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相关概念视频

Observational Learning01:12

Observational Learning

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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...
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Binet's Contribution to Measures of Intelligence01:23

Binet's Contribution to Measures of Intelligence

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Alfred Binet, along with his student Théophile Simon, was tasked by the French Ministry of Education in 1904 to create a method for identifying students who struggled to learn through conventional classroom instruction. This initiative aimed to address overcrowding by placing such students in specialized schools. Binet and Simon developed an intelligence test comprising 30 tasks, ranging from simple commands, like touching one's nose or ear, to more complex tasks, such as drawing...
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相关实验视频

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一种基于TabNet和机器学习模型的集体学习方法,用于在教育测试中检测作弊.

Yang Zhen1, Xiaoyan Zhu1

  • 1Anhui Technical College of Industry and Economy, Hefei, China.

Educational and psychological measurement
|July 26, 2024
PubMed
概括

这项研究评估了机器学习模型,用于检测教育考试作弊. 深度学习模型TabNet的表现优于其他模型,而混合型TabNet-AdaBoost模型获得了最高的准确性.

科学领域:

  • 教育技术的教育技术
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 学术不诚实是教育领域的一个重大挑战.
  • 机器学习越来越多地用于检测作弊,但像TabNet这样的深度学习模型未得到充分探索.
  • 评估各种模型对于确定有效的作弊检测方法至关重要.

研究的目的:

  • 为了比较12个机器学习模型的有效性,包括TabNet,用于检测学术不诚实.
  • 研究深度神经网络在教育环境中的表式数据任务的潜力.
  • 开发和评估一个混合组合模型,以提高作弊检测.

主要方法:

  • 一个对12个基本模型的比较分析:天真贝叶斯,线性差异分析,高斯过程,支向量机,决策树,随机森林,极端梯度增强 (XGBoost),AdaBoost,后勤回归,k-最近邻居,多层感知子和TabNet.
  • 使用接收器操作特征曲线 (AUC) 下面的面积进行性能评估.
  • 通过结合表现最好的TabNet和AdaBoost模型,开发一个整体模型.

主要成果:

  • 与其他11个基本模型相比,TabNet表现出更高的性能 (AUC = 0.85).
  • 混合TabNet-AdaBoost组合模型实现了显著更高的AUC (0.92).
关键词:
在 TabNet TabNet 中使用.欺骗检测检测检测的检测深度神经网络是一个神经网络.组合学习组合学习机器学习是机器学习.

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  • 深度神经网络模型显示出对表格数据挑战的强烈适应能力,例如学术不诚实检测.

    • 结论:

    • "TabNet"是一个非常有效的模型,用于检测在教育测试中的作弊.
    • 集合方法,特别是TabNet-AdaBoost,为学术不诚实检测提供了更高的准确性.
    • 这项研究为应用深度学习用于教育诚信提供了新的见解.