用深度迁移学习提升跨学科认知诊断准确率
TLCD: A Deep Transfer Learning Framework for Cross-Disciplinary Cognitive Diagnosis
- 通过迁移主学科共性特征，增强目标学科诊断能力
- 实验表明模型在跨学科任务中优于基础模型
- 适合教育AI、智能评测系统研发人员参考
在智能教育与人工智能技术驱动下，在线教育迅速发展，认知诊断技术可利用学生学习数据与反馈信息，精准评估其知识层面的能力水平。然而，海量数据虽带来资源丰富性，也导致特征提取复杂和学科数据稀缺问题。不同学科间知识体系、认知结构与数据特征差异显著，传统方法难以应对。本文深入研究神经网络与知识关联神经网络的认知诊断，提出一种创新的跨学科认知诊断方法（TLCD）。该方法融合深度学习与迁移学习策略，通过利用主学科的共性特征，提升模型在目标学科的表现。实验结果表明，基于深度学习的跨学科认知诊断模型在任务中表现优于基础模型，能更准确评估学生学习情况。
原文摘要 · Abstract (English)
Driven by the dual principles of smart education and artificial intelligence technology, the online education model has rapidly emerged as an important component of the education industry. Cognitive diagnostic technology can utilize students' learning data and feedback information in educational evaluation to accurately assess their ability level at the knowledge level. However, while massive amounts of information provide abundant data resources, they also bring about complexity in feature extraction and scarcity of disciplinary data. In cross-disciplinary fields, traditional cognitive diagnostic methods still face many challenges. Given the differences in knowledge systems, cognitive structures, and data characteristics between different disciplines, this paper conducts in-depth research on neural network cognitive diagnosis and knowledge association neural network cognitive diagnosis, and proposes an innovative cross-disciplinary cognitive diagnosis method (TLCD). This method combines deep learning techniques and transfer learning strategies to enhance the performance of the model in the target discipline by utilizing the common features of the main discipline. The experimental results show that the cross-disciplinary cognitive diagnosis model based on deep learning performs better than the basic model in cross-disciplinary cognitive diagnosis tasks, and can more accurately evaluate students' learning situation.
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