TLCD: A Deep Transfer Learning Framework for Cross-Disciplinary Cognitive Diagnosis

TL;DR

This paper introduces TLCD, a novel deep transfer learning framework that improves cross-disciplinary cognitive diagnosis by leveraging shared features across disciplines, enhancing accuracy in student ability assessment.

Contribution

The paper proposes an innovative neural network-based transfer learning method tailored for cross-disciplinary cognitive diagnosis, addressing data scarcity and feature complexity issues.

Findings

TLCD outperforms basic models in accuracy

Enhanced evaluation of students' learning states

Effective transfer of knowledge across disciplines

Abstract

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.