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相关概念视频

Cognitive Learning01:21

Cognitive Learning

243
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...
243
Problem-Solving01:29

Problem-Solving

165
Effective problem-solving consists of two steps: 1. identifying the problem and 2. selecting the appropriate problem-solving strategy (i.e., a plan of action used to find a solution). Humans use four problem-solving strategies:
165

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相关实验视频

Updated: Jul 5, 2025

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
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日志数据的双重集群:来自计算机复杂问题解决评估的见解

Xin Xu1, Susu Zhang2, Jinxin Guo3

  • 1Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing 100875, China.

Journal of Intelligence
|January 22, 2024
PubMed
概括
此摘要是机器生成的。

基于计算机的评估日志数据的双聚类分析揭示了不同的学生行为模式. 这种方法识别了具有相似行动和表现的学生群体,为解决问题过程提供了更深入的见解.

关键词:
在 PISA 测试中,动作序列的行动序列.双聚类是指双聚类.日志文件数据数据日志文件数据处理数据 处理数据.时间数据 时间数据

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Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools
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相关实验视频

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科学领域:

  • 教育的测量和评估.
  • 在教育中的数据挖掘和机器学习.
  • 认知科学和解决问题的方法

背景情况:

  • 基于计算机的评估通过日志文件生成有价值的行为数据.
  • 了解学生的解决问题需要分析这些复杂的过程数据.
  • 传统的集群方法分析学生或特征,但不是同时进行的.

研究的目的:

  • 应用双聚类算法,同时对学生和评估特征进行分类.
  • 评估双聚类在识别过程数据中的同质子组的有效性.
  • 探索双聚类在评估中分析动作序列和时间数据的实用性.

主要方法:

  • 在2012年PISA计算机评估 (CBA) 的日志文件数据上使用双聚类算法.
  • 在"Ticket"任务中应用了双聚类,分析了动作序列和时间数据.
  • 与传统的单模集群方法进行比较.

主要成果:

  • 双重集群成功地确定了同质的双重集群,将具有相似行为模式的学生分组到特定特征上.
  • 发现特定特征子集对于有效的双识别至关重要.
  • 整合基于时间的功能显著改善了对学生行为和结果的理解.

结论:

  • 双聚类提供了一种强大的方法,可以从日志数据中发现细粒度的洞察力,了解学生解决问题的行为.
  • 这种方法通过同时分析学生和特征,提供了比单模式集群更细致的理解.
  • 时间数据的整合提高了教育评估中的行为分析的解释性和深度.