在测试中练习,在测试中评估:基于测试的神经网络方法用于知识追踪
Shuanghong Shen1, Qi Liu2, Zhenya Huang2
1Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, Anhui, China.
概括
本研究介绍了一种基于测试的知识跟踪 (QKT) 模型,以改善在线学习. QKT有效地使用测试结构,以更好地了解学生的知识状态,增强教育经验.
科学领域:
- 教育技术的教育技术.
- 教育中的人工智能
- 机器学习用于学习分析.
背景情况:
- 在线学习系统旨在在全球范围内提供可访问,高质量的教育.
- 了解学习者的不断变化的知识状态对于有效的在线教学至关重要,这一任务被称为知识跟踪 (KT).
- 当前的KT方法往往忽视了测试中的和测试中的学习互动的结构性,假定分布均.
研究的目的:
- 开发一种新的模型,将知识追踪中固有的测试结构纳入知识追踪中.
- 提高在线学习环境中评估学习者的知识状态的准确性和效率.
- 为设计更有效的测试提供洞察力,使用更少的练习.
主要方法:
- 提出了一个新的基于测试的知识跟踪 (QKT) 模型.
- 设计了两个不同的神经网络模块:一个用于内测模拟,另一个用于内测融合.
- 将学习互动的测试结构集成到知识追踪过程中.
主要成果:
- 在公共现实世界数据集上,QKT模型实现了新的最先进的性能.
- 证明了结合测试结构可以提高学习者对知识状态的理解.
- 展示了使用更少的测试来实现准确评估的能力.
结论:
- 学习互动的测试结构是准确追踪知识的重要因素.
- QKT模型提供了一种更有效的方法来理解在线环境中的学生学习进度.
- 结果表明,设计更高效和有效的在线测验和学习模块具有实际意义.
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