Jiatong Li

Personalized learner modeling using cognitive diagnosis (CD), which aims to model learners’ cognitive states by diagnosing learner traits from behavioral data, is a fundamental yet significant task in many web learning services. Existing cognitive diagnosis models (CDMs) follow theproficiency-response paradigm that views learner traits and question parameters as trainable embeddings and learns them through learner performance prediction. However, we notice that this paradigm leads to the inevitable non-identifiability and explainability overfitting problem, which is harmful to the quantification of learners’ cognitive states and the quality of web learning services. To address these problems, we propose an identifiable cognitive diagnosis framework (ID-CDF) based on a novelresponse-proficiency-response paradigm inspired by encoder-decoder models. Specifically, we first devise the diagnostic module of ID-CDF, which leverages inductive learning to eliminate randomness in optimization to guarantee identifiability and captures the monotonicity between overall response data distribution and cognitive states to prevent explainability overfitting. Next, we propose a flexible predictive module for ID-CDF to ensure diagnosis preciseness. We further present an implementation of ID-CDF, i.e., ID-CDM, to illustrate its usability. Extensive experiments on four real-world datasets with different characteristics demonstrate that ID-CDF can effectively address the problems without loss of diagnosis preciseness. Our code is available at https://github.com/CSLiJT/ID-CDF.

Cognitive diagnostic assessment is a fundamental task in intelligent education, which aims at quantifying students’ cognitive level on knowledge attributes. Since there exists learning dependency among knowledge attributes, it is crucial for cognitive diagnosis models (CDMs) to incorporate attribute hierarchy when assessing students. The attribute hierarchy is only explored by a few CDMs such as Attribute Hierarchy Method, and there are still two significant limitations in these methods. First, the time complexity would be unbearable when the number of attributes is large. Second, the assumption used to model the attribute hierarchy is too strong so that it may lose some information of the hierarchy and is not flexible enough to fit all situations. To address these limitations, we propose a novel Bayesian network-based Hierarchical Cognitive Diagnosis Framework (HierCDF), which enables many traditional diagnostic models to flexibly integrate the attribute hierarchy for better diagnosis. Specifically, we first use an efficient Bayesian network to model the influence of attribute hierarchy on students’ cognitive states. Then we design a CDM adaptor to bridge the gap between students’ cognitive states and the input features of existing diagnostic models. Finally, we analyze the generality and complexity of HierCDF to show its effectiveness in modeling hierarchy information. The performance of HierCDF is experimentally proved on real-world large-scale datasets.