A Dual-Fusion Cognitive Diagnosis Framework for Open Student Learning Environments

Cognitive diagnosis model (CDM) is a fundamental component in intelligent education systems which aims to infer students' mastery levels based on historical response logs. However, existing CDMs usually follow the ID-based embedding paradigm, which could often diminish the effectiveness of CDMs in open student learning environments. This is mainly because most of them cannot directly infer new students' ability or utilize new exercises or knowledge concepts without retraining. Textual semantic information, due to its unified feature space and easy accessibility, can help alleviate this issue. However, directly incorporating textual semantic information may not benefit traditional CDMs due to the following challenges: the diversity and complexity of the original text corpus, lack of response-relevant features, and difficulty in integrating multi-source features. To this end, this paper proposes a Dual-Fusion Cognitive Diagnosis Framework (DFCD) to address the above challenges in open student learning environments. Specifically, to standardize the original text corpus and make it easier for CDMs to capture relevant textual semantic information, this paper first proposes the exercise-refiner and concept-refiner to make the exercises and knowledge concepts more coherent and reasonable in educational scenario via large language models. Then, DFCD encodes the refined features using text embedding models to obtain the textual semantic features. To construct response-relevant features, we propose a unified response-relevant feature construction to fully incorporate the information within the response logs. Finally, DFCD designs a dual-fusion module to merge the features from two sources, namely textual semantic features and response-relevant features. The ultimate representations possess the capability of inference in open student learning environments and can be also plugged in existing CDMs. Extensive experiments across three real-world datasets show that DFCD achieves superior performance and strong adaptability by improving the performance in three different scenarios of open student learning environments around 5% on average.