR) 框架,以解决电子学习中的学习者冷启动问题. 通过结合信任和关系数据,TDNR提高了推准确性,优于现有的方法.

关键词:
这就是HITS算法.神经网络的神经网络的神经网络推系统是一个推系统.堆溢出 堆溢出 堆溢出 堆溢出在信任信任信任信任信任信任信任

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科学领域:

  • 计算机科学 计算机科学
  • 信息科学 信息科学 信息科学
  • 教育技术的教育技术

背景情况:

  • 在线学习环境面临信息过载和数据共享的挑战.
  • 学习者冷启动问题阻碍了电子学习社区的新用户有效地回答问题.
  • 现有的推系统在非正式学习环境中难以获得稀疏性和准确性.

研究的目的:

  • 提出一个新的信任意识深度神经推 (TDNR) 框架,以解决学习者冷启动问题.
  • 通过建模复杂的非线性关系和结合信任度来提高推准确性.
  • 通过更好的专家-问者匹配,促进非正式电子学习社区的形成.

主要方法:

  • 开发了一个基于信任的深度神经推 (TDNR) 框架.
  • 使用隐藏的迪里克莱特分配 (LDA) 进行标签建模和使用枢纽和权威分数的专家排名.
  • 在推模型中纳入了问者-回答者关系图和信任度.
  • 在使用MAP,RMSE和F-measure指标的堆溢出数据集上评估模型.

主要成果:

  • 与基于评级和基于社会信任的方法相比,TDNR框架显著提高了建议的准确性.
  • TDNR在平均绝对误差 (MAE),根平均平方误差 (RMSE) 和F测量指标方面表现出卓越的表现.
  • 该模型有效地解决了信息过载和用户稀疏性问题,在冷启动场景中提供个性化的建议.

结论:

  • 拟议的TDNR框架为电子学习中的学习者冷启动问题提供了强大而可靠的解决方案.
  • 通过促进支持性的学习社区和准确的信息检索,TDNR增强了用户体验.
  • 混合方法有效地模拟复杂的关系,导致更相关和个性化的建议.