Jove
Visualize
联系我们

相关概念视频

Cognitive Learning01:21

Cognitive Learning

1.0K
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...
1.0K
Purposive Learning01:22

Purposive Learning

447
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...
447
Introduction to Learning01:18

Introduction to Learning

961
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...
961
Nursing Process for Patient and Caregiver Teaching I: Assessment and Diagnosis01:24

Nursing Process for Patient and Caregiver Teaching I: Assessment and Diagnosis

2.3K
The nursing process provides a clinical decision-making framework for patients and families to establish and implement a personalized care plan. Since part of the nurse's duties is to teach patients, the steps of the nursing process are the most effective way to approach instruction. The nursing process and the teaching-learning process are inextricably linked.
It is critical to determine the patient's learning needs during the assessment. Determination of learning needs compounds data...
2.3K
Associative Learning01:27

Associative Learning

1.3K
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...
1.3K
Observational Learning01:12

Observational Learning

841
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...
841

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Light-PTNet: A lightweight parallel temporal network for smartphone-based human motion classification.

PloS one·2025
Same author

Interactive Learning System for Learning Calculus.

F1000Research·2024
Same author

The Development of a Data Collection and Browser Fingerprinting System.

Sensors (Basel, Switzerland)·2023
Same author

MSTCN: A multiscale temporal convolutional network for user independent human activity recognition.

F1000Research·2023
Same author

Customer churn prediction for telecommunication industry: A Malaysian Case Study.

F1000Research·2022
Same author

Stacked deep analytic model for human activity recognition on a UCI HAR database.

F1000Research·2022
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: Jan 17, 2026

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

5.8K

个性化和协作式学习体验 (PCLE) 是人工智能驱动的学习管理系统 (LMS) 的框架.

Claireta Tang Weiling1, Lew Sook Ling2, Ooi Shih Yin2

  • 1Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, Melaka, 75450, Malaysia.

F1000Research
|September 15, 2025
PubMed
概括
此摘要是机器生成的。

这项研究通过开发机器学习模型来提高更好的课程建议来增强人工智能驱动的电子学习. K-Nearest Neighbours在结构化数据方面表现出色,而SVD和NCF在各种数据集中提供了一致的性能,改善了学生的参与度.

关键词:
协作过 合作过K-最近邻居模型 (KNN)学习管理系统学习管理系统神经协作过模型 (NCF)个性化学习 个性化学习单一值分解模型 (SVD) 是一种单一值分解模型.

更多相关视频

Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

2.9K

相关实验视频

Last Updated: Jan 17, 2026

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

5.8K
Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

2.9K

科学领域:

  • 教育技术的教育技术
  • 教育中的人工智能
  • 机器学习应用 机器学习应用

背景情况:

  • 在人工智能驱动的电子学习中,有效的学生参与和学术表现至关重要.
  • 学习管理系统 (LMS) 往往缺乏强大的协作课程推策略.
  • 个性化学习体验受到当前推系统的限制.

研究的目的:

  • 为课程推开发和评估协作过和机器学习模型.
  • 在AI驱动的电子学习环境中增强个性化的学习体验.
  • 通过先进的推策略来提高学生的参与度和学业成绩.

主要方法:

  • 应用机器学习模型:K-最近邻居 (KNN),单数值分解 (SVD) 和神经协作过 (NCF).
  • 利用了两个大型数据集:10万个Coursera评论和209000个Udemy课程详细信息/评论.
  • 使用平均绝对误差 (MAE),命中率 (HR) 和平均相互命中排名 (ARHR) 评估模型性能.

主要成果:

  • K-最近的邻居 (KNN) 在结构化Coursera数据集上表现出卓越的表现.
  • 单值分解 (SVD) 和神经协作过 (NCF) 在Coursera和Udemy数据集中表现出稳定的预测准确性.
  • 数据集特征显著影响了模型性能,KNN偏好结构化数据,SVD/NCF显示多功能性.

结论:

  • 协作过和机器学习模型显著提高了LMS的课程建议.
  • 该研究强调了在电子学习中提高学生参与度和学术表现的潜力.
  • 未来的研究将纳入学习风格,并评估适应性学习的更广泛的教育背景.