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相关概念视频

Observational Learning01:12

Observational Learning

314
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...
314
Associative Learning01:27

Associative Learning

579
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...
579
Purposive Learning01:22

Purposive Learning

207
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...
207
Cognitive Learning01:21

Cognitive Learning

526
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...
526
Introduction to Learning01:18

Introduction to Learning

533
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...
533
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

802
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
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相关实验视频

Updated: Sep 13, 2025

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task
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一个双编码器对比学习模型用于知识追踪.

Yanhong Bai1, Xingjiao Wu2, Tingjiang Wei1

  • 1Laboratory of AI for Education, East China Normal University, Shanghai 200062, China.

Entropy (Basel, Switzerland)
|July 29, 2025
PubMed
概括
此摘要是机器生成的。

双编码器对比知识跟踪 (DECKT) 通过使用一种新的对比学习框架,增强了个性化教育的知识状态表示. 这种方法有效地解决了数据稀疏性,改善了低频知识概念的预测.

关键词:
相反的学习学习学习.数据挖掘是数据挖掘的一个方法.深度学习是一种深度学习.图表神经网络的神经网络知识追踪知识追踪

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相关实验视频

Last Updated: Sep 13, 2025

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科学领域:

  • 人工智能的人工智能
  • 教育技术的教育技术
  • 机器学习 机器学习

背景情况:

  • 知识追踪 (KT) 模型对于个性化教育至关重要,但与数据稀疏性作斗争,导致较少频繁的概念的表现不佳.
  • 由于数据稀缺,现有的KT方法往往无法准确地建模学生不断演变的知识状态.
  • 这种不足影响了个性化的学习体验和绩效预测的质量.

研究的目的:

  • 引入双编码器对比知识跟踪 (DECKT),这是一个旨在改善知识状态表示的新框架.
  • 为了应对数据稀疏性的挑战,并加强教育环境中低频知识概念的建模.
  • 提高知识追踪模型的整体准确性和稳定性.

主要方法:

  • DECKT使用了一个对比的学习框架,带有最新的双编码器架构.
  • 主编码器处理当前数据,而动量编码器通过指数移动平均更新提供稳定的历史表示.
  • 将图形结构的约束损失和对抗训练纳入其中,以保持语义一致性并增强模型的稳定性.

主要成果:

  • 在不影响知识结构的情况下,DECKT显著提高了表示质量,特别是对于低频知识概念.
  • 该模型在模拟学生的实际知识状态方面展示了增强的能力,即使在稀疏的数据条件下.
  • 实验表明,DECKT在基准数据集上优于现有的最先进的知识追踪方法.

结论:

  • 通过其创新的对比式学习方法,DECKT有效地减轻了在稀少的教育数据中代表性的挑战.
  • 拟议的框架为知识追踪提供了一种更强大,更准确的方法,有利于个性化教育系统.
  • DECKT处理稀疏数据和增强低频概念表示的能力标志着该领域的重大进步.