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
概括
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.
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.
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